System and method for personalized indoor climate control using artificial intelligence

Abstract

A method (500) and a system (102) for personalized indoor climate control are disclosed. The method (500) includes receiving, at an artificial intelligence (AI) engine (106), environmental signal data from sensors (104a, 104b.104n) disposed within an indoor environment and occupant signal data from user-authorized sources. The environmental signal data indicates indoor environmental parameters. The occupant signal data includes occupancy status, activity context, and physiological indicators. The method (500) includes generating an indoor environment state model from the environmental and occupant signal data. The method (500) includes determining personalized environment targets based on the generated model by applying adaptive comfort models and physiological outcome models trained on historical occupant responses. The method (500) includes determining operating parameters for environmental control devices based on the personalized environment targets and transmitting control commands to the environmental control devices.

Description

12369W0006SYSTEM AND METHOD FOR PERSONALIZED INDOOR CLIMATE CONTROL USING ARTIFICIAL INTELLIGENCEFIELD OF INVENTION

[0001] The present disclosure relates to intelligent indoor environment control systems, and more particularly to a method and a system for personalized indoor climate control using artificial intelligence (Al).BACKGROUND

[0001] The information in this section merely provides background information related to the present disclosure and may not constitute prior art(s) for the present disclosure.

[0002] Indoor climate control systems have evolved from simple thermostatic devices to more sophisticated systems capable of regulating multiple environmental parameters. Traditional heating, ventilation, and air conditioning systems typically operate based on fixed setpoint temperatures configured by users, with limited consideration for other environmental factors such as humidity, air quality, and airflow characteristics. Further, the operation of existing systems at fixed setpoint temperatures leads to energy wastage by cooling or heating spaces unnecessarily, such as when no occupants are present or when comfort conditions have already been achieved.

[0003] Conventional climate control approaches generally rely on manual user input to establish desired temperature settings. The users are expected to determine and configure appropriate setpoints based on subjective comfort preferences. Such systems typically lack the capability to automatically adapt to changing environmental conditions or to account for variations in individual comfort requirements across different occupants, activities, or usage states.

[0004] The indoor environment includes multiple interrelated parameters beyond temperature alone. Humidity levels, air quality indicators including carbon dioxide concentration, volatile organic compounds, and particulate matter, as well as airflow patterns, collectively influence occupant comfort and well-being. Existing systems often address these parameters in isolation through separate devices such as standalone humidifiers, dehumidifiers, air purifiers, and fans, without coordinated control across the various environmental dimensions.12369W0006

[0005] Human comfort and physiological responses to indoor environments vary based on numerous factors including individual physiology, activity level, time of day, and sleep state. Different occupants within the same space can have varying preferences and sensitivities to environmental conditions. Furthermore, the same individual may have different environmental requirements during different activities or at different times throughout the day.

[0006] Further, existing systems do not adjust temperature dynamically during the sleep of the user, which can lead to discomfort and disturbed sleep.

[0007] Therefore, there is a need for an alternative solution that may overcome above discussed limitations.

[0008] The drawbacks / difficulties / disadvantages / limitations of the conventional techniques explained in the background section are just for exemplary purposes, and the disclosure would never limit its scope only to such limitations. A person skilled in the art would understand that this disclosure and the mentioned description may also solve other problems or overcome other drawbacks / disadvantages.SUMMARY OF INVENTION

[0009] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0010] According to an aspect of the present disclosure, a method for personalized indoor climate control is provided. The method includes receiving, at an artificial intelligence (Al) engine, environmental signal data from a plurality of sensors disposed within an indoor environment and occupant signal data from a plurality of user-authorized sources. The environmental signal data indicates a plurality of indoor environmental parameters. The occupant signal data includes one or more of an occupancy status, activity context, and a plurality of physiological indicators obtained from the plurality of user-authorized sources. The method further includes generating, by the artificial intelligence engine, an indoor environment state model from the environmental signal data and the occupant signal data. The method includes determining, by the artificial intelligence engine, a plurality of personalized environment targets based on the generated indoor environment state model by applying one or more adaptive comfort models and one or more physiological outcome models trained on a plurality of historical occupant responses. The method includes determining, by the artificial intelligence engine, one or more12369W0006operating parameters for a plurality of environmental control devices based on the determined plurality of personalized environment targets. The method includes transmitting, by the artificial intelligence engine, a plurality of control commands to the plurality of environmental control devices to enable adjustment of the one or more operating parameters of the plurality of environmental control devices based on the determined one or more operating parameters.

