TECHNICAL FIELD
[001] The present disclosure relates to smart homes. More particularly, the present
disclosure relates to climate control of smart homes.
BACKGROUND
[002] Background description includes information that can be useful in
understanding the present invention. It is not an admission that any of the information
provided herein is prior art or relevant to the presently claimed invention, or that any
publication specifically or implicitly referenced is prior art.
[003] Smart homes have become a reality, and it can be said with some degree of
certainty that smart homes will be ubiquitous in the future. Smart homes can be those where
conditions of the home such as climate control, lighting, security etc., can be triggered by any
set of inputs such as presence of a mobile device belonging to a first user at a preferred
vicinity of the home, facial tracking and other similar inputs that does not require any
additional effort from the first user. A smart home offering customisability of function for a
first user based on the detection of the first user is an aspiration for developers of smart
homes. However, such abilities often require sophisticated sensors and computing apparatus
that can be very expensive.
[004] A common problem arising with smart homes, or indeed, any building suitably
adapted from smart working, is the consumption of energy and the necessity to regulate the
consumption.
[005] Particularly with climate control, energy consumption is a widely noted issue.
Energy consumption of appliances such as air conditioners, fans, coolers and ventilators can
account for at least 15-20% of total energy consumption in the smart home. At present,
billing of fans and air-conditioners generally adopts a method of charging by area or
installing a heat meter. Although the area-based charging method is simple and convenient,
and the cost is low, there is still much that can be done to reduce expenditure of energy,
considering an inability of the first user, at times, to intervene to reduce the expense of
energy.
[006] Efforts to create smart homes tend, generally, to focus on the ease of use and
the ease of customisability of the appliances in the home. Saving of energy can often be

relegated to a secondary issue and limited efforts can be made to address it. Further, when the
aspect of customisability is also factored, the cost of energy increases significantly.
[007] Therefore, there is a requirement in the art for a climate control system in a
smart home or environment that can offer customisation options that are resource friendly
and further, operate bearing in mind a need to limit expenditure of energy.
[008] All publications herein are incorporated by reference to the same extent as if
each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[009] In some embodiments, the numbers expressing quantities or dimensions of
items, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term "about." Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention can contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0010] As used in the description herein and throughout the claims that follow, the
meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0011] Groupings of alternative elements or embodiments of the invention disclosed
herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion

occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.
OBJECTS OF THE PRESENT DISCLOSURE
[0012] Some of the objects of the present disclosure, which at least one embodiment
herein satisfies are as listed herein below.
[0013] It is an object of the present disclosure to provide a learning system and
method for wireless thermal management in an enclosed space (such as home, office etc.) in
real-time.
[0014] It is another object of the present disclosure to provide a simple and cost
effective system and method for controlling operation of fans, air conditioners etc.
automatically.
[0015] It is another object of the present disclosure to provide a reliable and fast
system and method for wireless thermal management in an enclosed space with enhanced
sustainability.
[0016] It is another object of the present disclosure to provide a precise, accurate and
time-efficient system and method for controlling operation and speed of fans, air conditioners
etc. in an enclosed space based on user preferences.
[0017] It is another object of the present disclosure to provide a smart system and
method for controlling operation and/or speed of fans, air conditioners etc. in an enclosed
space to enhance occupants/users' comfort.
[0018] It is another object of the present disclosure to provide an energy conserving
system and method for controlling operation and speed of fans, air conditioners etc. in an
enclosed space.
SUMMARY
[0019] The present disclosure relates to smart homes. More particularly, the present
disclosure relates to climate control of smart homes.
[0020] This summary is provided to introduce simplified concepts of a system for
time bound availability check of an entity, which are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended for use in determining/limiting the scope of the claimed subject matter.

