FIELD OF TECHNOLOGY

[0001] The present disclosure generally relates to systems and methods for adaptive scheduling of actuator control, including predictive utilization and scheduling.

BACKGROUND OF TECHNOLOGY

[0002] Typically, indoor control systems, such as, e.g., comfort systems, smart home systems, HVAC, etc., are pre-loaded with a default schedule. It is expected that a user, such as a homeowner, will manually edit the schedule to customize the comfort schedule to personalize it to their needs. This is a time consuming process, which does not adapt over time to a user’s needs as their usage patterns evolve over time. This problem is further compounded in a more complex zoned system where the usage schedule for up to 12 rooms is personalized individually.

SUMMARY OF DESCRIBED SUBJECT MATTER

[0003] In some aspects, the techniques described herein relate to a method including: receiving, by at least one processor, time-series motion data from at least one motion sensing device associated with an area; wherein the time-series motion data includes: at least one instance of motion detected in the area, and at least one time associated with the at least one instance; determining, by the at least one processor, an occupancy metric associated with the area in each time slot of a plurality of time slots based at least in part on the at least one instance of motion and the at least one time; wherein the plurality of time slots includes sub-divisions of each day of a week; generating, by the at least one processor, an occupancy schedule for the area based at least in part on: the occupancy metric associated with the area in each time slot and a history of occupancy metrics associated with the area in each time slot; wherein the occupancy schedule represents a prediction of a level of occupancy of the area during each subsequent time slot in subsequent weeks; determining, by the at least one processor, at least one smart building device associated with the area; and communicating, by the at least one processor, the occupancy schedule to the at least one smart building device; wherein the occupancy schedule is configured to cause the at least one smart building device to actuate at least one building actuator based at least in part on the prediction of the level of occupancy of the area during each subsequent time slot in subsequent weeks.

[0004] In some aspects, the techniques described herein relate to a method, further including utilizing, by the at least one processor, an occupancy prediction machine learning model to predict the occupancy schedule based at least in part on the occupancy metric associated with the area in each time slot, wherein the occupancy prediction machine learning model includes a trained prediction layer including parameters trained to generate the occupancy schedule based on: a regression layer including a plurality of learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

[0005] In some aspects, the techniques described herein relate to a method, further including: segmenting, by the at least one processor, the time-series motion data into a plurality of time windows; and assigning, by the at least one processor, each time window of the plurality of time windows to a particular time slot of the plurality of time slots.

[0006] In some aspects, the techniques described herein relate to a method, further including: determining, by the at least one processor, a quantity of motion in each time slot of the plurality of time slots; generating, by the at least one processor, a occupancy metric associated with the quantity of motion in each time slot; and determining, by the at least one processor, the occupancy metric in each time slot based at least in part on the occupancy metric in each time slot.

[0007] In some aspects, the techniques described herein relate to a method, wherein the quantity of motion includes at least one of: a frequency of motion in each time slot, or a duration of motion in each time slot.

[0008] In some aspects, the techniques described herein relate to a method, further including: accessing, by the at least one processor, a plurality of previous occupancy metrics associated with at least one previous week; aligning, by the at least one processor, a plurality of previous time slots of the at least one previous week with the plurality of time slots of the week; generating, by the at least one processor, the occupancy metric in each time slot based at least in part on the plurality of previous occupancy metrics associated with the at least one previous week and the occupancy metric of each time slot.

[0009] In some aspects, the techniques described herein relate to a method, further including: generating, by the at least one processor, the occupancy metric in each time slot based at least in part on a decay rate applied to an aggregation of each previous occupancy metric of each time slot and each occupancy metric of each time slot.

[0010] In some aspects, the techniques described herein relate to a method, further including: utilizing, by the at least one processor, the occupancy metric associated with the area in each time slot as a predictive occupancy status of the area; and generating, by the processor, the occupancy schedule for the area based at least in part on the predictive occupancy status.

[0011] In some aspects, the techniques described herein relate to a method, wherein the at least one motion sensing device includes at least one of: a security camera, an infrared motion detector, a door sensor, a window sensor, a smart light switch, a Wi-Fi router, a radio-frequency identification (RFID) reader, or a smart lock.

[0012] In some aspects, the techniques described herein relate to a method, further including: utilizing, by the at least one processor, an occupancy status prediction machine learning model to predict a predictive occupancy status associated with each time slot based at least in part on: a regression layer including a plurality of learned regression weight trained to correlate the occupancy metric to an occupancy status prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

[0013] In some aspects, the techniques described herein relate to a system including: at least one processor in communication with at least one non-transitory computer readable medium having software instructions stored thereon, wherein the at least one processor, upon execution of the software instructions, is configured to: receive time-series motion data from at least one motion sensing device associated with an area; wherein the time-series motion data includes: at least one instance of motion detected in the area, and at least one time associated with the at least one instance; determine an occupancy metric associated with the area in each time slot of a plurality of time slots based at least in part on the at least one instance of motion and the at least one time; wherein the plurality of time slots includes sub-divisions of each day of a week; generate an occupancy schedule for the area based at least in part on: the occupancy metric associated with the area in each time slot and a history of occupancy metrics associated with the area in each time slot; wherein the occupancy schedule represents a prediction of a level of occupancy of the area during each subsequent time slot in subsequent weeks; determine at least one smart building device associated with the area; and communicate the occupancy schedule to the at least one smart building device; wherein the occupancy schedule is configured to cause the at least one smart building device to actuate at least one building actuator based at least in part on the prediction of the level of occupancy of the area during each subsequent time slot in subsequent weeks.

[0014] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to utilize an occupancy prediction machine learning model to predict the occupancy schedule based at least in part on the occupancy metric associated with the area in each time slot, wherein the occupancy prediction machine learning model includes a trained prediction layer including parameters trained to generate the occupancy schedule based on: a regression layer including a plurality of learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

[0015] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to: segment the timeseries motion data into a plurality of time windows; and assign each time window of the plurality of time windows to a particular time slot of the plurality of time slots.

[0016] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to: determine a quantity of motion in each time slot of the plurality of time slots; generate an occupancy metric associated with the quantity of motion in each time slot; and determine the occupancy metric in each time slot based at least in part on the occupancy metric in each time slot.

[0017] In some aspects, the techniques described herein relate to a system, wherein the quantity of motion includes at least one of: a frequency of motion in each time slot, or a duration of motion in each time slot.

[0018] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to: access a plurality of previous occupancy metrics associated with at least one previous week; align a plurality of previous time slots of the at least one previous week with the plurality of time slots of the week; generate the occupancy metric in each time slot based at least in part on the plurality of previous occupancy metrics associated with the at least one previous week and the occupancy metric of each time slot. [0019] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to: generate the occupancy metric in each time slot based at least in part on a decay rate applied to an aggregation of each previous occupancy metric of each time slot and each occupancy metric of each time slot. [0020] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to: utilize the occupancy metric associated with the area in each time slot as a predictive occupancy status of the area; and generate the occupancy schedule for the area based at least in part on the predictive occupancy status.

[0021] In some aspects, the techniques described herein relate to a system, wherein the at least one motion sensing device includes at least one of: a security camera, an infrared motion detector, a door sensor, a window sensor, a smart light switch, a Wi-Fi router, a radio-frequency identification (RFID) reader, or a smart lock.

[0022] In some aspects, the techniques described herein relate to a system, wherein the at least one processor, upon execution of the software instructions, is further configured to: utilize an occupancy status prediction machine learning model to predict a predictive occupancy status associated with each time slot based at least in part on: a regression layer including a plurality of learned regression weight trained to correlate the occupancy metric to an occupancy status prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

[0023] In some aspects, the techniques described herein relate to a non-transitory computer readable medium including software instructions that, when executed, are configured to cause at least one processor to perform steps including: receiving time-series motion data from at least one motion sensing device associated with an area; wherein the time-series motion data includes: at least one instance of motion detected in the area, and at least one time associated with the at least one instance; determining an occupancy metric associated with the area in each time slot of a plurality of time slots based at least in part on the at least one instance of motion and the at least one time; wherein the plurality of time slots includes sub-divisions of each day of a week; generating an occupancy schedule for the area based at least in part on: the occupancy metric associated with the area in each time slot and a history of occupancy metrics associated with the area in each time slot; wherein the occupancy schedule represents a prediction of a level of occupancy of the area during each subsequent time slot in subsequent weeks; determining at least one smart building device associated with the area; and communicating the occupancy schedule to the at least one smart building device; wherein the occupancy schedule is configured to cause the at least one smart building device to actuate at least one building actuator based at least in part on the prediction of the level of occupancy of the area during each subsequent time slot in subsequent weeks.

[0024] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to utilizing an occupancy prediction machine learning model to predict the occupancy schedule based at least in part on the occupancy metric associated with the area in each time slot, wherein the occupancy prediction machine learning model includes a trained prediction layer including parameters trained to generate the occupancy schedule based on: a regression layer including a plurality of learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

[0025] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: segmenting the time-series motion data into a plurality of time windows; and assigning each time window of the plurality of time windows to a particular time slot of the plurality of time slots.

[0026] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: determining a quantity of motion in each time slot of the plurality of time slots; generating a occupancy metric associated with the quantity of motion in each time slot; and determining the occupancy metric in each time slot based at least in part on the occupancy metric in each time slot.

[0027] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, wherein the quantity of motion includes at least one of: a frequency of motion in each time slot, or a duration of motion in each time slot.

[0028] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: accessing a plurality of previous occupancy metrics associated with at least one previous week; aligning a plurality of previous time slots of the at least one previous week with the plurality of time slots of the week; generating the occupancy metric in each time slot based at least in part on the plurality of previous occupancy metrics associated with the at least one previous week and the occupancy metric of each time slot.

[0029] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: generating the occupancy metric in each time slot based at least in part on a decay rate applied to an aggregation of each previous occupancy metric of each time slot and each occupancy metric of each time slot.

[0030] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: utilizing the occupancy metric associated with the area in each time slot as a predictive occupancy status of the area; and generating the occupancy schedule for the area based at least in part on the predictive occupancy status.

[0031] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, wherein the at least one motion sensing device includes at least one of: a security camera, an infrared motion detector, a door sensor, a window sensor, a smart light switch, a WiFi router, a radio-frequency identification (RFID) reader, or a smart lock.

[0032] In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: utilizing an occupancy status prediction machine learning model to predict a predictive occupancy status associated with each time slot based at least in part on: a regression layer including a plurality of learned regression weight trained to correlate the occupancy metric to an occupancy status prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Various embodiments of the present disclosure can be further explained with reference to the attached drawings, wherein like buildings are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ one or more illustrative embodiments.

[0034] FIG. 1 is a block diagram of an exemplary computer-based system for adaptive and predictive actuator control based on use and/or occupancy detection in accordance with one or more embodiments of the present disclosure.

[0035] FIG. 2 illustrates a flowchart of an illustrative methodology in accordance with one or more embodiments of the present disclosure.