[0011] According to another aspect of the present disclosure, a system for personalized indoor climate control is provided. The system includes a memory and at least one processor operatively coupled with the memory. The at least one processor is configured to receive, at an artificial intelligence (Al) engine, environmental signal data from a plurality of sensors disposed within an indoor environment and occupant signal data from a plurality of user-authorized sources. The environmental signal data indicates a plurality of indoor environmental parameters. The occupant signal data includes one or more of an occupancy status, activity context, and a plurality of physiological indicators obtained from the plurality of user-authorized sources. The at least one processor is configured to generate, using the artificial intelligence engine, an indoor environment state model from the environmental signal data and the occupant signal data. The at least one processor is configured to determine, using the artificial intelligence engine, a plurality of personalized environment targets based on the generated indoor environment state model by applying one or more adaptive comfort models and one or more physiological outcome models trained on a plurality of historical occupant responses. The at least one processor is configured to determine, using the artificial intelligence engine, one or more operating parameters for a plurality of environmental control devices based on the determined personalized environment targets. The at least one processor is configured to transmit a plurality of control commands to the plurality of environmental control devices to enable adjustment of the one or more operating parameters of the plurality of environmental control devices based on the determined one or more operating parameters.

[0012] In an advantageous effect, the present disclosure enables autonomous climate control that adapts to individual occupant characteristics without requiring manual setpoint configuration. The integration of environmental signal data with occupant signal data including physiological indicators, allows the system to account for both objective environmental conditions and subjective human factors when determining climate control actions. The use of adaptive comfort models and physiological outcome models trained on historical occupant responses enables the system to learn and refine its control decisions12369W0006over time, resulting in improved personalization accuracy and reduced energy consumption through more targeted environmental adjustments.

[0013] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.BRIEF DESCRIPTION OF FIGURES

[0014] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0015] FIG. 1 illustrates an environment for implementing a system for personalized indoor climate control, in accordance with an embodiment of the present disclosure;

[0016] FIG. 2 illustrates a schematic block diagram depicting the system for the personalized indoor climate control, in accordance with an embodiment of the present disclosure;

[0017] FIG. 3 illustrates an exemplary use case scenario for implementing the system within a real-world indoor environment, in accordance with an embodiment of the present disclosure;

[0018] FIGS. 4A-4B illustrates a schematic flow diagram depicting various layers within an architecture for the system, in accordance with an embodiment of the present disclosure; and

[0019] FIG. 5 illustrates a flowchart depicting a method for the personalized indoor climate control, in accordance with an embodiment of the present disclosure.

[0020] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help improve understanding of aspects of the present invention.

[0021] Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.DETAILED DESCRIPTION OF INVENTION

[0022] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the various embodiments, and specific language12369W0006will be used to describe the same. It should be understood at the outset that although illustrative implementations of the embodiments of the present disclosure are illustrated below, the present invention may be implemented using any number of techniques, whether currently known or in existence. The present disclosure is not necessarily limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified within the scope of the present disclosure.

[0023] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.

[0024] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0025] It is to be understood that as used herein, terms such as, “includes,” “comprises,” “has,” etc. are intended to mean that the one or more features or elements listed are within the element being defined, but the element is not necessarily limited to the listed features and elements, and that additional features and elements may be within the meaning of the element being defined. In contrast, terms such as “consisting of’ are intended to exclude features and elements that have not been listed.

[0026] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term “or” as used herein, refers to a non-exclusive or unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.12369W0006

[0027] As is traditional in the field, embodiments may be described and illustrated in terms of blocks that carry out a described function or functions. These blocks, which may be referred to herein as units or modules or the like, are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the invention. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the invention.

[0028] The accompanying drawings are used to help easily understand various technical features, and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents, and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.

[0029] FIG. 1 illustrates an environment 100 for implementing a system 102 for personalized indoor climate control, in accordance with an embodiment of the present disclosure. In an embodiment, the environment 100 refers to an overall operational ecosystem within which the system 102 operates. The environment 100 includes a coordinated set of hardware components, communication networks, and data-processing resources that enable sensing, analysis, and control of indoor environmental conditions.

[0030] In an embodiment, the system 102 may alternatively be termed the intelligent indoor environment control system 102 within the scope of the present disclosure. In an embodiment, the system 102 may be hosted on a cloud server 110. In another embodiment, the system 102 may be implemented by User Equipment (UE). In a non-limiting example,12369W0006the UE may be a smartphone, a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a tablet, or a smartwatch. In yet another embodiment, the system 102 may be implemented by a combination of the UE and the cloud server 110. More specifically, one or more steps may be performed in the UE, and the remaining steps may be performed by the cloud server 110.

[0031] In an illustrated embodiment, the cloud server 110 may communicate with a network 112, which may be any suitable communication network enabling data exchange between components of the environment 100. The network 112 may include wired networks, wireless networks, local area networks, wide area networks, the Internet, or combinations thereof.