[0021] An aspect of the present disclosure pertains to a system for wireless thermal
management in an enclosed space (such as a room or any other defined physical space). The proposed system, in an aspect, can include a first device that can be provided in the enclosed space for thermal regulation, and can be operatively coupled to a gateway to operate upon receipt of a first signal. The system can further include a second device that can be provided in the enclosed space for thermal regulation, and can be operatively coupled to the gateway and operable upon receipt of a second signal. The system can further include a controller that can be operatively coupled to the gateway, and can be configured to operate the first device and the second device, wherein the system can further include a learning engine that can be operatively coupled to the controller.
[0022] In an aspect, controller of the proposed system can include one or more
processors that can be operatively coupled to a memory, said memory storing instructions, which when executed by the one or more processors, cause the controller to: receive, from one or more sensors provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space; and receive, from the gateway, a first set of data packets from a first user, said first set of data packets pertaining to desired thermal attributes for the enclosed space. Based on receipt of the first set of data packets, the controller is configured to generate the first signal. Based on any or a combination of the plurality of first parameters, the first set of data packets, and energy parameters of the second device, the controller of the present system can be configured to generate the second signal. Learning engine of the proposed system can accordingly be adapted to, over a defined time period, analyse at least one or combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to the respective first device and the second device.
[0023] In an aspect, the first device can be operated to, upon generation of the first
signal, modulate current thermal attributes of the enclosed space to the corresponding desired
thermal attributes for the enclosed space such that the second device can be selectively
operated to, upon generation of the second signal, modulate the current thermal attributes of
the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[0024] In an aspect, the learning engine can be any neural network that is configured
to learn based on historical data pertaining to the first user based on the first set of data packets received from the first user in the past across a defined time period.

[0025] In an aspect, the one or more energy parameters of the second device can be
any or a combination of energy consumption by the second device, and efficiency of the
second device.
[0026] In an aspect, the second signal can be selectively generated for the second
device such that the overall energy consumption of the second device is least.
[0027] In an aspect, the gateway can be operable by wireless means selected from a
group comprising Wi-Fi, Bluetooth, mobile connectivity, infrared, radio frequency and a
combination thereof.
[0028] In an aspect, the at least one of the controller, the IR blaster, the one or more
sensors, a communication module (such as Wi-Fi module etc.) can be integrated or
operatively coupled with the first device (For example., fan).
[0029] In an embodiment, the first device can be a fan, and the second device can be
at least one of an air conditioner, air cooler and ventilator.
[0030] In an embodiment, the one or more sensors can be at least one of a temperature
sensor, humidity sensor and photoelectric sensor, and wherein the plurality of first parameters
can be at least one of temperature, humidity and precipitation etc. parameters that can be
associated with the enclosed space.
[0031] Another aspect of the present disclosure pertains to a controller for wireless
thermal management in an enclosed space. The controller can include one or more processors
operatively coupled to a memory, the memory storing instructions executable by the one or
more processors to: receive, from one or more sensors provided in the enclosed space, a
plurality of first parameters pertaining to current thermal attributes of the enclosed space; and
receive, from a gateway, a first set of data packets from a first user, said first set of data
packets pertaining to desired thermal attributes for the enclosed space. Based on receipt of the
first set of data packets, the controller can be configured to generate a first signal. Based on
any or a combination of the first set of data packets, energy parameters of a second device,
and the plurality of first parameters, the controller can be configured to generate a second
signal for operating the second device. A learning engine can be operatively coupled to the
controller and can be adapted to, over a defined time period, analyse at least one or a
combination of the first set of data packets, the first signal and the second signal to enable
automatic transmission of the first signal and the second signal to a first device and the
second device respectively.
[0032] In an aspect, the first device can be operated by the controller to, upon
generation of the first signal, modulate the current thermal attributes of the enclosed space to

the corresponding desired thermal attributes for the enclosed space, and the second device can be selectively operated by the controller to, upon generation of the second signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[0033] In an aspect, the first signal and the second signal can be generated based on
comparison of current thermal attributes and desired thermal attributes of the enclosed space. The desired thermal attributes can be stored in a database operatively coupled to the controller.
[0034] Another aspect of the present disclosure pertains to a method for wireless
thermal management in an enclosed space. The method includes steps of: receiving, at a computing device, from one or more sensors operatively coupled to it and provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space; receiving, at the computing device from a gateway, a first set of data packets from a first user, pertaining to desired thermal attributes for the enclosed space, wherein, upon receipt of the first set of data packets, a first signal is generated at the computing device, and wherein, based on any or a combination of the plurality of first parameters, the first set of data packets and energy parameters of a second device, a second signal is generated at the computing device; and analysing, by a learning engine, over a defined time period, at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to a first device and the second device respectively.
[0035] In an aspect, the method includes steps of: operating, at the computing device,
the first device to, upon generation of the first signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space; and operating, at the computing device, the second device selectively to, upon generation of the second signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[0036] Various objects, features, aspects and advantages of the inventive subject
matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0037] The diagrams are for illustration only, which thus is not a limitation of the
present disclosure, and wherein:
[0038] FIG. 1 illustrates an exemplary block diagram representation of a system for
wireless thermal management in an enclosed space, in accordance with an embodiment of the
present disclosure.
[0039] FIG. 2A illustrates basic principle of architecture of a gateway of FIG. 1
connecting three bus segments, in accordance with an embodiment of the present disclosure.
[0040] FIG. 2B illustrates an exemplary embodiment of features of the
modular gateway architecture according to FIG. 2A.
[0041] FIG. 3 illustrates learning engine of FIG. 1, in accordance with an embodiment
of the present disclosure.
[0042] FIG. 4 illustrates an exemplary flow diagram representation of a method for
wireless thermal management in an enclosed space, in accordance with an embodiment of the
present disclosure.
[0043] FIGs. 5A to 5D illustrate exemplary representations of an internet of things
(IoT) connected fan of FIG. 1 in accordance with an embodiment of the present disclosure.
[0044] FIG. 6 illustrates an exemplary representation of an application-programming
interface (API) for wireless thermal management in an enclosed space in accordance with an
embodiment of the present disclosure.
DETAILED DESCRIPTION
[0045] The following is a detailed description of embodiments of the disclosure
depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0046] In the following description, numerous specific details are set forth in order to
provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention can be practiced without some of these specific details.
[0047] Embodiments of the present invention include various steps, which will be
described below. The steps can be performed by hardware components or can be embodied in