[0036] FIG. 3 depicts an example adaptive thermostat control based on occupancy events in accordance with one or more embodiments of the present disclosure.

[0037] FIG. 4 depicts an example adaptive thermostat control based on occupancy events in accordance with one or more embodiments of the present disclosure.

[0038] FIG. 5 depicts an example multi-zone adaptive thermostat control based on occupancy events in accordance with one or more embodiments of the present disclosure.

[0039] FIG. 6 depicts an example adaptive thermostat control based on occupancy events and a learned rate of temperature loss in accordance with one or more embodiments of the present disclosure.

[0040] FIG. 7 depicts an example adaptive thermostat control based on occupancy events and a learned rate of temperature loss in accordance with one or more embodiments of the present disclosure.

[0041] FIG. 8 depicts an example adaptive thermostat control based on occupancy events and a learned rate of temperature loss in accordance with one or more embodiments of the present disclosure.

[0042] FIG. 9 depicts an example adaptive thermostat control based on occupancy events and a learned rate of temperature loss in accordance with one or more embodiments of the present disclosure.

[0043] FIG. 10 depicts a block diagram of an exemplary computer-based system and platform for adaptive and predictive actuator control based on use and/or occupancy detection in accordance with one or more embodiments of the present disclosure. [0044] FIG. 11 depicts a block diagram of another exemplary computer-based system and platform for adaptive and predictive actuator control based on use and/or occupancy detection in accordance with one or more embodiments of the present disclosure.

[0045] FIG. 12 depicts illustrative schematics of an exemplary implementation of the cloud computing/architecture(s) in which embodiments of a system for adaptive and predictive actuator control based on use and/or occupancy detection may be specifically configured to operate in accordance with some embodiments of the present disclosure.

[0046] FIG. 13 depicts illustrative schematics of another exemplary implementation of the cloud computing/architecture(s) in which embodiments of a system for adaptive and predictive actuator control based on use and/or occupancy detection may be specifically configured to operate in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

[0047] Various detailed embodiments of the present disclosure, taken in conjunction with the accompanying FIGs., are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive. [0048] Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases “in one embodiment” and “in some embodiments” as used herein do not necessarily refer to the same embodiment(s), though it may. Furthermore, the phrases “in another embodiment” and “in some other embodiments” as used herein do not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments may be readily combined, without departing from the scope or spirit of the present disclosure.

[0049] In addition, the term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on."

[0050] As used herein, the terms “and” and “or” may be used interchangeably to refer to a set of items in both the conjunctive and disjunctive in order to encompass the full description of combinations and alternatives of the items. By way of example, a set of items may be listed with the disjunctive “or”, or with the conjunction “and.” In either case, the set is to be interpreted as meaning each of the items singularly as alternatives, as well as any combination of the listed items. [0051] FIGs. 1 through 13 illustrate systems and methods of actuator control using adaptive and predictive scheduling in an area, such as on a per room, per zone and/or per space basis. The area may be an internal area within a building and/or external -to a building. The area may be enclosed and/or open. The following embodiments provide technical solutions and technical improvements that overcome technical problems, drawbacks and/or deficiencies in the technical fields involving manual scheduled actuator settings, such as thermostat set points and reactive actuation of systems and devices. As explained in more detail, below, technical solutions and technical improvements herein include aspects of improved automation of scheduled actuation in actuators of device and/or system for proactive and adaptive scheduling based on use and occupancy predictions, resulting in improved efficiency the device and/or system.

[0052] The technical solution leverages activation data from sensors, such as motion and door contacts, and potentially the indoor air sensors, or other devices having motion sensors integrated therein, as an input to the comfort system to create an adaptive schedule. This provides the advantage of allowing a building system, such as a system of home and/or commercial space, to adapt usage with minimal user interaction, in addition to optimizing the schedule based on user habits (adapting, for instance to the homeowner changing their habits as they take up an evening class).

[0053] The technical solution fuses comfort and security devices in order to achieve a highly optimized schedule will enable homeowners to maximize any cost savings in energy spend, while giving a superior operation with the maximum comfort.

[0054] Based on such technical features, further technical benefits become available to users and operators of these systems and methods. Moreover, various practical applications of the disclosed technology are also described, which provide further practical benefits to users and operators that are also new and useful improvements in the art.

[0055] FIG. 1 is a block diagram of an exemplary computer-based system for adaptive and predictive actuator control based on use and/or occupancy detection in accordance with one or more embodiments of the present disclosure.

[0056] In some embodiments, a system of actuators 104, associated with an area, are configured to perform localized and/or zoned management of aspects of the system. An example can include comfort systems in a residential and/or commercial building, the comfort system including actuators 104 to modulate local operation of the comfort system in particular areas of the building. In such an example, the actuators 104 may include, e.g., devices for managing airflow from an HVAC system, a boiler and/or furnace and/or heating element of an HVAC system, dampers, ductwork, hydronic heating devices, forced air heating components, air and/or hot water valves, among other devices configured to manage the zoning or targeting of area-specific comfort control. Other examples can include smart home equipment, home automation systems, lighting and/or plumbing systems, etc.

[0057] In some embodiments, the term “area” may refer to a room, zone, region, grouping of rooms, outdoor space, floor of a building, or any other suitable division of the building/space associated with the system of actuators 104 or any combination thereof. Accordingly, in some embodiments, the actuators 104 may be each be assigned to a particular area(s) such that control of each actuator 104 may affect the conditions and/or operation of the particular area(s). In some embodiments, the area may refer to any division of space for a building. The term “building” may refer to any building, residential or commercial, enclosed or open, including, e.g., a house, a condominium, an apartment, a midrise building, a high rise building, a skyscraper, a strip mall, a mall, a mobile home, an amphitheater, a stadium, patio, gazebo, among other buildings having a space that can be divided into areas, or any combination thereof.

[0058] Throughout this disclosure, the terms “room”, “zone” and “area” may be used in relation to embodiments of systems and/or methods of the disclosure. These terms are illustrative, and the systems and/or methods may be employed for area-level, adaptive and predictive control of actuators according to any area type, e.g., room, grouping of rooms, outdoor space, floor of a building, or any other suitable division of the building/space associated with the system of actuators 104 or any combination thereof.

[0059] Accordingly, in some embodiments, a network of disparate sensors and actuators may be employed to identify utilization patterns and adaptively control the actuators based on the utilization patterns in each area for area-specific control of the actuators, and thus area-specific management of systems and/or devices. Accordingly, in some embodiments, a motion sensor 108 may be connected to a network 101 via an edge device 106. Similarly, an actuator 104 may be connected to the network 101 via an edge device 102. A predictive actuation system 110 may be connected to the network 101 to communicate with the edge device 102 and edge device 106 to determine utilization patterns and develop adaptive and predictive schedules for actuation of the actuator 104 based on the utilization patterns. Thus, the predictive actuation system 110 may leverage the disparate devices on the network 101 to develop efficient actuation schedules that schedule the control of each actuator 104 based on area (e.g., zone, room, space, etc.) based on the utilization patterns to more efficiently operate the actuator 104 in the associated area.

[0060] In some embodiments, the edge device 102 and/or the edge device 106 may include any suitable computational device. In some embodiments, a computational device may refer to any combination of hardware and/or software to perform one or more functions. For example, the edge devices 102 and/or 106 may include, e.g., a laptop computer, desktop computer, smart home device, Wi-Fi router, access point, border router, gateway, smartphone, wearable device, or any other suitable that is suitable for interfacing with the sensor 108 and/or actuator 104.

[0061] In some embodiments, the edge device 102 and/or the edge device 106 may include computational resources associated with the sensor 108 and/or actuator 104, such as, e.g., smart home or loT systems, embedded systems on chip (SoC) embedded with the sensor 108 and/or actuator 104, smart home border gateway and/or hub, or other suitable smart home/IoT hardware that is integrated via hardware and/or software with the sensor 108 and/or actuator 104.

[0062] In some embodiment, the edge device 102 and/or the edge device 106 may include third- party computational hardware that interfaces with the sensor 108 and/or actuator 104, e.g., via an API, a Bluetooth connection, a Wi-Fi connection, USB, by a cloud-based interaction (e.g., function and/or API calls to a cloud service associated with the sensor 108 and/or actuator 104) or by any other suitable interface. Thus, the edge device 102 and/or the edge device 106 may be programmed to interact with the sensor 108 and/or actuator 104 to trigger an operation and/or change of setting of the sensor 108 and/or actuator 104. For example, the edge device 102 and/or the edge device 106 may be a smartphone or third-party software service (e.g., a virtual assistant such as Siri™, Google Assistant ™, Amazon Alexa™, etc., or a smart home dashboard such as Apple HomeKit, Google Home, etc.), such that the edge device 102 and/or the edge device 106 integrates with the predictive actuation system 110, the sensor 108 and the actuator 104 via publicly exposed interfaces.

[0063] In some embodiments, the sensor 108 may include any device suitable for detecting and/or recognizing and presence of or motion of a user. For example, the sensor 108 may include, e.g., a security camera that uses computer vision technique to detect and/or recognize motion, an infrared motion detector to detect presence and/or movement, a door sensor to detect entry and/or exit relative to a particular space, a window sensor to detect entry and/or exit relative to a particular space, a smart light switch to detect use of lighting, a smart light to detect use of lighting, a Wi-Fi router to detect a presence and/or use of a computing device associated with a user, a radiofrequency identification (RFID) reader to detect a presence of an RFID tag associated with a user, a smart lock to detect entry and/or exit relative to a particular space, a vibration sensor to detect vibrations of movement in a room, a pressure sensor to detect pressure changes resulting from movement and/or the opening and closing of doors and/or windows, time-of-flight sensors such as ultrasound, light detection and ranging (LiDAR), radar, laser and/or infrared sensors, etc. to detect the location and change of location of objects, software-based settings changes and other softwarebased triggers and detectors, among other sensor hardware and/or software or any combination thereof.

[0064] In some embodiments, the actuator 104 may refer to any controllable device within a building/space, such as one or more areas of the building/space as detailed above. In some embodiments, the actuator 104 may be associated with a comfort system of a home or commercial building. Thus, the actuator 104 may include one or more devices configured to manage the operation of the comfort system in a particular area within the home or commercial building, such as, e.g., managed air flow devices (e.g., damper, micro damper, valve, mini-split, etc.), discrete heating and/or cooling components of the comfort system (e.g., hydronic underfloor heating components, electric underfloor heating elements, a radiator, a standalone air conditioning unit, a standalone electric heater, a radiation heating device, ventilation ducting/venting, etc.), and/or system level devices, such as, e.g., a system/combi boiler, hot water heater/boiler, heat pump, chiller, district heating, furnace, etc., include, e.g., a thermostat, a water heater, air conditioner, HVAC, managed air flow devices (e.g., damper, micro damper, valve, mini split, etc.), room level heating and/or cooling devices (e.g., hydronic underfloor, electric underfloor, electric heating, boiler, furnace, stand-alone AC, ventilation, etc.).