[0032] In an embodiment, the environment 100 may include a plurality of sensors 104a, 104b...104n that may be disposed within an indoor environment and may be connected to the network 112. The indoor environment refers to a physical space within which the system 102 operates. The sensors 104a, 104b...104n may be configured to capture various indoor environmental parameters and transmit environmental signal data through the network 112 to the cloud server 110. The sensors 104a, 104b...104n may include, but are not limited to, temperature sensors, humidity sensors, air quality sensors measuring carbon dioxide, volatile organic compounds, and particulate matter, as well as occupancy and presence sensors. The sensors 104a, 104b...l04n may be distributed within one or more indoor zones to establish the physical state of the indoor environment.

[0033] In an embodiment, the environment 100 may include a plurality of environmental control devices 108a, 108b ...108n that may also be connected to the network 112. The environmental control devices 108a, 108b...108n receive control commands from the cloud server 110 through the network 112 and actuate to modify the indoor environment accordingly. The environmental control devices 108a, 108b...108n may include air conditioners, heat pumps, smart fans, airflow devices, humidifiers, dehumidifiers, air purifiers, and ventilation systems. The environmental control devices 108a, 108b...108n may operate simultaneously or sequentially under orchestration by the system 102.

[0034] In an embodiment, the system 102 may include an artificial intelligence (Al) engine 106 configured to process data and generate control decisions for personalized indoor climate control.

[0035] In an embodiment, the Al engine 106 may process data received from the sensors 104a, 104b...104n. Further, the Al engine 106 may analyze the environmental12369W0006conditions and generate control decisions. In an embodiment, the control decisions may be transmitted through the network 112 to the appropriate environmental control devices 108a, 108b...l08n to achieve desired indoor climate conditions. In some embodiments, the Al engine 106 may execute on-device. In some embodiments, the Al engine 106 may execute in the cloud server 110. In some embodiments, the Al engine 106 may execute in a hybrid edge-plus-cloud configuration supporting distributed execution across multiple deployment models.

[0036] In an embodiment, the system 102 may include a device abstraction layer and orchestration layer that works across heterogeneous climate appliances of different types, brands, and protocols. The device abstraction and orchestration layer translates high-level climate intent into device-specific actions for the environmental control devices 108a, 108b...l08n. The system 102 communicates with the environmental control devices 108a, 108b...l08n via infrared transmission modules, wireless communication modules including Wireless Fidelity (Wi-Fi), Bluetooth (BLE), Zigbee, and Thread, and smart home interoperability standards such as Matter.

[0037] In an embodiment, the environment 100 may operate in a closed-loop configuration where the sensors 104a, 104b...104n may continuously or periodically monitor the indoor environment. The Al engine 106 may process the environmental signal data to determine control actions, and the environmental control devices 108a, 108b...108n execute the commands to modify temperature, humidity, airflow, and air quality. In an embodiment, feedback from the sensors 104a, 104b...104n may allow the system 102 to evaluate the effectiveness of the control actions and refine future decisions through adaptive learning.

[0038] Now, hardware details of the system 102 are discussed and explained in detail in conjunction with FIG. 2.

[0039] FIG. 2 illustrates a schematic block diagram depicting the system 102 for the personalized indoor climate control, in accordance with an embodiment of the present disclosure.

[0040] In an embodiment, the system 102 may include a memory 204 including a database 212, a processor 202 communicatively coupled with the memory 204, an Input / Output (VO) interface 208, and a plurality of modules 210. In an embodiment, the said components may be in communication with each other.

[0041] In one embodiment, the memory 204 is configured to store instructions executable by the processor 202. In one embodiment, the memory 204 communicates via12369W0006a bus within the system 102. The memory 204 includes, but is not limited to, a non-transitory computer-readable storage media, such as various types of volatile and nonvolatile storage media including, but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media, and the like. In one example, the memory includes a cache or random-access memory (RAM) for the processor 202. In alternative examples, the memory 204 is separate from the processor 202, such as a cache memory of a processor, the system memory, or other memory. The memory 204 is an external storage device, or the database 212 is for storing data. The memory 204 is operable to store instructions executable by the processor 202. The functions, acts, or tasks illustrated in the figures or described are performed by the programmed processor for executing the instructions stored in the memory 204. The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies include multiprocessing, multitasking, parallel processing, and the like.

[0042] As a non-limiting example, the processor 202 may be a single processing unit or a set of units, each including multiple computing units. The processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions (computer-readable instructions) stored in the memory 204. Among other capabilities, the processor 202 may be configured to fetch and execute computer-readable instructions and data stored in the memory 204. The processor 202 includes one or a plurality of processors. The plurality of processors is further implemented as a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit, such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an Al-dedicated processor such as a neural processing unit (NPU). The plurality of processors controls the processing of the input data in accordance with a predefined operating rule or an artificial intelligence (Al) model stored in the memory 204. The predefined operating rule or the Al model is provided through training or learning.