machine-executable instructions, which can be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps can be performed by a combination of hardware, software, and firmware and/or by human operators.
[0048] The present disclosure relates to smart homes. More particularly, the present
disclosure relates to climate control of smart homes.
[0049] An aspect of the present disclosure pertains to a system for wireless thermal
management in an enclosed space (such as a room or any other defined physical space). The proposed system, in an aspect, can include a first device that can be provided in the enclosed space for thermal regulation, and can be operatively coupled to a gateway to operate upon receipt of a first signal. The system can further include a second device that can be provided in the enclosed space for thermal regulation, and can be operatively coupled to the gateway and operable upon receipt of a second signal. The system can further include a controller that can be operatively coupled to the gateway, and can be configured to operate the first device and the second device, wherein the system can further include a learning engine that can be operatively coupled to the controller.
[0050] In an aspect, controller of the proposed system can include one or more
processors that can be operatively coupled to a memory, said memory storing instructions, which when executed by the one or more processors, cause the controller to: receive, from one or more sensors provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space; and receive, from the gateway, a first set of data packets from a first user, said first set of data packets pertaining to desired thermal attributes for the enclosed space. Based on receipt of the first set of data packets, the controller is configured to generate the first signal. Based on any or a combination of the plurality of first parameters, the first set of data packets, and energy parameters of the second device, the controller of the present system can be configured to generate the second signal. Learning engine of the proposed system can accordingly be adapted to, over a defined time period, analyse at least one or combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to the respective first device and the second device.
[0051] In an aspect, the first signal and the second signal can be generated based on
comparison of current thermal attributes and desired thermal attributes of the enclosed space. The desired thermal attributes can be stored in a database operatively coupled to the controller.

[0052] In an aspect, the first device can be operated to, upon generation of the first
signal, modulate current thermal attributes of the enclosed space to the corresponding desired
thermal attributes for the enclosed space such that the second device can be selectively
operated to, upon generation of the second signal, modulate the current thermal attributes of
the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[0053] In an aspect, the learning engine can be any neural network that is configured
to learn based on historical data pertaining to the first user based on the first set of data
packets received from the first user in the past across a defined time period.
[0054] In an aspect, the one or more energy parameters of the second device can be
any or a combination of energy consumption by the second device, and efficiency of the
second device.
[0055] In an aspect, the second signal can be selectively generated for the second
device such that the overall energy consumption of the second device is least.
[0056] In an aspect, the gateway can be operable by wireless means selected from a
group comprising Wi-Fi, Bluetooth, mobile connectivity, infrared, radio frequency and a
combination thereof.
[0057] In an aspect, the at least one of the controller, an infrared (IR) blaster, the one
or more sensors, a communication module (such as Wi-Fi module etc.) can be integrated or
operatively coupled with the first device (For example., fan).
[0058] In an embodiment, the first device can be a fan, and the second device can be
at least one of an air conditioner, air cooler and ventilator.
[0059] In an embodiment, the one or more sensors can be at least one of a temperature
sensor, humidity sensor and photoelectric sensor, and wherein the plurality of first parameters
can be at least one of temperature, humidity and precipitation etc. parameters that can be
associated with the enclosed space.
[0060] In an embodiment, the learning engine can include a neural network, the
neural network, upon training, can be configured to provide enhanced learning capabilities to
operate the first device and the second device accurately based on user preferences.
[0061] Another aspect of the present disclosure pertains to a controller for wireless
thermal management in an enclosed space. The controller can include one or more processors
operatively coupled to a memory, the memory storing instructions executable by the one or
more processors to: receive, from one or more sensors provided in the enclosed space, a
plurality of first parameters pertaining to current thermal attributes of the enclosed space; and
receive, from a gateway, a first set of data packets from a first user, said first set of data