[0065] In some embodiments, while the actuator 104 herein is described as relating to a comfort system, such embodiments are illustrative. In some embodiments, the actuator 104 may be associated, alternatively or in addition, with other in-home devices for area-specific control, such as, e.g., a security camera, a dishwasher, an oven, a microwave, a television, a power window shade, lighting, a refrigerator, an ice machine, among other actuators or any combination thereof For example, the occupancy of a room may be used to turn electronic devices off, such as, e.g., home theater equipment, televisions, lighting, an oven, a stove, a range, among other devices that may be unnecessary and/or unsafe to remain active while a user is away. Other such appliances and devices may be controllable based on occupancy/presence of users, or any combination thereof.

[0066] In some embodiments, the term “actuator” and/or “building actuator” may be employed. Such terms refer to an actuator of building. For example, “home actuator” may refer to an actuator configured for use in a home, while a “commercial actuator” may be configured for use in a commercial building. Herein, the terms “actuator” and “building actuator” refer to an actuator of any building type, including, e.g., a house, a condominium, an apartment, a midrise building, a high rise building, a skyscraper, a strip mall, a mall, a mobile home, an amphitheater, a stadium, patio, gazebo, among other buildings having a space that can be divided into areas, or any combination thereof, as detailed above.

[0067] In some embodiments, the sensor 108 and the actuator 104 may be integrated into a common device, such as a thermostat and/or discrete heating and/or cooling components of the comfort system with integrated motion sensor or may be integrated into separate devices that may communicate via the network 101. Similarly, in some embodiments, the edge device 102 and the edge device 106 may integrated into or may be a same device or may be separate and independent devices that may communicate via the network 101.

[0068] In some embodiments, the network 101 may include any suitable computer network, including, two or more computers that are connected with one another for the purpose of communicating data electronically. In some embodiments, the network may include a suitable network type, such as, e.g., a public switched telephone network (PTSN), an integrated services digital network (ISDN), a private branch exchange (PBX), a wireless and/or cellular telephone network, a computer network including a local-area network (LAN), a wide-area network (WAN) or other suitable computer network, or any other suitable network or any combination thereof. In some embodiments, a LAN may connect computers and peripheral devices in a physical area by means of links (wires, Ethernet cables, fiber optics, wireless such as Wi-Fi, etc.) that transmit data. In some embodiments, a LAN may include two or more personal computers, printers, and high- capacity disk-storage devices, file servers, or other devices or any combination thereof. LAN operating system software, which interprets input and instructs networked devices, may enable communication between devices to: share the printers and storage equipment, simultaneously access centrally located processors, data, or programs (instruction sets), and other functionalities. Devices on a LAN may also access other LANs or connect to one or more WANs. In some embodiments, a WAN may connect computers and smaller networks to larger networks over greater geographic areas. A WAN may link the computers by means of cables, optical fibers, or satellites, cellular data networks, or other wide-area connection means. In some embodiments, an example of a WAN may include the Internet.

[0069] In some embodiments, the network 101 may include the Internet, a LAN, a Zigbee network, a Z-Wave network, a Matter™ network, an Apple HomeKit network, or any other suitable networking technology or ecosystem or any combination thereof. Accordingly, in some embodiments, the predictive actuation system 110 may provide functionality from a cloud platform, e.g., via software-as-a-service (SaaS), over the Internet, or as a locally hosted service on a smart home network such as Zigbee, Z-Wave, Matter and/or HomeKit, or via a distributed network on which one or more the edge device 102 and/or edge device 106 are nodes, or as a local software package on one or more the edge device 102 and/or edge device 106, or by any other suitable architecture or any combination or hybrid implementation thereof.

[0070] In some embodiments, the predictive actuation system 110 may include hardware components such as a processor 116, which may include local or remote processing components. In some embodiments, the processor 116 may include any type of data processing capacity, such as a hardware logic circuit, for example an application specific integrated circuit (ASIC) and a programmable logic, or such as a computing device, for example, a microcomputer or microcontroller that include a programmable microprocessor. In some embodiments, the processor 116 may include data-processing capacity provided by the microprocessor. In some embodiments, the microprocessor may include memory, processing, interface resources, controllers, and counters. In some embodiments, the microprocessor may also include one or more programs stored in memory.

[0071] Similarly, the predictive actuation system 110 may include datastore 118, such as one or more local and/or remote data storage solutions such as, e.g., local hard-drive, solid-state drive, flash drive, database or other local data storage solutions or any combination thereof, and/or remote data storage solutions such as a server, mainframe, database or cloud services, distributed database or other suitable data storage solutions or any combination thereof. In some embodiments, the storage 111 may include, e.g., a suitable non-transient computer readable medium such as, e.g., random access memory (RAM), read only memory (ROM), one or more buffers and/or caches, among other memory devices or any combination thereof.

[0072] In some embodiments, the predictive actuation system 110 may implement computer engines for occupancy learning based on motion events, and adaptive and/or predictive scheduling of the actuator 104 based on the learned occupancy for area-specific control of the comfort system. In some embodiments, the terms “computer engine” and “engine” identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).

[0073] Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multicore, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.

[0074] Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints. [0075] In some embodiments, to learn occupancy patterns for each area associated with the comfort system, and thus predict utilization patterns of the comfort system on an area-specific basis, the predictive actuation system 110 may include computer engines including, e.g., an occupancy learning engine 112. In some embodiments, the occupancy learning engine 112 may include dedicated and/or shared software components, hardware components, or a combination thereof. For example, the occupancy learning engine 112 may include a dedicated processor and storage. However, in some embodiments, the occupancy learning engine 112 may share hardware resources, including the processor 116 and datastore 118 of the predictive actuation system 110.

[0076] In some embodiments, to predict an adaptive and efficient actuation schedule for the actuator 104, the predictive actuation system 110 may include computer engines including, e.g., a scheduling engine 114. In some embodiments, the scheduling engine 114 may include dedicated and/or shared software components, hardware components, or a combination thereof. F or example, the scheduling engine 114 may include a dedicated processor and storage. However, in some embodiments, the scheduling engine 114 may share hardware resources, including the processor 116 and datastore 118 of the predictive actuation system 110.

[0077] In some embodiments, the occupancy learning engine 112 and the scheduling engine 114 are depicted as being implemented by a remote system across the network 101 from the edge device 102 and the edge device 106. However, the occupancy learning engine 112 and/or the scheduling engine 114 may be implemented locally on the edge device 102, on the edge device 106, or on a separate device in communication with the edge device 102 and/or the edge device 106, or any suitable combination thereof.

[0078] In some embodiments, the sensor 108 may be located in a particular area, such as, e.g., a particular building, room, or other suitable space in which the comfort system operates. The sensor 108 may identify motion events within the space in which the sensor is location 108 based on sensor readings. In some embodiments, the sensor 108 may be a part of a security system with motion and/or object detection sensors distributed throughout the building. Thus, each area having an actuator 104 may also be associated with one or more sensors of the security system. Accordingly, the area associated with the sensor 108 may be identified and presence may be detected during operation of the security system. The data from the sensor 108 that represents detect presence may be provided to the predictive actuation system 110. The actuator 104 in the area of the sensor 108 may also be identified, and the motion and/or presence of users in the area may be used for the determination of utilization patterns for the actuator 104. [0079] Additionally, and/or alternative embodiments of the sensor 108 may be employed. For example, the sensor 108 may detect entry and/or exit based on, e.g., a smart lock actuation, smart window latch, or other suitable sensor. In another example, the sensor 108 may detect and/or recognize the presence and/or movement of a person within the space based on computer vision processing by the sensor 108 and/or edge device 106 of an image or video feed of the space. Other forms of presence and/or motion detection may be employed, such as, e.g., access card usage, an infrared motion sensor, detection of the on/off state of lights, detect via Wi-Fi, Bluetooth, NFC and/or RFID of the presence and/or movement of a computing device associated with a user (e.g., a smartphone, smartwatch, laptop, tablet, etc.), or any other sensing technique or any combination thereof.

[0080] In some embodiments, each motion event in the area may be logged and uploaded to the predictive actuation system 110, e.g., via a suitable application programming interface (API) or other suitable interfacing and/or messaging technology. In some embodiments, “APF’ refers to a computing interface that defines interactions between multiple software intermediaries. An “application programming interface" or “API” defines the kinds of calls or requests that can be made, how to make the calls, the data formats that should be used, the conventions to follow, among other requirements and constraints. An “application programming interface" or “API” can be entirely custom, specific to a component, or designed based on an industry-standard to ensure interoperability to enable modular programming through information hiding, allowing users to use the interface independently of the implementation.

[0081] In some embodiments, the motion events may be used to detect area-specific occupancy level according to a quantity of motion, such as, e.g., duration of occupancy of the area, frequency of occupancy, duration of each detected movement, duration of each detect movement, or any other suitable methodology for quantifying occupancy. In some embodiments, for some actuators, usage patterns may be more effectively correlated to other types of activity than occupancy and/or movement, such as hot water usage, power usage, or other suitable measure of activity in the space. Thus, the occupancy level may be quantified according to the associated measure of activity for the actuator.

[0082] In some embodiments, the occupancy learning engine 112 may ingest the occupancy level and determine, for each time slot throughout a schedule period (e.g., a day, a week, a month, a year, etc.), an occupancy metric indicative of the degree of occupancy experienced in the area during that time slot in the schedule period. For example, the schedule period may be a recurring period and the occupancy metric may be determined over time for a particular time slot based on the usage patterns. Thus, the occupancy metric represents an expected level of usage of the space based on the detected motion events. In some embodiments, the occupancy metric may be generated based on, e.g., a number of occupancy levels, usage levels, rate of decay, fixed detection intervals, etc., each of which represent parameters which can be fine-tuned to most optimally represent usage patterns throughout the schedule period.

[0083] In some embodiments, occupancy metrics may be collected for each area based on the locations of each sensor 108 on the network 101. As a result, area-specific occupancy can be assessed in each area throughout the building/space in order to control each actuator 104 of each area based on the area-specific occupancy.

[0084] As an example, the learning process may start with either default or manually configured daily/weekly time schedule. Each day of the week is segmented into fixed intervals, for instance 15 min slots resulting in 672 periods per week. If an occupancy event has been detected in a slot, it is determined the slot has the highest level of usage and will go to the comfort temperature setpoint. During a slot where no occupancy events have been detected, the setpoint will be lowered to a marginally setback (lower temperature) setpoint. When no occupancy events have been detected in two consecutive slots, the room will be setback further to an 'empty room' setpoint (which would give a minimal temperature). The usage levels of the slots are used as an input to the comfort schedule. If the current usage is higher than the scheduled usage, the schedule is adapted to the higher level. If the scheduled usage is higher than the current usage level of the room, the scheduled usage level will 'decay'. After consecutive weeks with a lower usage level, the schedule will have decayed to the lower-level pattern.