[0043] The processor 202 may be disposed in communication with one or more input / output (I / O) devices via the I / O interface 208. The I / O interface 208 employs12369W0006communication code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, and the like, etc. In another embodiment of the present invention, the I / O interface 208 employs ethernet, Industrial Wireless Local Area Network (LAN), Process Field Bus (PROFIBUS), actuator sensor (AS) Interface, and the like.

[0044] The plurality of modules 210 may include a data receiving module 216, an indoor environment state model generating module 218, a personalized environment target determining module 220, an operational parameter determining module 222, a control command transmitting module 224, a sleep pattern detecting module 226, a room condition updating module 228, and a future environmental condition predicting module 230. In an embodiment, the data receiving module 216, the indoor environment state model generating module 218, the personalized environment target determining module 220, the operational parameter determining module 222, the control command transmitting module 224, the sleep pattern detecting module 226, the room condition updating module 228, and the future environmental condition predicting module 230 may be in communication with each other. The working of the plurality of modules 210 is explained in detail in the forthcoming paragraphs.

[0045] In an embodiment, the data receiving module 216 may receive environmental signal data from the plurality of sensors 104a, 104b...104n. Further, the data receiving module 216 may receive occupant signal data from a plurality of user-authorized sources. The environmental signal data may indicate a plurality of indoor environmental parameters including temperature, humidity, air quality measurements such as carbon dioxide, volatile organic compounds, and particulate matter. The occupant signal data may include one or more of an occupancy status, activity context, and a plurality of physiological indicators obtained from the plurality of user-authorized sources. The plurality of physiological indicators may include heart rate, sleep stage, skin temperature, and respiratory signals. In an embodiment, the plurality of user-authorized sources may include, but are not limited to, wearable devices, mobile applications, and smart home hubs. In an embodiment, one or more contextual cues may be detected based on the received environmental signal data and the occupant signal data. The one or more contextual cues may include one or more of sleep onset, occupancy, and one or more learned routines.

[0046] Further, the indoor environment state model generating module 218 may generate an indoor environment state model from the environmental signal data and the occupant signal data received by the data receiving module 216. The indoor environment12369W0006state model generating module 218 may perform data normalization, filtering, and validation of heterogeneous sensor inputs received from the plurality of sensors 104a, 104b...104n and the plurality of user-authorized sources. In an embodiment, the indoor environment state model generating module 218 may align time-series data across sources to construct the indoor environment state model. In an embodiment, the indoor environment state model represents both objective environmental conditions and subjective human context within the indoor environment.

[0047] Further, the personalized environment target determining module 220 may determine a plurality of personalized environment targets based on the generated indoor environment state model. In an embodiment, the personalized environment target determining module 220 may apply one or more adaptive comfort models and one or more physiological outcome models trained on a plurality of historical occupant responses to compute the plurality of personalized environment targets. The plurality of personalized environment targets may include, but are not limited to, target temperature ranges, target humidity levels, and target air quality thresholds specific to an individual occupant. The personalized environment target determining module 220 may infer individual sensitivity to temperature, humidity, airflow, and air quality based on the plurality of historical occupant responses. In an embodiment, the Al engine 106 may include constraint and safety layers that ensure the plurality of personalized environment targets comply with health, performance, and safety requirements.

[0048] Further, the operating parameter determining module 222 may determine one or more operating parameters for the plurality of environmental control devices 108a, 108b...l08n based on the determined plurality of personalized environment targets. The operating parameter determining module 222 may include appliance capability and constraint models that define operational limits and capabilities of each environmental control device for orchestration purposes. The operating parameter determining module 222 may allocate control actions across multiple appliance types (the environmental control devices 108a, 108b...l08n) and optimize control decisions based on energy efficiency, comfort stability, transition smoothness, noise constraints, and appliance operational limits.

[0049] In an embodiment, the control command transmitting module 224 may be configured to transmit a plurality of control commands to the environmental control devices 108a, 108b...108n. These commands may allow adjustment of one or more operating parameters of the environmental control devices based on the determined12369W0006operating parameters. In an embodiment, the control command transmitting module 224 may translate environment-level control strategies into appliance-specific commands and transmit the plurality of control commands via one or more communication interfaces, including infrared, Wi-Fi, BLE, and matter protocols.

[0050] In an embodiment, the sleep pattern detecting module 226 may monitor and identify occupant sleep states based on the received environmental signal data and the occupant signal data to adjust environmental conditions accordingly during rest periods. In an embodiment, the room condition updating module 228 may track changes in environmental parameters following actuation of the plurality of environmental control devices 108a, 108b...l08n. In an embodiment, the future environmental condition predicting module 230 may anticipate upcoming environmental states based on current conditions, external factors, and historical patterns to enable proactive climate control.