packets pertaining to desired thermal attributes for the enclosed space. Based on receipt of the first set of data packets, the controller can be configured to generate a first signal. Based on any or a combination of the first set of data packets, energy parameters of a second device, and the plurality of first parameters, the controller can be configured to generate a second signal for operating the second device. A learning engine can be operatively coupled to the controller and can be adapted to, over a defined time period, analyse at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to a first device and the second device respectively.
[0062] In an aspect, the first device can be operated by the controller to, upon
generation of the first signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space, and the second device can be selectively operated by the controller to, upon generation of the second signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[0063] In an aspect, the first signal and the second signal can be generated based on
comparison of current thermal attributes and desired thermal attributes of the enclosed space. The desired thermal attributes can be stored in a database operatively coupled to the controller.
[0064] Another aspect of the present disclosure pertains to a method for wireless
thermal management in an enclosed space. The method includes steps of: receiving, at a computing device, from one or more sensors operatively coupled to it and provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space; receiving, at the computing device from a gateway, a first set of data packets from a first user, pertaining to desired thermal attributes for the enclosed space, wherein, upon receipt of the first set of data packets, a first signal is generated at the computing device, and wherein, based on any or a combination of the plurality of first parameters, the first set of data packets and energy parameters of a second device, a second signal is generated at the computing device; and analysing, by a learning engine, over a defined time period, at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to a first device and the second device respectively.
[0065] In an aspect, the method includes steps of: operating, at the computing device,
the first device to, upon generation of the first signal, modulate the current thermal attributes

of the enclosed space to the corresponding desired thermal attributes for the enclosed space; and operating, at the computing device, the second device selectively to, upon generation of the second signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[0066] FIG. 1 illustrates an exemplary block diagram representation of a system for
wireless thermal management in an enclosed space in accordance with an embodiment of the present disclosure.
[0067] According to an embodiment, the system 100 can include one or more
processor(s) 102. The one or more processor(s) 102 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 102 are configured to fetch and execute computer-readable instructions stored in a memory 104 of the system 100. The memory 104 can store one or more computer-readable instructions or routines, which can be fetched and executed to create or share the data units over a network service. The memory 104 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0068] Various components /units of the proposed system 100 can be implemented as
a combination of hardware and programming (for example, programmable instructions) to implement their one or more functionalities as elaborated further themselves or using processors 102. In examples described herein, such combinations of hardware and programming can be implemented in several different ways. For example, the programming for the units can be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for units can include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium can store instructions that, when executed by the processing resource, implements the various units. In such examples, the system 100 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium can be separate but accessible to the system 100 and the processing resource. In other examples, the units can be implemented by electronic circuitry. A database 120 can include data that is either stored or generated as a result of functionalities implemented by any of the other components /units of the proposed system 100.

[0069] In an embodiment, the system 100 for wireless thermal management in the
enclosed space is disclosed. The system 100 can include: a first devicel08 that can be provided in the enclosed space for thermal regulation, operatively coupled to a gateway 110 and operable on receipt of a first signal; a second device 112 that can be provided in the enclosed space for thermal regulation, operatively coupled to the gateway 110 and operable on receipt of a second signal; a controller 106 that can be operatively coupled to the gateway 110 and configured to operate the first device 108 and the second device 112; and a learning engine 114. The controller 106 can include one or more processors 102 that can be operatively coupled to memory 104.
[0070] The memory 104 storing computer-implemented instructions are executable by
the one or more processors 102 to receive a plurality of first parameters that can be pertaining to current thermal attributes of the enclosed space from one or more sensors 116 that can be provided in the enclosed space.
[0071] The controller 106 can be configured to receive, from the gateway 110, first
set of data packets from a first computing device 118 of a first user, wherein the first set of data packets can be pertaining to desired thermal attributes for the enclosed space, wherein the controller 106 can be configured to, on receipt of the first set of data packets, generate the first signal and wherein the first device 108 can be operated to modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space upon generation of the first signal. The controller 106 is configured to, based on any or a combination of the first set of data packets, the plurality of first parameters, and energy parameters of the second device 112, generate the second signal. The second device 112 can be selectively operated by the controller 106, based on one or more energy parameters, to modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space upon generation of the second signal.
[0072] In an embodiment, the learning engine 114 can be operatively coupled to the
gateway 110 and can be adapted to, over a defined time period, analyse at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to the respective first device 108 and the second device 112.
[0073] In an exemplary embodiment, the desired thermal attributes can be set by
either first user or by the learning engine 114. The desired thermal attributes can be stored in the database 120.