[0085] In some embodiments, the occupancy level of a room (the number of occupancy events over a fixed interval) may be used to determine the usage level of a room as represented by the occupancy metric. The usage level then determines the comfort setpoint to use. As the occupancy level decays over time, the usage level is downgraded to bias towards more economical setpoints. These setpoints can be customized to allow a balance between setback and recovery of the room. [0086] In addition, the historical usage patterns of the room may be used to customize the future schedule to enable a living schedule that can be leveraged by existing adaptive functionality which preempts turning on/off the comfort system early as described above. To avoid drastic changes to the schedule week by week, the usage level decays over time, which avoids the schedule adapting too quickly to 'one-off empty room events while also preserving historical patterns.

[0087] Additionally, or alternatively, in some embodiments, the sensor activation data (when a sensor 108 is activated) can be counted, accumulated, analyzed with respect to time or otherwise analyzed to provide a more accurate view of occupancy level, and provide a more detailed picture of how occupied a space is, allowing for more accurate prediction of future use and helping set the correct comfort level.

[0088] Additionally, or alternatively, in some embodiments, the armed/set status of the whole security system, or separate areas (also known as partitions, areas or groups) which can be individually set, can be used as additional input to generate usage. In some embodiments, while part of the intrusion system is armed (set), a part of the system may not be occupied. Thus, the occupancy learning engine 112 may track the set start and set end and predict when users will be back in those areas. Accordingly, both the armed (set) status itself and the usual arm/disarm (set/unset) times can be used to improve the comfort schedule. As a result, the occupancy learning engine 112 may use the security system so that the system is ready for use when the user is there but is saving energy when the user is not there.

[0089] Additionally, or alternatively, in some embodiments, external triggers, such as API calls from third-party devices and services, can be used as additional input to generate usage. For example, a user may use a third-party smart home integration or virtual assistant to send commands and/or requests. The commands and/or requests may be used to assess usage and/or presence in order to establish usage patterns. Thus, the occupancy learning engine 112 may track the external trigger usage to improve the comfort schedule.

[0090] Additionally or alternatively, in some embodiments, multiple occupancy levels can be added (for example constantly occupied, mostly occupied, occasionally occupied, mostly empty, always empty, not used) can be added with multiple temperature set points to allow rooms not actually in use, but that have occasional use, such as periodic entry to access something, such as in a pantry, closet, bathroom, garage, or other space where the user may periodically enter and use. [0091] In some embodiments, the predictive actuation system 110 may utilize the occupancy learning engine 112 to develop a predictive occupancy metric that predicts the occupancy level of each area in the building/space in the future based on the past, historical and/or recent occupancy metrics. In some embodiments, the occupancy learning engine 112 may develop a statistical aggregation, such as a mean, median, standard deviation, weighted mean, weight average, percentage deviation from a mean or median, or other suitable aggregation of the occupancy metric for a particular time slot in the schedule period. In some embodiments, the aggregation may be augmented with a forgetting/ decay rate such that more recent occupancy levels are weighted higher in developing the aggregation than more distant occupancy levels.

[0092] In some embodiments, the occupancy learning engine 112 may use an occupancy prediction machine learning model to predict the predictive occupancy metric for each time slot based on the past and/or recent occupancy metrics. In some embodiments, the occupancy status machine learning model may be configured to utilize one or more exemplary Al/machine learning techniques chosen from, but not limited to, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary neutral network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary implementation of Neural Network may be executed as follows: a. define Neural Network architecture/model, b. transfer the input data to the exemplary neural network model, c. train the exemplary model incrementally, d. determine the accuracy for a specific number of timesteps, e. apply the exemplary trained model to process the newly received input data, f. optionally and in parallel, continue to train the exemplary trained model with a predetermined periodicity.

[0093] In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary trained neural network model may specify a neural network by at least a neural network topology, a series of activation functions, and connection weights. For example, the topology of a neural network may include a configuration of nodes of the neural network and connections between such nodes. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary trained neural network model may also be specified to include other parameters, including but not limited to, bias values/functions and/or aggregation functions. For example, an activation function of a node may be a step function, sine function, continuous or piecewise linear function, sigmoid function, hyperbolic tangent function, or other type of mathematical function that represents a threshold at which the node is activated. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary aggregation function may be a mathematical function that combines (e.g., sum, product, etc.) input signals to the node. In some embodiments and, optionally, in combination of any embodiment described above or below, an output of the exemplary aggregation function may be used as input to the exemplary activation function. In some embodiments and, optionally, in combination of any embodiment described above or below, the bias may be a constant value or function that may be used by the aggregation function and/or the activation function to make the node more or less likely to be activated.

[0094] Accordingly, the occupancy status machine learning model may include a layer to ingest the occupancy metric and output the predictive occupancy metric based on learned parameters of the layer. For example, for a regression model, the occupancy status machine learning model may include a regression layer of regression nodes (e.g., long short-term memory or gated recovery units or other type of node) having learned regression weights trained to correlate the occupancy metric to a future occupancy metric based on a history of occupancy metrics.

[0095] In some embodiments, based on the predictive occupancy metric, the predictive actuation system 110 may utilize a scheduling engine 114 to develop an occupancy schedule that schedules adjustments to set points of the actuator 104 in order to provide area-specific control of, e.g., the comfort system. Thus, the occupancy schedule for the area of the actuator 104 may facilitate improved efficiency of the comfort system by selectively control the actuator 104 to shut off operation within the area during times of a low probability of occupancy while allowing of an occupancy schedule of a separate area to control the actuator of the separate area to turn on operation of the comfort system in the area. As detailed above, turning on and/or shutting off operation of the comfort system in a particular area may include control of the actuator 104 to actuate, e.g., HVAC ducting and/or micro-ducting, one or more valves, hydronic components, electric heating elements, stand-alone air condition unit, ventilation components, among others or any combination thereof. As a result, the scheduling engine 114 may independently schedule set point adjustments to multiple separate areas associated with multiple separate actuators 104- based on detect utilization patterns specific to each area. 1 [0096] In some embodiments, the scheduling engine 114 may employ a “heat map” that maps the predictive occupancy metrics to each time slot in the schedule period such that occupancy level is mapped throughout the schedule period. The scheduling engine 114 may then employ the heat map to determine an actuation set point variation throughout the schedule period. For example, as the occupancy level rises across time slots, the actuation set point is adjusted upwards, and as occupancy level falls, the actuation set point may be adjusted downwards. In some embodiments, the adjustments may be directly or indirectly correlated to the degree of change in occupancy level. In some embodiments, the adjustments may be made relative to a user set point that establishes the user preference for while the user occupies the space. Thus, as occupancy level increases the scheduling engine 114 may adjust the actuator set point towards the user set point, and as occupancy level decreases, the scheduling engine 114 may adjust the actuator set point away from the user set point.

[0097] In some embodiments, the adjustments to the actuator set point may be according to a trained regression model correlating occupancy and energy use to actuator set point. Such a regression model may take into account drift of loss, e.g., at different times of the year or times of the day, that may cause the actuator set point to deviate from the actual condition and/or actuator behavior targeted by the actual set point. For example, the temperature of the space may fall faster during the winter, thus causing a deviation between a thermostat set point and the actual temperature. In taking the drift into account, the actuator 104 may be pulsed less frequently to balance efficiency with user comfort, as detailed further below.

[0098] In some embodiments, the occupancy learning engine 112 may use an occupancy prediction machine learning model to predict the occupancy schedule prediction for each time slot based on the past and/or recent occupancy metrics. In some embodiments, the occupancy prediction machine learning model may be configured to utilize one or more exemplary Al/machine learning techniques chosen from, but not limited to, decision trees, boosting, supportvector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary neutral network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary implementation of Neural Network may be executed as follows: a. define Neural Network architecture/model, b. transfer the input data to the exemplary neural network model, c. train the exemplary model incrementally, d. determine the accuracy for a specific number of timesteps, e. apply the exemplary trained model to process the newly received input data, f. optionally and in parallel, continue to train the exemplary trained model with a predetermined periodicity.

[0099] In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary trained neural network model may specify a neural network by at least a neural network topology, a series of activation functions, and connection weights. For example, the topology of a neural network may include a configuration of nodes of the neural network and connections between such nodes. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary trained neural network model may also be specified to include other parameters, including but not limited to, bias values/functions and/or aggregation functions. For example, an activation function of a node may be a step function, sine function, continuous or piecewise linear function, sigmoid function, hyperbolic tangent function, or other type of mathematical function that represents a threshold at which the node is activated. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary aggregation function may be a mathematical function that combines (e.g., sum, product, etc.) input signals to the node. In some embodiments and, optionally, in combination of any embodiment described above or below, an output of the exemplary aggregation function may be used as input to the exemplary activation function. In some embodiments and, optionally, in combination of any embodiment described above or below, the bias may be a constant value or function that may be used by the aggregation function and/or the activation function to make the node more or less likely to be activated.

[0100] Accordingly, the occupancy prediction machine learning model may include a layer to ingest the occupancy metric and output the occupancy schedule prediction based on learned parameters of the layer. For example, for a regression model, the occupancy prediction machine learning model may include a regression layer of regression nodes (e.g., long short-term memory or gated recovery units or other type of node) having learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on a history of occupancy metrics.

[0101] In some embodiments, the predictive actuation system 110 may use the scheduling engine 114 to schedule actuation of actuators 104 in the area, e.g., of the comfort system, such as a thermostat according to temperature set point adjustments, as well as for other set points and set point adjustments in one or more other systems and/or devices in the area, such as, e.g., air quality controls such as air purification/filtering and humidity controls (either increase or decrease), lighting levels, ventilation, modify response to emergency events such as smoke/fire detection, carbon monoxide, flood, etc. (for example giving wider alerts to all areas that people are likely to hear the alarm), audio playback (radio/music - follow the user) and other room level systems.

[0102] The system can also be extended where such inputs give a knowledge of the specific user - and can be extended to have multiple per-user schedules, which can be overlaid. In this way we can adjust the comfort system to the personal profile of a specific person and merged when multiple users are in the area.

[0103] In some embodiments, the predictive actuation system 110 may provide the occupancy schedule to the actuator 104 and/or the edge device 102. The actuator 104 and/or the edge device 102 may be configured to interpret the occupancy schedule to automatically adjust the actuator set point within the area independently of set points in other areas according to time slot in the schedule period. Thus, the actuator 104 may automatically and dynamically adjust its operation to account of usage patterns, thus predicting user set points while increasing efficiency when the user is not present in the area in order to provide area-specific control of, e.g., the comfort system. Thus, the occupancy schedule for the area of the actuator 104 may facilitate improved efficiency of the comfort system by selectively control the actuator 104 to shut off operation within the area during times of a low probability of occupancy while allowing of an occupancy schedule of a separate area to control the actuator of the separate area to turn on operation of the comfort system in the area.