[0051] FIG. 3 illustrates an exemplary use case scenario for implementing the system 102 within a real -world indoor environment 300, in accordance with an embodiment of the present disclosure. In an embodiment, the system 102 may coordinate sensing, processing, communication, and air-conditioning control through an integrated hardware and software architecture. In an illustrated embodiment, a dock device 302 functioning as a central controller of the system 102. In an embodiment, the dock device 302 may include a Dock Printed Circuit Board (DOC PCB) that may be equipped with the plurality of sensors 104a, 104b...l04n including the temperature sensors, relative and absolute humidity sensors, and air-quality sensors. The plurality of sensors 104a, 104b...l04n are capable of measuring carbon dioxide (CO2), volatile organic compounds (VOCs), and particulate matter such as PM1, PM2.5, and PM10, with optional sensors for airflow velocity, ambient light, and noise levels, all of which are used to continuously characterize the indoor environment.

[0052] In an embodiment, the dock device 302 may communicate wirelessly with an air conditioner 308 to transmit appliance-specific commands via an infrared (IR) interface to regulate temperature, humidity, and airflow according to decisions generated by the Al engine 106.

[0053] In an embodiment, user interaction with the system 102 is supported through three control options including touch buttons on the dock device, a mobile application 312, and a remote control device 306, thereby ensuring user accessibility even when the system 102 operates autonomously.

[0054] The remote control device 304 may include a remote PCB, including a BLE controller powered by AAA cells. The remote device 304 supports one-to-one pairing12369W0006between the Bluetooth remote and individual dock units, with re-pairing required if the remote device 304 is changed. In an embodiment, the remote device 304 may include a Light Emitting Diode (LED) adapted to provide visual feedback for various events including switch press events, fault indications, and pairing status

[0055] In an embodiment, the mobile application 312 may provide a user interface for controlling system 102 and monitoring. In an embodiment, the mobile application 312 may communicate with the dock device 302 through the BLE when the dock device 302 is offline. In another embodiment, the mobile application 312 may communicate with the dock device 302 through the Wi-Fi when connected to the Internet, enabling cloud-based communication via an Amazon Web Services (AWS) backend for remote access and data services.

[0056] In an embodiment, the system 102 may include an Infrared-based third-party remote to facilitate connectivity to the cloud server 110, enabling the dock device 302 to exchange data with remote cloud servers for extended processing, device management, and the system 102 updates. In an embodiment, the system 102 may include an Infrared (IR) receiver. The IR receiver may be configured to receive IR signals from the user-operated external remotes to detect manual overrides or mid-cycle user interventions. Further, the system 102 may understand user intent during the learning phase and reduce friction by avoiding a new control interface. The system 102 may be configured to enable seamless transitions between manual control and autonomous operation, especially during early user trust-building.

[0057] In an embodiment, the system 102 may support user-operated external remotes (for example Air conditioner remotes or fan remotes) and optional user input devices without committing to shipping a proprietary remote.

[0058] In the illustrated configuration, the system 102 may leverage environmental signal data collected at the dock device 302, integrate it with occupant-related information when available, and autonomously determine personalized environment targets and operating parameters for the air conditioner, with communication occurring across a combination of IR, BLE, and Wi-Fi links to support seamless, context-aware climate management within the environment.

[0059] FIGS. 4A-4B illustrates a schematic flow diagram depicting various layers within an architecture for the system 102, in accordance with an embodiment of the present disclosure. In an embodiment, a data collection layer 450 may aggregate data from multiple12369W0006heterogeneous sources to enable personalized indoor environment control. The data processing layer 460 may receive aggregated data from the data collection layer 450 and perform a series of processing operations to transform raw multi-source data into structured, analyzed information suitable for personalized indoor environment control decisions.

[0060] In an embodiment, the data collection layer 450 may include a main dock sensor 402 that may capture environmental parameters from the dock device 302. The main dock sensor 402 may continuously or periodically capture environmental parameters, including temperature, humidity, and air quality measurements. The remote sensor 404 may provide environmental measurements from distributed locations within the indoor environment. The main dock sensor 402 and the remote sensor 404 may be distributed within one or more indoor zones, with sensor data timestamped and spatially associated with the one or more indoor zones to establish the physical state of the environment.

[0061] In an embodiment, the data collection layer 450 may include a weather application programming interface (API) 406 may supply external environmental conditions to the data collection layer 450. The weather API 406 may provide external environmental inputs, including outdoor temperature, outdoor humidity, and air quality index, that the system 102 incorporates in determining personalized environment targets. In an embodiment, the data collection layer 450 may include a user feedback 408 may capture explicit occupant inputs and preferences. Further, the data collection layer 450 may include user data 410 that may include occupant profiles and historical information. Furthermore, the data collection layer 450 may include room data 412 that contains spatial and configuration information about the indoor environment. Moreover, the data collection layer may include an AC data 414 that may provide operational parameters and status information from air conditioning equipment.