[0074] In an embodiment, the first signal and the second signal can be generated
based on comparison of current thermal attributes and desired thermal attributes of the enclosed space.
[0075] In an exemplary embodiment, the energy parameters can correspond to energy
or power profile information of the second device 112. This can enable saving of power or energy consumption.
[0076] The gateway 110 is operable by wireless means selected from a group
comprising Wi-Fi, Bluetooth, mobile connectivity, infrared, radio frequency and a combination thereof. GATEWAY110
[0077] As shown in FIG. 2, the gateway 110 can be connected to three bus
segments 1, 2, 3 and can have a function of routing messages from one bus segment to one or both of the other bus segments. Basic principle of the architecture shown are modular gateways (logical software gateways) 12, 13, 23, such a gateway being responsible for routing messages between two subnets. Gateway 12 thus routes messages from 1 to 2 and vice-versa; gateway 13 routes messages from 1 to 3 and vice-versa, and gateway 23 routes messages from 2 to 3 and vice-versa. Each logical software gateway thus describes an individual connection pathway between two subnets, i.e., bus segments. Gateways 12, 13, 23 can be designed as software programs, which are used to perform the protocol-specific adaptations needed for message routing between the two subnets. Depending on the exemplary embodiment, each subnet is an individual transmission medium. For example, subnet 1 can be a low-speed CAN; subnet 2 can be a high-speed CAN, and subnet 3 can be an SPI bus. If a new subnet is added(for example, a MOST bus), additional logical software gateways can be introduced. The existing ones do not need to be modified. If a subnet, for example, the SPI bus, is removed, logical software gateways 13 and 23 are removed. Logical software gateways for all possible connections are to be written to have a universal gateway function. Depending on the design of the gateway to be implemented, these logical software gateways are then combined to form one overall system. In general, not all subnets are directly connected to one another, so that only selected connection pathways are to be provided with selected logical software gateways. If each subnet is to be connected to each other subnet, N*(N-l)/2 logical software gateways are needed. Variable N is the number of subnets in the overall system. Thus, for three subnets, there will be three logical software gateways; for four subnets there will be six, and for five subnets there will be ten

logical software gateways. It is of secondary importance whether these logical software
gateways are located in one central gateway or in a plurality of point-to-point gateways.
[0078] Referring to FIG. 2B, gateway 110, which is preferably implemented as a
program in a microcontroller of a control unit, includes, in addition to the modular software gateways shown (:CANCAN, :CANSPI), bus-specific transmitting units, which monitor access to the bus medium. Receiving objects (:Rx-CAN, :Rx-SPI), which determine into which logical software gateway an incoming message is routed, are associated with each bus segment. Similarly, there are bus-specific transmitting objects (:TxCAN, :TxSPI) for the transmission operation, which monitor access to the particular bus and prevent more than one modular software gateway from simultaneously occupying the transmitting medium. The software gateways (in FIG. 2B: CANCAN:CANSPI) are internally composed of a plurality of software objects, which buffer incoming messages and perform the protocol-specific adaptations. One simple adaptation is, for example, that a CAN message is to be sent from high-speed CAN having ID (for example 1000) to low-speed CAN (having ID 2000). These protocol-specific adaptations are then performed by appropriate programs (for example, in the simplest case, by a table). Configuration tables are used for the protocol-specific adaptations performed within the logical software gateways.
[0079] In a preferred embodiment, the bus-specific receiving objects are configured
via routing tables via which the decision is made as to whether an incoming message is to be
routed to no logical software gateway, one logical software gateway, or both logical software
gateways. The subsequent treatment of the message is thus saved in the routing table for each
incoming message type. Furthermore, it can happen, due to the different speeds of the buses,
that only every 5th message of a certain type (for example, engine speed) is relayed from one
bus segment to the other. This can also be implemented via the above-mentioned routing
tables in the receiving object. These routing tables are independent of the source code of the
actual gateway, so that a change in the routing table results in little or no change in the
software of the respective modular gateway. The bus-specific receiving unit looks up the
found message in the routing table and decides, on the basis of the information contained
therein, which logical software gateway contains the message for further processing.
[0080] The bus-specific transmitting units, i.e., the programs provided there, monitor
access to the bus. If the bus has just been occupied, they make sure that no message is sent by any logical software gateway. In addition, as mentioned before, the logical software gateways buffer the messages to prevent loss of messages, for example, when the bus segment to which a message is to be sent has just been occupied. A message is thus kept in a wait loop before it