[0104] As detailed above, turning on and/or shutting off operation of the comfort system in a particular area may include control of the actuator 104 to actuate, e.g., HVAC ducting and/or micro-ducting, one or more valves, hydronic components, electric heating elements, stand-alone air condition unit, ventilation components, among others or any combination thereof. [0105] FIG. 2 illustrates a flowchart of an illustrative methodology in accordance with one or more embodiments of the present disclosure.

[0106] In some embodiments, the occupancy learning engine 112 may receive time-series motion data at 202 from one or more sensors 108 associated with a particular area of, e.g., a home, such as a room, group of rooms, outdoor space of the home, or other demarcation of area. In some embodiments, the time-series motion data may include instances of motion detected by the sensor 108 and a time associated with each instance of motion. In some embodiments, the instances of motion may include, e.g., an indicator that motion was detected in the area, a type of motion detected (e.g., an activity performed by a user), a duration of the motion, a magnitude of the motion, among other motion-related data and characteristics based on the data available from the sensor 108 as detailed above.

[0107] For example, in some embodiments, the sensor 108 may include a camera device, such as a smart home security camera, motion sensor (e.g., a time-of-flight sensor using ultrasound, infrared, laser, radar, or other suitable ranging technologies or any combination thereof), or other sensor of a security system. The camera device may capture imagery of the motion and utilize one or more computer vision models to detect and recognize the motion, e.g., according to activity or movement types.

[0108] In some embodiments, the occupancy learning engine 112 may segment the time-series motion data into time windows at 204.

[0109] In some embodiments, the scheduling of the actuator 104 may be based around a schedule period having time slots defining each segment of occupancy within the area. Accordingly, occupancy metrics may be established for each time slot. In some embodiments, rather than a windowed approach, the schedule may be based on a continuous time-based occupancy signal. In some embodiments, the windowed approach may be more computationally efficient by avoiding continuous computation through time and allowed occupancy to be assessed periodically. In some embodiments, the size of the time windows may be any suitable size to account for changes in occupancy, such as, e.g., one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, or other suitable window size to balance actuation set point adjustment that accurately follows usage patterns while minimizing computational resource needs.

[0110] In some embodiments, the time-series motion data may be segmented into time windows that align with the time slots of the schedule period, e.g., based on the time and/or duration of each instance of motion. Thus, the time windows of motion data may be aligned with the time slots of the schedule period to correlate occupancy as signaled by motion with actuation set point adjustment scheduling.

[0111] In some embodiments, the occupancy learning engine 112 may develop occupancy metrics quantifying a degree of occupancy of the area for each time window at 206.

[0112] In some embodiments, for each time slot, the occupancy learning engine 112 may develop an occupancy metric based on the motion data associated with the time slot. For example, the motion data may indicate a quantity of motion, such as a number of instances of motion, a duration of each instance, a frequency of instances, a number of users detected, among other measures of motion quantity within the time slot.

[0113] In some embodiments, the based on the quantity of motion, the occupancy learning engine 112 may classify or categorize the quantity of motion in a time slot according to an occupancy score. For example, the occupancy score may be a three-point scoring system, where 1 denotes unused, 2 denotes temporarily unused and 3 denotes in use (or in other words, empty, unoccupied, and occupied, respectively), e.g. , based on two thresholds, an upper threshold and lower threshold. [0114] In some embodiments, the categorization may be determined based on thresholds of quantity of motion, such as, e.g., an upper threshold and lower threshold. In the three-point scoring system, a time slot with a quantity of motion above the upper threshold may be categorized as a 3, a time slot with a quantity of motion below the lower threshold may be categorized as a 1 , and a time slot with a quantity of motion above the lower threshold and below the upper threshold may be categorized as a two.

[0115] In some embodiments, the occupancy learning engine 112 may use the score of each time slot to develop a predictive occupancy metric that predicts the occupancy level of the space in the future based on the past, historical and/or recent occupancy metrics. In some embodiments, the occupancy learning engine 112 may perform time-based modelling such as by developing a statistical aggregation, such as a mean, median, standard deviation, scored mean, score average, percentage deviation from a mean or median, or other suitable aggregation of the occupancy metric for a particular time slot in the schedule period. In some embodiments, the aggregation may be augmented with a forgetting/decay rate such that more recent occupancy levels are scored higher in developing the aggregation than more distant occupancy levels. [0116] In some embodiments, rather than scoring each time slot based on the quantity of motion, the occupancy learning engine 112 may employ an upper score associated with a quantity of motion that indicates user presence, and a lower score associated a quantity of motion that indicates no user presence in the space. In some embodiments, the upper score and the lower score may be separated by one or more intermediate scores that indicate a likelihood of a user presence.

[0117] In some embodiments, using such a weighting scheme, the occupancy learning engine 112 may assign each time slot with the upper weight or the lower weight based on the quantity of motion in a current time period. The occupancy learning engine 112 may then determine an occupancy metric for each time slot based on time-based modelling using an aggregation of the assigned score and historical assigned score such that the occupancy metric may be any of the upper score, the lower score or the one or more intermediate scores based on a reward equation with a decay rate.

[0118] In some embodiments, the occupancy learning engine 112 may model a time-based occupancy of the area based on the occupancy metrics at 208 to provide predictive occupancy metrics indicative of usage patterns of the particular area of the home.

[0119] In some embodiments, the occupancy learning engine 112 may access previous occupancy metrics associated with at least one previous set of time-series motion data. In some embodiments, the occupancy learning engine 112 may align the previous time slots of the previous set of timeseries motion data with the time slots of the current time-series motion data. In some embodiments, the occupancy learning engine 112 may then generate the occupancy metric in each time slot using a time-based modelling of occupancy based on the plurality of previous occupancy metrics and the current occupancy score of each time slot.

[0120] In some embodiments, to model time-based occupancy, the occupancy learning engine 112 may generate a predictive occupancy metric from the current and previous occupancy metrics for each time slot of the current and previous schedule periods. To do so, the occupancy learning engine 112 may employ a reward function with a decay rate that combines the current occupancy metric with previous occupancy metrics to produce a predictive weight in a range of the lower weight, intermediate weight(s) through upper weight. For example, the occupancy learning engine may employ a function such as equation (1) below:

Equation (1) [0121] Where m _p is the predictive occupancy metric, A is the decay rate, m is the previous occupancy metric, s is the current occupancy score based on the time-series motion data, and t is the schedule period. Accordingly, the occupancy learning engine 112 may update an occupancy metric derived for a previous schedule period with the current occupancy score using a decay rate A. The decay rate A may be predefined (e.g., 2, 3, 4, 5, 6, or more), user configurable, and/or learned via a suitable optimization function using feedback from user input. Accordingly, the occupancy learning engine 112 may model the occupancy of the space by quantifying occupancy patterns within the space from previous data. The model of the occupancy may be employed in a predictive fashion to indicate a likelihood that the user will occupy or otherwise use the space in the future based on past usage behaviors.

[0122] In some embodiments, the scheduling engine 114 may predict an actuation schedule for one or more area-specific actuators of, e.g., a comfort system of the home based on the model of time-based occupancy at 210.

[0123] In some embodiments, the actuation schedule may be based on the predictive occupancy metrics, e.g., an occupancy schedule. In some embodiments, the scheduling engine 114 may correlate a degree of actuator set point adjustment to the occupancy metric of each time slot in the occupancy schedule. For example, the user may have defined a user set point and a minimum set point whereby the user set point is the intended actuator set point for while the user occupies the space, and the minimum set point is a minimum actuator set point allowable. The scheduling engine 114 may employ a predefined relationship between occupancy metric to generate a degree of adjustment from the user set point. The relationship may be a direct, linear relationship that produces actuator set points between the user set point and the minimum set point based on the occupancy metric for a time slot. In some embodiments, the relationship may be logarithmic, exponential, pre-mapped (e.g., using a look-up table), to correlate actuator set point adjustments to the occupancy metric of a time slot.

[0124] In some embodiments, the actuator set point in the actuation schedule may take into account time-based temperature characteristics in the particular, such as, e.g., a likely temperature loss given the time of year and/or time of day. For example, in some embodiments, the temperature loss due to forced hot air versus forced hot water versus electrical heating versus hydronic heating may vary, thus benefitting from different parameters defining the temperature characteristics of the area. The temperature characteristics may also depend on the area itself, such as, e.g., size, shape, degree of insulation, number of windows, number of doors, exposure to the exterior (e.g., via screened in wall(s) or other openings), among other characteristics of the area or any combination thereof. Thus, the actuator set point may be determined based on likelihood of user presence and time since and/or until user presence according to the predictive occupancy metrics. [0125] In some embodiments, where the time-series motion data includes a recognized user activity, e.g., based on computer vision analysis of imagery of the user in the space, the scheduling engine 114 may adjust the actuator set point based on the type of activity. For example, if the user is usually exercising or currently exercising at a particular time slot, the scheduling engine 114 may modify a temperature set point downwards to make the exercise more comfortable. Similarly, is the user is sleeping or usually sleeping, the scheduling engine 114 may modify a temperature set point upwards to make the sleep more comfortable.

[0126] In some embodiments, upon generating the actuation schedule for operation of the comfort system in the area of the home, the scheduling engine 114 may identify the actuator 104 in the area associated with the sensor 108. In some embodiments, the actuator 104 may be indexed according to space and/or sensor 108 such that the scheduling engine 114 may access the index to identify the actuator 104 mapped to the space and/or sensor 108. The scheduling engine 114 may then communicate the actuation schedule to the edge device 102 and/or the actuator 104. The actuation schedule may then cause the edge device 102 to control the actuator 104 according to the actuator set points encoded in the actuation schedule based on the prediction of the level of occupancy of the space during each subsequent time slot in subsequent schedule periods.

[0127] FIG. 3 depicts an example adaptive thermostat control based on occupancy events in accordance with one or more embodiments of the present disclosure. The dotted line depicts actuator set point based on the occupancy metric including an occupancy score. The thick dashed line depicts user set point manually input by the user. The thin dashed line indicates an alternative user set point manually input by the user.

[0128] FIG. 4 depicts an example adaptive thermostat control based on occupancy events in accordance with one or more embodiments of the present disclosure. The dotted line depicts actuator set point based on the occupancy metric including an occupancy probability. The thick dashed line depicts user set point manually input by the user. The thin dashed line indicates an alternative user set point manually input by the user. [0129] FIG. 5 depicts an example multi-zone adaptive thermostat control based on occupancy events in accordance with one or more embodiments of the present disclosure. The dotted line depicts actuator set point based on the occupancy metric including an occupancy probability. The thick dashed line depicts user set point manually input by the user. The thin dashed line indicates an alternative user set point manually input by the user.

[0130] FIG. 6, FIG. 7, FIG. 8, and FIG. 9 depict example adaptive thermostat control based on occupancy events and a learned rate of temperature loss in accordance with one or more embodiments of the present disclosure.