[0062] In an embodiment, a data processing layer 460 may include a data cleaning and normalization block 416 that may receive raw data inputs from the data collection layer 450. The data cleaning and normalization block 416 may perform filtering, validation, and standardization operations to help maintain data quality and consistency across heterogeneous sources. The data cleaning and normalization block 416 may also resolve inconsistencies and may filter sensor noise from the environmental signal data and the occupant signal data.

[0063] Further, the data processing layer 460 may include a feature engineering block 418 may receive cleaned and normalized data from the data cleaning and normalization12369W0006block 416. The feature engineering block 418 may extract and construct relevant features from the processed data for subsequent analysis and model input, the data processing layer 460 may include a time series analysis block 420 that may receive the engineered features from the feature engineering block 418 and may perform temporal analysis operations to identify patterns, trends, and correlations within time-sequenced data streams. The time series analysis block 420 may enable the system 102 to determine circadian phase and time-of-day context that may influence personalized environment target computation.

[0064] Referring to FIG. 4B, the architecture of the system 100 may include an Al model layer 470 and a control layer 480. The Al model layer 470 may receive input from the data processing layer 460 and may include multiple specialized Al modules that may be configured to process different aspects of occupant and environmental data to generate personalized climate control decisions. The system 102 may be designed to operate autonomously with minimal or no user input, and may automatically adjust environmental conditions based on the one or more contextual cues, such as sleep onset, occupancy, and learned routines, without requiring users to manually set temperature or humidity targets.

[0065] In an embodiment, the Al model layer 470 may include a personal profile Al 422 that may process the individual occupant profiles and preferences. Further, the Al model layer 470 may include a room profile Al 424 that may analyze room characteristics and spatial configurations. Furthermore, the Al model layer 470 may include a sleep Al 426 that may monitor and identify occupant sleep states to adjust environmental conditions during rest periods, targeting biological outcomes such as sleep quality and recovery rather than traditional comfort-based optimization. Moreover, the Further, the Al model layer 470 may include a couple Al 428 that may handle multi-occupant environments by processing multiple physiological and contextual inputs and may resolve or balance differing comfort or health needs among occupants. The Al model layer 470 may also include a care Al 430 may prioritize health-first optimization beyond comfort, targeting biological outcomes, including respiratory well-being.

[0066] In an illustrated embodiment, the control layer 480 may include an AC control interface 432 that may receive outputs from the Al model layer 470. The AC control interface 432 may translate high-level climate control decisions from the Al model layer 470 into appliance-specific commands for the plurality of environmental control devices 108a, 108b...l08n. A user interface 434 may provide interaction capabilities for system users. The user interface 434 may connect to the mobile application 312, the remote device12369W0006304, and the dock device 302 to enable flexible control and monitoring of the climate control system through multiple interface options.

[0067] FIG. 5 illustrates a flowchart depicting a method 500 for the personalized indoor climate control, in accordance with an embodiment of the present disclosure. In an embodiment, the method 500 is a computer-implemented method that includes steps 502-510 executed by the system 102. The method 500 may be performed by the system 102 in conjunction with the modules 210, the details of which are explained in conjunction with FIGS. 1 to 4B, and the same are not repeated here for the sake of brevity in the present disclosure.

[0068] The method 500 may integrate multi-dimensional environmental sensing with occupant-specific data to generate personalized climate control decisions through the artificial intelligence engine 106. The method 500 may employ adaptive comfort models that may learn from historical occupant responses to determine environment targets without requiring explicit user input.

[0069] In an embodiment, the method 500 may begin at step 502 of receiving the environmental signal data and the occupant signal data. At step 502, the Al engine 106 may receive the environmental signal data from the plurality of sensors 104a, 104b...104n disposed within the indoor environment and the occupant signal data from the plurality of user-authorized sources.

[0070] At step 504, the method 500 may include generating an indoor environment state model. At step 504, the Al engine 106 may generate the indoor environment state model from the environmental signal data and the occupant signal data.

[0071] Further, at step 506, the method 500 may include determining personalized environment targets. At step 506, the Al engine 106 may determine the plurality of personalized environment targets based on the generated indoor environment state model by applying one or more adaptive comfort models and the one or more physiological outcome models trained on the historical occupant responses.

[0072] Further, at step 508, the method 500 may include determining operating parameters. At step 508, the Al engine 106 may determine one or more operating parameters for the environmental control devices 108a, 108b...108n based on the determined personalized environment targets. The operating parameter determining module 222 may also allocate the control actions across multiple appliance types (the environmental control devices 108a, 108b...108n), which are discussed above in the description.12369W0006

[0073] Furthermore, at step 510, the method 500 may include transmitting the control commands. At step 510, the Al engine 106 may transmit the plurality of control commands to the environmental control devices 108a, 108b...l08n to enable adjustment of the operating parameters of the environmental control devices 108a, 108b...108n based on the determined operating parameters.