is directly forwarded. The internal scheduler of the gateway unit takes notice of the message having been placed into a wait loop. It causes the message to be transmitted by transmitting a message to the corresponding modular logical software gateway, which then causes the message to be transmitted. Therefore, if a logical software gateway intends to transmit a message, it must report this intent to the scheduler. It depends on the order of the reports which software gateway first obtains the authorization for sending a message. This ensures that the correct order of the messages is observed. If the system contains particularly high-priority messages, the scheduler provides a plurality of methods for reporting an intent to transmit; these methods can be called by the logical software gateways. The scheduler always processes the high-priority requests first and then those of normal priority, and it grants transmission authorizations to the logical software gateways as a function of the priorities. For example, each intent to transmit is provided with a piece of information representing the priority of the message, or the scheduler includes a table in which the priorities of the messages are marked; the scheduler reads the priority from the table.
[0081] The above-described architecture and procedure permit the gate unit to be
configured via tables without modifying their software. For example, by modifying the
parameter sets in the memory, the gateway can be reprogrammed for different message
routing. If the same interfaces are used, the gateway software can be configured exclusively
using parameter sets. If other interfaces are connected to the gateway, a modular software
module is to be integrated into the gateway. Different gateway configurations are thus
generated by combining software modules, for example, from libraries and by providing
routing information. Integration of a new CAN interface having a new CAN matrix is
basically limited to the inputting of the new routing information into the routing table. CAN¬
CAN gateways of different baud rates can thus be integrated into a system in a very short
time. Testability and verification of the obtained codes are simplified by the fact that the
configuration-dependent code is centrally tested and only an integration test for the new
logical SW gateway, i.e., the new configuration, is performed in addition to the system test.
[0082] In an exemplary embodiment, the gateway 110 can be replaced by any other
gateway unit that is known in the art.
[0083] Referring to FIG. 1, the learning engine 114 is any neural network configured
to learn based on historical data for the first user. The learning engine 114 can be configured to provide training of a trend of the received first set of data packets from the first user.

[0084] The one or more energy parameters for any second device 112 can be any or a
combination of energy consumption by said second device 112, and efficiency of the second
device 112.
[0085] The second signal can be selectively generated for the second device 112 such
that the overall energy consumption of the second device 112 is least.
[0086] The controller 106 can include an infrared (TR) blaster that can be configured
to operate the first device 108 and to selectively operate the second device 112.
[0087] The first device 108 can be a fan, blower or any other low energy consuming
home-appliance. The second device 112 can be at least one of an air conditioner, air cooler,
ventilator etc.
[0088] The one or more sensors 116 can be at least one of a temperature sensor,
humidity sensor and photoelectric sensor, and wherein the plurality of first parameters can be
at least one of temperature, humidity and precipitation associated with the enclosed space.
[0089] In an exemplary embodiment, the enclosed space can be a room of any home,
office etc.
[0090] In an exemplary embodiment, the one or more processors 102 can be
configured to calculate thermal comfort data of users based on American Society of Heating,
Refrigerating and Air Conditioning Engineers (ASHRAE) standards (i.e. by using ASHRAE
55 psychometric chart principles). The calculated thermal comfort data can be fed to learning
engine 114 to train learning models with the fed data for enabling further learning of learning
models.
LEARNING ENGINE 114
[0091] The learning engine 114 can include a neural network, wherein the neural
network, upon training, can be configured to provide learning capabilities to operate the first
device 108and the second device 112.
[0092] Referring to FIG. 3, the learning engine 114 can be a machine-learning engine
for analyzing, learning, and recognizing behaviors or patterns of occupants or users. The
system 100 analyzes raw information from all the components of the system 100 and/or from
the occupants to identify active elements in the stream, classify such elements, derive a
variety of metadata regarding the actions and interactions of such elements, and supply this
information to the machine-learning engine 114. As described in greater detail below,
the machine-learning engine 114 can be configured to evaluate the received information and
remember the received information and results of the evaluation over time. Further,
the machine-learning engine 114 can identify certain anomalous and/or normal behaviors.