[0131] In some embodiments, timed based temperature characteristics may be used to determine how dynamic a modification to a setpoint change can be without affecting comfort. This is an application (per room) value that may be learned after installation and adapt to seasonal effects on heating or cooling loads. In regards to FIG. 6, FIG. 7, FIG. 8, and FIG. 9, the term “room” is used as illustrative of one or more embodiments of the adaptive thermostat control. However, one or ordinary skill in the art would understand that FIG. 6, FIG. 7, FIG. 8, and FIG. 9 and the associated description apply to embodiments using any area type, including, e.g., a room, zone, region, grouping of rooms, outdoor space, floor of a building, or any other suitable division of the building/space.

[0132] In some embodiments, a +/- 0.5C deviation from setpoint may not be detectable (by feel). Thus, a room constant may be defined as a first order response time for a 0.5C temperature reduction (or increase) after a downward setpoint change that may result in a corresponding reduction in energy demand, which in some cases be as low as a 0% demand. In some embodiments, therefore, if the presence detection function determines a low probability of occupation to decrease the setpoint 0.5C after the room first order response time. The function may repeat until the minimum comfort parameter is achieved. In some embodiments, each room time constant with no presence detected will result in a 0.5°C reduction (or increase) in the setpoint.

[0133] In some embodiments, the body cannot detect a 0.5°C change in ambient temperature, but each will reduce energy consumption by 5%, such that two time constants result in 10%. Each heat-up and cool-down is used to update a heat-up ramp and 1 st order cooling time This learning process adapts to both the space and the season. Moreover, a 1°C reduction in setpoint equates to a 10% saving in energy. Therefore, the reduction in heat loss from a room as the result of a 0.5°C setpoint reduction is minimal, about 5%. This might lead thoughts that a more aggressive setback is the correct action, however such setbacks may result in noticeable temperature swings that adversely affect comfort and HVAC plant efficiencies.

[0134] Thus, to protect comfort levels, a rapidly cooling room may reduce the setpoint slower than in a well-insulated room. Accordingly, a variable setpoint reduction or rate of change may be employed to account for insulation of the space. For example, a progressive reduction in room setpoint related to the rooms learned performance may be employed. A progressive reduction may provide energy savings, keep the heating controls within a modulation band ensuring a reduction of heat input rather than turning it off completely.

[0135] In some embodiments, the actuator may employ fuzzy logic to learn a percent demand at each potential setpoint. This is a feedforward control loop with an error correction feedback loop on top that drives the learning and provides additional demand to get temperature to a setpoint. The output is a modulating (PWM for ON/OFF or direct modulation for communicating appliances, therefore a 0.5°C reduction of the setpoint may immediately result in a heat input reduction but still leave a small percent demand allowing temperatures to reduce in a controlled manor.

[0136] In some embodiments, as the room temperature falls, the percent demand may gradually increase back up to the setpoint minus a 0.5°C fuzzy value. The logic may be timed (based on a learned time constant) and further react to reduce the setpoint as the temperature falls and the nonoccupied condition continues. In some embodiments, for systems that lose heat rapidly the logic may have greatest effect in a poorly insulated space, but may nevertheless benefit a well-insulated, fast responding space by making the operation more usable and adaptable.

[0137] FIG. 10 depicts a block diagram of an exemplary computer-based system and platform 1000 in accordance with one or more embodiments of the present disclosure. However, not all of these components may be required to practice one or more embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of various embodiments of the present disclosure. In some embodiments, the illustrative computing devices and the illustrative computing components of the exemplary computer-based system and platform 1000 may be configured to manage a large number of members and concurrent transactions, as detailed herein. In some embodiments, the exemplary computer-based system and platform 1000 may be based on a scalable computer and network architecture that incorporates varies strategies for assessing the data, caching, searching, and/or database connection pooling. An example of the scalable architecture is an architecture that is capable of operating multiple servers.

[0138] In some embodiments, referring to FIG. 10, client device 1002, client device 1003 through client device 1004 (e.g., clients) of the exemplary computer-based system and platform 1000 may include virtually any computing device capable of receiving and sending a message over a network (e.g., cloud network), such as network 1005, to and from another computing device, such as servers 1006 and 1007, each other, and the like. In some embodiments, the client devices 1002 through 1004 may be personal computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, and the like. In some embodiments, one or more client devices within client devices 1002 through 1004 may include computing devices that typically connect using a wireless communications medium such as cell phones, smart phones, pagers, walkie talkies, radio frequency (RF) devices, infrared (IR) devices, citizens band radio, integrated devices combining one or more of the preceding devices, or virtually any mobile computing device, and the like. In some embodiments, one or more client devices within client devices 1002 through 1004 may be devices that are capable of connecting using a wired or wireless communication medium such as a PDA, POCKET PC, wearable computer, a laptop, tablet, desktop computer, a netbook, a video game device, a pager, a smart phone, an ultra-mobile personal computer (UMPC), and/or any other device that is equipped to communicate over a wired and/or wireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, OFDM, OFDMA, LTE, satellite, ZigBee, etc.). In some embodiments, one or more client devices within client devices 1002 through 1004 may include may run one or more applications, such as Internet browsers, mobile applications, voice calls, video games, videoconferencing, and email, among others. In some embodiments, one or more client devices within client devices 1002 through 1004 may be configured to receive and to send web pages, and the like. In some embodiments, an exemplary specifically programmed browser application of the present disclosure may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web based language, including, but not limited to Standard Generalized Markup Language (SMGL), such as HyperText Markup Language (HTML), a wireless application protocol (WAP), a Handheld Device Markup Language (HDML), such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, and the like. In some embodiments, a client device within client devices 1002 through 1004 may be specifically programmed by either Java, .Net, QT, C, C++, Python, PHP and/or other suitable programming language. In some embodiment of the device software, device control may be distributed between multiple standalone applications. In some embodiments, software components/applications can be updated and redeployed remotely as individual units or as a full software suite. In some embodiments, a client device may periodically report status or send alerts over text or email. In some embodiments, a client device may contain a data recorder which is remotely downloadable by the user using network protocols such as FTP, SSH, or other file transfer mechanisms. In some embodiments, a client device may provide several levels of user interface, for example, advance user, standard user. In some embodiments, one or more client devices within client devices 1002 through 1004 may be specifically programmed include or execute an application to perform a variety of possible tasks, such as, without limitation, messaging functionality, browsing, searching, playing, streaming, or displaying various forms of content, including locally stored or uploaded messages, images and/or video, and/or games.

[0139] In some embodiments, the exemplary network 1005 may provide network access, data transport and/or other services to any computing device coupled to it. In some embodiments, the exemplary network 1005 may include and implement at least one specialized network architecture that may be based at least in part on one or more standards set by, for example, without limitation, Global System for Mobile communication (GSM) Association, the Internet Engineering Task Force (IETF), and the Worldwide Interoperability for Micro wave Access (WiMAX) forum. In some embodiments, the exemplary network 1005 may implement one or more of a GSM architecture, a General Packet Radio Service (GPRS) architecture, a Universal Mobile Telecommunications System (UMTS) architecture, and an evolution of UMTS referred to as Long Term Evolution (LTE). In some embodiments, the exemplary network 1005 may include and implement, as an alternative or in conjunction with one or more of the above, a WiMAX architecture defined by the WiMAX forum. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary network 1005 may also include, for instance, at least one of a local area network (LAN), a wide area network (WAN), the Internet, a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual private network (VPN), an enterprise IP network, or any combination thereof. In some embodiments and, optionally, in combination of any embodiment described above or below, at least one computer network communication over the exemplary network 1005 may be transmitted based at least in part on one of more communication modes such as but not limited to: NFC, RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, OFDM, OFDMA, LTE, satellite and any combination thereof. In some embodiments, the exemplary network 1005 may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), a content delivery network (CDN) or other forms of computer or machine readable media.

[0140] In some embodiments, the exemplary server 1006 or the exemplary server 1007 may be a web server (or a series of servers) running a network operating system, examples of which may include but are not limited to Apache on Linux or Microsoft IIS (Internet Information Services). In some embodiments, the exemplary server 1006 or the exemplary server 1007 may be used for and/or provide cloud and/or network computing. Although not shown in FIG. 10, in some embodiments, the exemplary server 1006 or the exemplary server 1007 may have connections to external systems like email, SMS messaging, text messaging, ad content providers, etc. Any of the features of the exemplary server 1006 may be also implemented in the exemplary server 1007 and vice versa.

[0141] In some embodiments, one or more of the exemplary servers 1006 and 1007 may be specifically programmed to perform, in non-limiting example, as authentication servers, search servers, email servers, social networking services servers, Short Message Service (SMS) servers, Instant Messaging (IM) servers, Multimedia Messaging Service (MMS) servers, exchange servers, photo-sharing services servers, advertisement providing servers, financial/banking-related services servers, travel services servers, or any similarly suitable service-base servers for users of the client devices 1001 through 1004.

[0142] In some embodiments and, optionally, in combination of any embodiment described above or below, for example, one or more exemplary computing client devices 1002 through 1004, the exemplary server 1006, and/or the exemplary server 1007 may include a specifically programmed software module that may be configured to send, process, and receive information using a scripting language, a remote procedure call, an email, a tweet, Short Message Service (SMS), Multimedia Message Service (MMS), instant messaging (IM), an application programming interface, Simple Object Access Protocol (SOAP) methods, Common Object Request Broker Architecture (CORBA), HTTP (Hypertext Transfer Protocol), REST (Representational State Transfer), SOAP (Simple Object Transfer Protocol), MLLP (Minimum Lower Layer Protocol), or any combination thereof. [0143] FIG. 11 depicts a block diagram of another exemplary computer-based system and platform 1100 in accordance with one or more embodiments of the present disclosure. However, not all of these components may be required to practice one or more embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of various embodiments of the present disclosure. In some embodiments, the client device 1102a, client device 1102b through client device 1102n shown each at least includes a computer-readable medium, such as a random-access memory (RAM) 1108 coupled to a processor 1110 or FLASH memory. In some embodiments, the processor 1110 may execute computer-executable program instructions stored in memory 1108. In some embodiments, the processor 1110 may include a microprocessor, an ASIC, and/or a state machine. In some embodiments, the processor 1110 may include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor 1110, may cause the processor 1110 to perform one or more steps described herein. In some embodiments, examples of computer- readable media may include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor, such as the processor 1110 of client device 1102a, with computer-readable instructions. In some embodiments, other examples of suitable media may include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape, or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. In some embodiments, the instructions may comprise code from any computer-programming language, including, for example, C, C++, Visual Basic, Java, Python, Perl, JavaScript, and etc.