[0074] In an embodiment, the method 500 may employ reinforcement learning to reinforce successful control strategies and adjust future decisions without requiring explicit user input. Following transmission of the control commands, the system 102 may monitor changes in environmental parameters and may observe occupant physiological and behavioral responses. The system 102 may evaluate deviations between intended targets and achieved conditions to update user-specific environment models and preference weights. This may establish a closed-loop, self-improving indoor environment control process that may refine subsequent computation of the personalized environment targets based on observed outcomes.

[0075] In another aspect of the present disclosure, an apparatus for the for personalized indoor climate control is disclosed. In an embodiment, the apparatus may include the plurality of sensors 104a, 104b...104n. Further, the apparatus may include the plurality of environmental control devices 108a, 108b...108n that may be in communication with the plurality of sensors 104a, 104b...104n. The apparatus may include the memory 204, the processor 202 coupled to the memory 204. The processor 202 may be in communication with the plurality of sensors 104a, 104b...104n and the plurality of environmental control devices 108a, 108b...108n. The memory 102 may include programmable instructions which, when executed by the processor 104, cause the processor 104 to perform the steps of the present disclosure discussed and explained in conjunction with FIGS. 1-5.

[0076] In various embodiments, the present disclosure may be embedded as firmware within an Original Equipment Manufacturer (OEM) hardware to perform the steps of the present disclosure discussed and explained in conjunction with FIGS. 1-5.

[0077] Now, the advantages of the present disclosure are discussed in the forthcoming paragraphs.

[0078] The system 102 for personalized indoor climate control provides autonomous operation through the Al engine 106 without requiring manual user input for temperature or humidity settings. The Al engine 106 processes environmental signal data and occupant signal data to determine personalized environment targets and the operating parameters12369W0006automatically based on contextual cues, including sleep onset, occupancy status, and learned routines.

[0079] The plurality of sensors 104a, 104b...104n and the plurality of environmental control devices 108a, 108b...108n work together to achieve coordinated climate control across temperature, humidity, airflow, and air quality dimensions. The system 102 orchestrates multiple environmental control devices 108a, 108b...108n simultaneously or sequentially to achieve the plurality of personalized environment targets while optimizing for energy efficiency and comfort stability.

[0080] The device abstraction layer within the system 102 enables operation across heterogeneous climate appliances of different types, brands, and communication protocols. The device abstraction layer translates high-level climate intent into device-specific actions, allowing the system 102 to control environmental control devices 108a, 108b...l08n from various manufacturers without requiring protocol-specific user configuration.

[0081] The system 102 employs adaptive learning that refines control strategies over time based on observed outcomes and occupant responses. The system 102 monitors postactuation physiological responses of occupants to evaluate the effectiveness of control actions and updates user-specific environment models accordingly. The system 102 reinforces successful control strategies and adjusts future decisions through closed-loop feedback without requiring explicit user intervention.

[0082] The system 102 builds persistent user-specific climate profiles over time that can be reused across different rooms or locations. The user-specific climate profiles capture individual sensitivities to temperature, humidity, airflow, and air quality, enabling consistent personalized climate control when occupants move between indoor environments.

[0083] The system 102 accounts for sensor reliability issues, including sensor drift, contamination such as dust, and degradation over time. A sensor compensation may occur via model re-weighting, confidence scoring, or redundance across the plurality of sensors 104a, 104b...104n purely at a high level. The Al engine 106 includes model adjustments to compensate for these sensor reliability factors and maintain accurate environmental sensing. The system 102 also accounts for appliance performance changes over time and adjusts models to compensate for degradation in environmental control device performance.12369W0006

[0084] The system 102 may include a local edge controller or a cloud-based processor for data fusion and environment state construction, supporting distributed execution across multiple deployment configurations, including on-device, cloud-based, or hybrid edge-plus-cloud architectures.

[0085] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the elements. The elements can be at least one of a hardware device or a combination of hardware devices and software modules.

[0086] It is understood that terms including “unit” or “module” at the end may refer to the unit for processing at least one function or operation and may be implemented in hardware, software, or a combination of hardware and software.

[0087] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0088] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.

[0089] Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

[0090] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.12369W0006

[0091] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of at least one embodiment, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

Claims

12369W0006WE CLAIM:

1. A method (500) for personalized indoor climate control, the method (500) comprising:receiving, at an artificial intelligence (Al) engine (106), environmental signal data from a plurality of sensors (104a, 104b...104n) disposed within an indoor environment and occupant signal data from a plurality of user-authorized sources, wherein the environmental signal data indicates a plurality of indoor environmental parameters, wherein the occupant signal data comprises one or more of an occupancy status, activity context, and a plurality of physiological indicators obtained from the plurality of user-authorized sources;generating, by the Al engine (106), an indoor environment state model from the environmental signal data and the occupant signal data;determining, by the Al engine (106), a plurality of personalized environment targets based on the generated indoor environment state model by applying one or more adaptive comfort models and one or more physiological outcome models trained on a plurality of historical occupant responses;determining, by the Al engine (106), one or more operating parameters for a plurality of environmental control devices (108a, 108b...108n) based on the determined plurality of personalized environment targets; andtransmitting, by the Al engine (106), a plurality of control commands to the plurality of environmental control devices (108a, 108b...l08n) to enable adjustment of the one or more operating parameters of the plurality of environmental control devices (108a, 108b...l08n) based on the determined one or more operating parameters.