The system 100 can also include a computer vision engine that can be operatively coupled to
the controller 106 and to the learning engine 114. In an exemplary embodiment, the computer
vision engine can be implemented as a part of a video input device (e.g., as a firmware
component wired directly into a video camera). In such a case, the outputs of the video
camera can be provided to the machine-learning engine 114 for analysis.
[0093] For example, in one embodiment, the computer vision engine can be
configured to analyze video frames to identify targets of interest (such as rooms inside
homes), track those targets of interest (i.e. analysing various parameters inside the rooms),
infer properties about the targets of interest (i.e. inferring multiple parameters of the rooms),
classify them by categories, and tag the observed data. In one embodiment, the computer
vision engine generates a list of attributes (such as temperature, humidity, lighting conditions,
air quality, air freshness, precipitation, user preferred values of the mentioned list and the
like) of the classified objects of interest and provides the list to the machine-learning
engine 114. Additionally, the computer vision engine can supply the machine-learning
engine 114 with a variety of information about each tracked object within a scene.
[0094] In one embodiment, the machine-learning engine 114 receives the video
frames and the results generated by the computer vision engine. The machine-learning engine analyzes the received data, builds semantic representations of behaviors/events depicted in the video frames and learned over time, determines patterns or user-preferred patterns, and learns from these observed behaviors to identify normal and/or abnormal events. Data describing a normal (or abnormal) behavior/event, along with the semantic labels applied to such an event, can be provided to first device 108 and to the second device 112 to operate them, and the operation of control of the first device 108 and second device 112 can be presented on a GUI interface screen.
[0095] The computer vision engine and the machine-learning engine can each be
configured to process the received video data, generally, in real-time. That is, the computer vision engine can be configured to "see" events as they occur. However, the machine learning engine 114 (i.e., a semantic model and a cognitive model) can lag behind in evaluating the sequence of event being observed by the computer vision engine. Thus, time scales for processing information by the computer vision engine and the machine-learning engine can differ. For example, in one embodiment, the computer vision engine processes the received video data frame by frame, while the machine-learning engine processes the received data every N-frames.

[0096] Referring to FIG. 3, the machine-learning engine 114 employs two models for
recognizing, analyzing, and learning behaviors or patterns of occupants; namely, a semantic model and a cognitive model. The behaviours or patterns can be user-preferred speed of fan, air conditioner etc., user-preferred temperature or any other climatic conditions, user-preferred lighting conditions in the enclosed space etc. Based on data provided by the computer vision engine, the semantic model generates semantic descriptions (representations) of what is depicted in the video stream can include semantic descriptions (representations) of objects/subjects and their actions. In other words, the semantic model provides labels data with semantic meaning as to what is observed in the scene. In turn, the cognitive model can be configured to observe patterns associated with a given event; update a pattern (i.e., a memory) representing a given event; reinforcing long-term memories associated with an event; develop "memories" representing new patterns of behavior; create new semantic labelling to apply to new patterns of behavior. As stated, in one embodiment, new patterns of behaviour can be generated as a combination of known patterns. In such a case, the semantic labelling applied to a new behavior can represent a combination of the labels applied to patterns in that new behavior.
[0097] Thus, the cognitive model can simulate some aspects of a human brain, e.g.,
how the human brain perceives abstract concepts, reasons about them, recognizes behaviors, and learns new concepts. In one embodiment, the cognitive model can employ a neuro-semantic network that includes a combination of a semantic representation module and a cognitive model. Each of these components is described in greater detail below. The neuro-semantic network can include a plurality of nodes representing semantic concepts (i.e., a neural net). As is known, a neural net can represent simple concepts using a single node (e.g., a room or its characteristic parameters) and complex concepts can be represented by multiple nodes that include multiple concepts connected by links. The neuro-semantic network can include several levels, where the lowest level describes a collection of primitive events. Higher levels of the neuro-semantic network can describe complex concepts, which are created by combining primitive concepts. Typically, the higher the level of complexity, the more complex concepts it defines. In one embodiment, the neuro-semantic network can provide increasing levels of complexity where the primitives for one level of complexity are combined to form a primitive for the next level of complexity, and so on. Data provided to the cognitive model can be used to excite nodes of the neuro-semantic network, allowing behaviors to be recognized and the network itself to be updated. Updates can include creating nodes, updating nodes, deleting nodes, modifying, or creating links between nodes.