[0144] In some embodiments, client devices 1102a through 1102n may also comprise a number of external or internal devices such as a mouse, a CD-ROM, DVD, a physical or virtual keyboard, a display, or other input or output devices. In some embodiments, examples of client devices 1102a through 1102n (e.g., clients) may be any type of processor-based platforms that are connected to a network 1106 such as, without limitation, personal computers, digital assistants, personal digital assistants, smart phones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In some embodiments, client devices 1102a through 1102n may be specifically programmed with one or more application programs in accordance with one or more principles/methodologies detailed herein. In some embodiments, client devices 1102a through 1102n may operate on any operating system capable of supporting a browser or browser-enabled application, such as Microsoft™, Windows™, and/or Linux. In some embodiments, client devices 1102a through 1102n shown may include, for example, personal computers executing a browser application program such as Microsoft Corporation's Internet Explorer™, Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera. In some embodiments, through the member computing client devices 1102a through 1102n, user 1112a, user 1112b through user 1112n, may communicate over the exemplary network 1106 with each other and/or with other systems and/or devices coupled to the network 1106. As shown in FIG. 11, exemplary server devices 1104 and 1113 may include processor 1105 and processor 1114, respectively, as well as memory 1117 and memory 1116, respectively. In some embodiments, the server devices 1104 and 1113 may be also coupled to the network 1106. In some embodiments, one or more client devices 1102a through 1102n may be mobile clients.

[0145] In some embodiments, at least one database of exemplary databases 1107 and 1115 may be any type of database, including a database managed by a database management system (DBMS). In some embodiments, an exemplary DBMS-managed database may be specifically programmed as an engine that controls organization, storage, management, and/or retrieval of data in the respective database. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to provide the ability to query, backup and replicate, enforce rules, provide security, compute, perform change and access logging, and/or automate optimization. In some embodiments, the exemplary DBMS -managed database may be chosen from Oracle database, IBM DB2, Adaptive Server Enterprise, FileMaker, Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQL implementation. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to define each respective schema of each database in the exemplary DBMS, according to a particular database model of the present disclosure which may include a hierarchical model, network model, relational model, object model, or some other suitable organization that may result in one or more applicable data buildings that may include fields, records, files, and/or objects. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to include metadata about the data that is stored. [0146] In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be specifically configured to operate in a cloud computing/architecture 1125 such as, but not limiting to: infrabuilding a service (laaS) 1310, platform as a service (PaaS) 1308, and/or software as a service (SaaS) 1306 using a web browser, mobile app, thin client, terminal emulator or other endpoint 1304. FIGs. 12 and 13 illustrate schematics of exemplary implementations of the cloud computing/architecture(s) in which the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be specifically configured to operate.

[0147] It is understood that at least one aspect/functionality of various embodiments described herein can be performed in real-time and/or dynamically. As used herein, the term “real-time” is directed to an event/action that can occur instantaneously or almost instantaneously in time when another event/action has occurred. For example, the “real-time processing,” “real-time computation,” and “real-time execution” all pertain to the performance of a computation during the actual time that the related physical process (e.g., a user interacting with an application on a mobile device) occurs, in order that results of the computation can be used in guiding the physical process.

[0148] As used herein, the term “dynamically” and term “automatically,” and their logical and/or linguistic relatives and/or derivatives, mean that certain events and/or actions can be triggered and/or occur without any human intervention. In some embodiments, events and/or actions in accordance with the present disclosure can be in real-time and/or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc.

[0149] As used herein, the term “runtime” corresponds to any behavior that is dynamically determined during an execution of a software application or at least a portion of software application.

[0150] In some embodiments, exemplary inventive, specially programmed computing systems and platforms with associated devices are configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols/modes such as, without limitation, IPX/SPX, X.25, AX.25, AppleTalk(TM), TCP/IP (e.g., HTTP), near-field wireless communication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes.

[0151] In some embodiments, the NFC can represent a short-range wireless communications technology in which NFC-enabled devices are “swiped,” “bumped,” “tap” or otherwise moved in close proximity to communicate. In some embodiments, the NFC could include a set of short- range wireless technologies, typically requiring a distance of 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to 424 kbit/s. In some embodiments, the NFC can involve an initiator and a target; the initiator actively generates an RF field that can power a passive target. In some embodiment, this can enable NFC targets to take very simple form factors such as tags, stickers, key fobs, or cards that do not require batteries. In some embodiments, the NFC’s peer-to-peer communication can be conducted when a plurality of NFC-enable devices (e.g., smartphones) within close proximity of each other.

[0152] In some embodiments, the Bluetooth can represent a short-range wireless communications technology in which Bluetooth-enabled devices are paired to establish a communication link may allow one device to send commands to another. For example, a user with a smartphone may communicate with a smart home device, such as a sensor 108 or actuator 104, or a security system, or other suitable Bluetooth enabled home system, to establish setting and parameters, such as an arm/set state of a security system. The use of Bluetooth may facilitate the detection of triggers in home systems that can be used to identify the presence based on the arm/set state of the home system without the need for an Internet and/or Wi-Fi connection.

[0153] The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical, or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others.

[0154] As used herein, the terms “computer engine” and “engine” identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).

[0155] Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multicore, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.

[0156] Computer-related systems, computer systems, and systems, as used herein, include any combination of hardware and software. Examples of software may include software components, programs, applications, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0157] One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores,” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that make the logic or processor. Of note, various embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc ).

[0158] In some embodiments, one or more of illustrative computer-based systems or platforms of the present disclosure may include or be incorporated, partially or entirely into at least one personal computer (PC), laptop computer, ultra-laptop computer, tablet, touch pad, portable computer, handheld computer, palmtop computer, personal digital assistant (PDA), cellular telephone, combination cellular telephone/PDA, television, smart device (e.g., smart phone, smart tablet or smart television), mobile internet device (MID), messaging device, data communication device, and so forth.

[0159] As used herein, term “server” should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Cloud servers are examples.

[0160] In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may obtain, manipulate, transfer, store, transform, generate, and/or output any digital object and/or data unit (e.g., from inside and/or outside of a particular application) that can be in any suitable form such as, without limitation, a file, a contact, a task, an email, a message, a map, an entire application (e.g., a calculator), data points, and other suitable data. In some embodiments, as detailed herein, one or more of the computer-based systems of the present disclosure may be implemented across one or more of various computer platforms such as, but not limited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3) Microsoft Windows™; (4) Open VMS™; (5) OS X (MacOS™); (6) UNIX™; (7) Android; (8) iOS™; (9) Embedded Linux; (10) Tizen™; (11) WebOS™; (12) Adobe AIR™; (13) Binary Runtime Environment for Wireless (BREW™); (14) Cocoa™ (API); (15) Cocoa™ Touch; (16) Java™ Platforms; (17) JavaFX™; (18) QNX™; (19) Mono; (20) Google Blink; (21) Apple WebKit; (22) Mozilla Gecko™; (23) Mozilla XUL; (24) .NET Framework; (25) Silverlight™; (26) Open Web Platform; (27) Oracle Database; (28) Qt™; (29) SAP NetWeaver™; (30) Smartface™; (31) Vexi™; (32) Kubernetes™ and (33) Windows Runtime (WinRT™) or other suitable computer platforms or any combination thereof. In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to utilize hardwired circuitry that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways as a software component such as, without limitation, a stand-alone software package, a combination of software packages, or it may be a software package incorporated as a “tool” in a larger software product.

[0161] For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.

[0162] In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to handle numerous concurrent users that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000- 9,999 ), at least 10,000 (e.g., but not limited to, 10,000-99,999 ), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to, 1,000,000,000-999,999,999,999), and so on.

[0163] In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to output to distinct, specifically programmed graphical user interface implementations of the present disclosure (e.g., a desktop, a web app., etc.). In various implementations of the present disclosure, a final output may be displayed on a displaying screen which may be, without limitation, a screen of a computer, a screen of a mobile device, or the like. In various implementations, the display may be a holographic display. In various implementations, the display may be a transparent surface that may receive a visual projection. Such projections may convey various forms of information, images, or objects. For example, such projections may be a visual overlay for a mobile augmented reality (MAR) application.

[0164] In some embodiments, illustrative computer-based systems or platforms of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming and/or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.

[0165] As used herein, the term “mobile electronic device,” or the like, may refer to any portable electronic device that may or may not be enabled with location tracking functionality (e.g., MAC address, Internet Protocol (IP) address, or the like). For example, a mobile electronic device can include, but is not limited to, a mobile phone, Personal Digital Assistant (PDA), Blackberry ™, Pager, Smartphone, or any other reasonable mobile electronic device.

[0166] As used herein, terms “proximity detection,” “locating,” “location data,” “location information,” and “location tracking” refer to any form of location tracking technology or locating method that can be used to provide a location of, for example, a particular computing device, system or platform of the present disclosure and any associated computing devices, based at least in part on one or more of the following techniques and devices, without limitation: accelerometer(s), gyroscope(s), Global Positioning Systems (GPS); GPS accessed using Bluetooth™; GPS accessed using any reasonable form of wireless and non-wireless communication; WiFi™ server location data; Bluetooth™ based location data; triangulation such as, but not limited to, network based triangulation, WiFi™ server information based triangulation, Bluetooth™ server information based triangulation; Cell Identification based triangulation, Enhanced Cell Identification based triangulation, Uplink-Time difference of arrival (U-TDOA) based triangulation, Time of arrival (TOA) based triangulation, Angle of arrival (AOA) based triangulation; techniques and systems using a geographic coordinate system such as, but not limited to, longitudinal and latitudinal based, geodesic height based, Cartesian coordinates based; Radio Frequency Identification such as, but not limited to, Long range RFID, Short range RFID; using any form of RFID tag such as, but not limited to active RFID tags, passive RFID tags, battery assisted passive RFID tags; or any other reasonable way to determine location. For ease, at times the above variations are not listed or are only partially listed; this is in no way meant to be a limitation.

[0167] As used herein, terms “cloud,” “Internet cloud,” “cloud computing,” “cloud architecture,” and similar terms correspond to at least one of the following: (1) a large number of computers connected through a real-time communication network (e.g., Internet); (2) providing the ability to run a program or application on many connected computers (e.g., physical machines, virtual machines (VMs)) at the same time; (3) network-based services, which appear to be provided by real server hardware, and are in fact served up by virtual hardware (e.g., virtual servers), simulated by software running on one or more real machines (e.g., allowing to be moved around and scaled up (or down) on the fly without affecting the end user).

[0168] In some embodiments, the illustrative computer-based systems or platforms of the present disclosure may be configured to securely store and/or transmit data by utilizing one or more of encryption techniques (e.g., private/public key pair, Triple Data Encryption Standard (3DES), block cipher algorithms (e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms (e g., MD5, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL, RNGs).

[0169] As used herein, the term “user” shall have a meaning of at least one user. In some embodiments, the terms “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein and/or a consumer of data supplied by a data provider. By way of example, and not limitation, the terms “user” or “subscriber” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session or can refer to an automated software application which receives the data and stores or processes the data.

[0170] The aforementioned examples are, of course, illustrative, and not restrictive.

[0171] At least some aspects of the present disclosure will now be described with reference to the following numbered clauses.