2. The method (500) as claimed in claim 1, comprising:receiving feedback signal data from one or more of the plurality of sensors (104a, 104b...104n) and the plurality of environmental control devices (108a, 108b...108n) based on the transmitted plurality of control commands, wherein the feedback signal data indicates one or more changes in the plurality of indoor environmental parameters; andupdating, based on the feedback signal data, one or more user-specific environment models to refine subsequent computation of the personalized environment targets, wherein the one or more user-specific environment models12369W0006comprise one or more user climate profiles that are reusable across one or more locations.

3. The method (500) as claimed in claim 1, wherein the plurality of physiological indicators comprises at least one of heart rate, sleep stage, skin temperature, and respiratory signals received from the plurality of user-authorized sources.

4. The method (500) as claimed in claim 1, comprising:detecting one or more contextual cues based on the received environmental signal data and the occupant signal data, wherein the one or more contextual cues comprise one or more of sleep onset, occupancy, and one or more learned routines.

5. The method (500) as claimed in claim 1, wherein the plurality of user-authorized sources comprises one or more of a plurality of wearable devices, a plurality of mobile applications, and a plurality of smart home hubs.

6. The method (500) as claimed in claim 1, wherein the plurality of personalized environment targets comprises one or more of a target temperature range, a target humidity level, and a target air quality threshold specific to an individual occupant.

7. The method (500) as claimed in claim 1, further comprising:detecting, by the Al engine (106), a plurality of sleep patterns indicative of sleep stages based on the received environmental signal data and the occupant signal data; andupdating, by the Al engine (106), one or more room conditions by transmitting the plurality of control commands to the plurality of environmental control devices (108a, 108b...108n).

8. The method (500) as claimed in claim 7, wherein updating the one or more room conditions comprises:executing, by the Al engine (106), one or more temperature adjustments throughout a sleep duration by transmitting the plurality of control commands to the plurality of environmental control devices (108a, 108b...108n) to produce corresponding temperature changes in the indoor environment; and12369W0006updating, by the Al engine (106), the one or more room conditions by transmitting the plurality of control commands to the plurality of environmental control devices (108a, 108b...108n).

9. The method (500) as claimed in claim 1, wherein determining the one or more operating parameters comprises:determining, by the Al engine (106), the one or more operating parameters across the plurality of environmental control devices (108a, 108b...108n) using one or more energy consumption constraints, one or more thermal response characteristics of the plurality of environmental control devices (108a, 108b...108n), and one or more appliance operational limits based on the determined personalized environment targets.

10. The method (500) as claimed in claim 1, comprising:predicting, by the Al engine (106), one or more future environmental conditions using external weather data and a plurality of historical indoor environmental patterns based on the determined personalized environment targets.

11. The method (500) as claimed in claim 1, wherein the operating parameters of the plurality of environmental control devices (108a, 108b...l08n) comprise one or more of temperature, humidity, airflow, and air quality within the indoor environment.

12. A system (102) for personalized indoor climate control, the system (102) comprising:a memory (204);at least one processor (202) is operatively coupled with the memory (204), wherein the at least one processor (202) is configured to:receive, at an artificial intelligence (Al) engine (106), environmental signal data from a plurality of sensors (104a, 104b...104n) disposed within an indoor environment and occupant signal data from a plurality of user- authorized sources, wherein the environmental signal data indicates a plurality of indoor environmental parameters, wherein the occupant signal data comprises one or more of an occupancy status, activity context, and a plurality of physiological indicators obtained from the plurality of user-authorized sources;12369W0006generate, using the Al engine (106), an indoor environment state model from the environmental signal data and the occupant signal data;determine, using the Al engine (106), a plurality of personalized environment targets based on the generated indoor environment state model by applying one or more adaptive comfort models and one or more physiological outcome models trained on a plurality of historical occupant responses;determine, using the Al engine (106), one or more operating parameters for a plurality of environmental control devices (108a, 108b...108n) based on the determined personalized environment targets; and transmit a plurality of control commands to the plurality of environmental control devices (108a, 108b...108n) to enable adjustment of the one or more operating parameters of the plurality of environmental control devices (108a, 108b...l08n) based on the determined one or more operating parameters.

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