1. A method including: receiving, by at least one processor, time-series presence data from at least one presence sensing device associated with an area; where the time-series presence data includes: at least one instance of presence detected in the area, and at least one time associated with the at least one instance; determining, by the at least one processor, an occupancy metric associated with the area in each time slot of a plurality of time slots based at least in part on the at least one instance of presence and the at least one time; where the plurality of time slots includes sub-divisions of each day of a week; generating, by the at least one processor, an occupancy schedule for the area based at least in part on: the occupancy metric associated with the area in each time slot and a history of occupancy metrics associated with the area in each time slot; where the occupancy schedule represents a prediction of a level of occupancy of the area during each subsequent time slot in subsequent weeks; determining, by the at least one processor, at least one comfort system actuator associated with the area; and communicating, by the at least one processor, the occupancy schedule to the at least one comfort system actuator; where the occupancy schedule is configured to cause the at least one comfort system actuator to actuate at least one building actuator based at least in part on the prediction of the level of occupancy of the area during each subsequent time slot in subsequent weeks.

2. The method of clause 1, further including utilizing, by the at least one processor, an occupancy prediction machine learning model to predict the occupancy schedule based at least in part on the occupancy metric associated with the area in each time slot, where the occupancy prediction machine learning model includes a trained prediction layer including parameters trained to generate the occupancy schedule based on: a regression layer including a plurality of learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

3. The method of an preceding clause, further including: segmenting, by the at least one processor, the time-series presence data into a plurality of time windows; and assigning, by the at least one processor, each time window of the plurality of time windows to a particular time slot of the plurality of time slots.

4. The method of any preceding clause, further including: determining, by the at least one processor, a quantity of presence in each time slot of the plurality of time slots; generating, by the at least one processor, an occupancy metric associated with the quantity of presence in each time slot; and determining, by the at least one processor, the occupancy metric in each time slot based at least in part on the occupancy metric in each time slot.

5. The method of clause 4, where the quantity of presence includes at least one of: a frequency of presence in each time slot, or a duration of presence in each time slot.

6. The method of clause 4, further including: accessing, by the at least one processor, a plurality of previous occupancy metrics associated with at least one previous week; aligning, by the at least one processor, a plurality of previous time slots of the at least one previous week with the plurality of time slots of the week; generating, by the at least one processor, the occupancy metric in each time slot based at least in part on the plurality of previous occupancy metrics associated with the at least one previous week and the occupancy metric of each time slot.

7. The method of clause 6, further including: generating, by the at least one processor, the occupancy metric in each time slot based at least in part on a decay rate applied to an aggregation of each previous occupancy metric of each time slot and each occupancy metric of each time slot.

8. The method of clause 7, further including: utilizing, by the at least one processor, the occupancy metric associated with the area in each time slot as a predictive occupancy status of the area; and generating, by the processor, the occupancy schedule for the area based at least in part on the predictive occupancy status.

9. The method of any preceding clause, where the at least one presence sensing device includes at least one of: a security camera, an infrared presence detector, a door sensor, a window sensor, a smart light switch, a Wi-Fi router, a radio-frequency identification (RFID) reader, a smart lock, vibration sensing, pressure sensing, ultrasound,

LiDAR, radar, or local setpoint adjustment. method of any preceding clause, further including: utilizing, by the at least one processor, an occupancy status prediction machine learning model to predict a predictive occupancy status associated with each time slot based at least in part on: a regression layer including a plurality of learned regression weight trained to correlate the occupancy metric to an occupancy status prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot. stem including: at least one processor in communication with at least one non-transitory computer readable medium having software instructions stored thereon, where the at least one processor, upon execution of the software instructions, is configured to: receive time-series presence data from at least one presence sensing device associated with an area; where the time-series presence data includes: at least one instance of presence detected in the area, and at least one time associated with the at least one instance; determine an occupancy metric associated with the area in each time slot of a plurality of time slots based at least in part on the at least one instance of presence and the at least one time; where the plurality of time slots includes sub-divisions of each day of a week; generate an occupancy schedule for the area based at least in part on: the occupancy metric associated with the area in each time slot and a history of occupancy metrics associated with the area in each time slot; where the occupancy schedule represents a prediction of a level of occupancy of the area during each subsequent time slot in subsequent weeks; determine at least one comfort system actuator associated with the area; and communicate the occupancy schedule to the at least one comfort system actuator; where the occupancy schedule is configured to cause the at least one comfort system actuator to actuate at least one building actuator based at least in part on the prediction of the level of occupancy of the area during each subsequent time slot in subsequent weeks.

12. The system of clause 11, where the at least one processor, upon execution of the software instructions, is further configured to utilize an occupancy prediction machine learning model to predict the occupancy schedule based at least in part on the occupancy metric associated with the area in each time slot, where the occupancy prediction machine learning model includes a trained prediction layer including parameters trained to generate the occupancy schedule based on: a regression layer including a plurality of learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

13. The system of any of clauses 11 to 12, where the at least one processor, upon execution of the software instructions, is further configured to: segment the time-series presence data into a plurality of time windows; and assign each time window of the plurality of time windows to a particular time slot of the plurality of time slots.

14. The system of any of clauses 11 to 13, where the at least one processor, upon execution of the software instructions, is further configured to: determine a quantity of presence in each time slot of the plurality of time slots; generate an occupancy metric associated with the quantity of presence in each time slot; and determine the occupancy metric in each time slot based at least in part on the occupancy metric in each time slot.

15. The system of clause 14, where the quantity of presence includes at least one of: a frequency of presence in each time slot, or a duration of presence in each time slot.

16. The system of clause 14, where the at least one processor, upon execution of the software instructions, is further configured to: access a plurality of previous occupancy metrics associated with at least one previous week; align a plurality of previous time slots of the at least one previous week with the plurality of time slots of the week; generate the occupancy metric in each time slot based at least in part on the plurality of previous occupancy metrics associated with the at least one previous week and the occupancy metric of each time slot.

17. The system of clause 16, where the at least one processor, upon execution of the software instructions, is further configured to: generate the occupancy metric in each time slot based at least in part on a decay rate applied to an aggregation of each previous occupancy metric of each time slot and each occupancy metric of each time slot.

18. The system of clause 17, where the at least one processor, upon execution of the software instructions, is further configured to: utilize the occupancy metric associated with the area in each time slot as a predictive occupancy status of the area; and generate the occupancy schedule for the area based at least in part on the predictive occupancy status.

19. The system of any of clauses 11 to 18, where the at least one presence sensing device includes at least one of: a security camera, an infrared presence detector, a door sensor, a window sensor, a smart light switch, a Wi-Fi router, a radio-frequency identification (RFID) reader, a smart lock, vibration sensing, pressure sensing, ultrasound,

LiDAR, radar, or local setpoint adjustment.

20. The system of any of clauses 11 to 19, where the at least one processor, upon execution of the software instructions, is further configured to: utilize an occupancy status prediction machine learning model to predict a predictive occupancy status associated with each time slot based at least in part on: a regression layer including a plurality of learned regression weight trained to correlate the occupancy metric to an occupancy status prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

21. A non -transitory computer readable medium including software instructions that, when executed, are configured to cause at least one processor to perform steps including: receiving time-series presence data from at least one presence sensing device associated with an area; where the time-series presence data includes: at least one instance of presence detected in the area, and at least one time associated with the at least one instance; determining an occupancy metric associated with the area in each time slot of a plurality of time slots based at least in part on the at least one instance of presence and the at least one time; where the plurality of time slots includes sub-divisions of each day of a week; generating an occupancy schedule for the area based at least in part on: the occupancy metric associated with the area in each time slot and a history of occupancy metrics associated with the area in each time slot; where the occupancy schedule represents a prediction of a level of occupancy of the area during each subsequent time slot in subsequent weeks; determining at least one comfort system actuator associated with the area; and communicating the occupancy schedule to the at least one comfort system actuator; where the occupancy schedule is configured to cause the at least one comfort system actuator to actuate at least one building actuator based at least in part on the prediction of the level of occupancy of the area during each subsequent time slot in subsequent weeks.

22. The non -transitory computer readable medium of clause 21, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to utilizing an occupancy prediction machine learning model to predict the occupancy schedule based at least in part on the occupancy metric associated with the area in each time slot, where the occupancy prediction machine learning model includes a trained prediction layer including parameters trained to generate the occupancy schedule based on: a regression layer including a plurality of learned regression weights trained to correlate the occupancy metric to an occupancy schedule prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

23. The non -transitory computer readable medium of any of clauses 21 to 22, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: segmenting the time-series presence data into a plurality of time windows; and assigning each time window of the plurality of time windows to a particular time slot of the plurality of time slots.

24. The non -transitory computer readable medium of any of clauses 21 to 23, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: determining a quantity of presence in each time slot of the plurality of time slots; generating an occupancy metric associated with the quantity of presence in each time slot; and determining the occupancy metric in each time slot based at least in part on the occupancy metric in each time slot.

25. The non -transitory computer readable medium of clause 24, where the quantity of presence includes at least one of: a frequency of presence in each time slot, or a duration of presence in each time slot.

26. The non-transitory computer readable medium of clause 24, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: accessing a plurality of previous occupancy metrics associated with at least one previous week; aligning a plurality of previous time slots of the at least one previous week with the plurality of time slots of the week; generating the occupancy metric in each time slot based at least in part on the plurality of previous occupancy metrics associated with the at least one previous week and the occupancy metric of each time slot.

27. The non -transitory computer readable medium of clause 26, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: generating the occupancy metric in each time slot based at least in part on a decay rate applied to an aggregation of each previous occupancy metric of each time slot and each occupancy metric of each time slot.

28. The non -transitory computer readable medium of clause 27, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: utilizing the occupancy metric associated with the area in each time slot as a predictive occupancy status of the area; and generating the occupancy schedule for the area based at least in part on the predictive occupancy status.

29. The non -transitory computer readable medium of any of clauses 21, to 28 where the at least one presence sensing device includes at least one of: a security camera, an infrared presence detector, a door sensor, a window sensor, a smart light switch, a Wi-Fi router, a radio-frequency identification (RFID) reader, a smart lock, vibration sensing, pressure sensing, ultrasound,

LiDAR, radar, or local setpoint adjustment. 30. The non-transitory computer readable medium of any of clauses 21 to 29, further including software instructions that, when executed, are configured to cause the at least one processor to perform steps to: utilizing an occupancy status prediction machine learning model to predict a predictive occupancy status associated with each time slot based at least in part on: a regression layer including a plurality of learned regression weight trained to correlate the occupancy metric to an occupancy status prediction based on the history of occupancy metrics, and the occupancy metric associated with the area in each time slot.

[0172] Publications cited throughout this document are hereby incorporated by reference in their entirety. While one or more embodiments of the present disclosure have been described, it is understood that these embodiments are illustrative only, and not restrictive, and that many modifications may become apparent to those of ordinary skill in the art, including that various embodiments of the inventive methodologies, the illustrative systems and platforms, and the illustrative devices described herein can be utilized in any combination with each other. Further still, the various steps may be carried out in any desired order (and any desired steps may be added and/or any desired steps may be eliminated).