[0098] In one embodiment, the semantic representation module receives data
describing rooms, occupants in the rooms from the computer vision engine. Such data can include identification data, posture, location, trajectory, velocity, acceleration, direction, and other quantitative characteristics that describe characteristics of occupants identified in the scene by the computer vision engine. Based on data received from the computer version engine, the semantic representation module forms two semantic streams; namely, a primitive event symbol stream and a phase-space symbol stream. The primitive event symbol stream includes semantic i.e., symbolic, descriptions of primitive events recognized in the scene and objects participating in such primitive events. The phase-space partitioning stream includes semantic descriptions, i.e., phase-space symbols, of values of quantitative characteristics of an object (e.g., a symbol "a" indicating that an object was located in a certain area of the scene in the enclosed space or a symbol "x" indicating that an object's velocity is within a certain range, and so on). Here, object or subject means user or occupant. The semantic representation module can generate formal language vectors based on first parameters of the thermal attributes of the enclosed space by combining relative data from the primitive event and phase-space symbol streams. As described in greater detail herein, the formal language vectors are used to describe both semantic and quantitative aspects of behavior observed to have occurred within a scene.
[0099] As shown, the cognitive model includes a perception module,
a behavior comprehension module, and reinforcement and decay module. In general, the perception module analyzes data provided by the semantic representation module, learns patterns, generalizes based on observations, and learns by making analogies. In one embodiment, the perception module can include multiple memories such as a perceptual memory, an episodic memory, and a long-term memory. Based on the incoming data, the perception module can perceive multi-level concepts (structures), such as a percept. As used herein a "percept" represents a combination of nodes (and links between nodes). That is, a percept can be defined as a sub graph of a neural net that includes each node (and links between node) relevant for a particular identified behaviour/pattern. Thus, percepts can represent behaviors perceived by the machine-learning engine 114 to have occurred. More complex behaviors can be represented as combinations of percepts. As described in detail below, perceived concepts and corresponding memories can be stored in a workspace and processed by various codelets. In one embodiment, a codelet provides an active, typically independent, process (agent) that includes executable code. Generally, a codelet can evaluate percepts and relationships between percepts to recognize behaviors and other events

important to the system 100, build new structures by using analogies (e.g., combine two
similar percepts into a higher level node), detect anomalies (e.g., by comparing percepts to
long-term memory content), look for expected events/behaviors, and so on.
[00100] In one embodiment, the perception module can be further configured to
determine whether the computer vision engine has misclassified an object. For example, if the perception module determines that the computer vision engine has repeatedly applied particular classification to an object (e.g., an occupant) and then classifies this same object as something else (e.g., a non-living thing), the perception module can inform the computer vision engine of the misclassification.
[00101] In general, the behavior comprehension module recognizes behaviors and
responds to recognized behaviors. For this purpose, the behavior comprehension module further analyzes structures placed in the workspace. As the presence of given percepts are broadcast to other components of the cognitive model, multiple internal and external actions can be performed. For example, internal actions can include updating and/or generalizing procedures and concepts, models and events, creating new concepts and procedures, generating expectation structures/procedures, and so on. In one embodiment, external actions can include issuing a signal (e.g., alarm) responsive to recognized (or unrecognized) behavior, providing feedback to other components of the system 100 (such as the semantic representation module, the computer-vision engine, etc.), adjusting camera operations, and so on. The feedback can include data regarding the observed events/behaviors needed to modify the system 100 to better recognize the events/behaviors/patterns in the future.
[00102] In general, the reinforcement and decay module reinforces memories of
repeatedly occurring behaviors and decays and/or eliminates memories of occasionally occurring behaviors. More specifically, percepts, and associated nodes, can decay over time if not used or alternatively, can be reinforced, if used. Thus, for example, when a structure, such as a percept, is placed into the workspace similar memories can be reinforced (or updated to better generalize the behavior represented by the memory). In this manner, a competitive learning environment is created where useful percepts, and associated nodes, survive because they are reinforced, and non-useful, percepts, and associated nodes, decay away.
[00103] Referring to FIG. 1, in an exemplary embodiment, the system 100 can operate
the first device 108 (i.e. fans) at first in order to provide thermal management in the enclosed space. Based on an amount of energy required by the system 100 to operate second device

112 (air conditioners, air coolers etc.), the system 100 can be configured to check the amount of energy present. If the amount of energy present or stored in the system 100 is greater than the energy required, the controller 106 can be configured to operate the second device 112 (such as air conditioners, air coolers etc.) in order to provide thermal management to the enclosed space (such as homes, offices etc.). The first device 108 can consume less energy as compared to that of the second device 112. In this way, energy conservation takes place by selectively operating the second device 112.
[00104] In an exemplary embodiment, the learning engine 114 can implement one or
more learning algorithms that can be programmed or selected by the first user. The learning
algorithms can be executed by the controller 106. One such control algorithm can be a set-
point schedule containing a list of times associated to a list of temperatures. The controller
106sets-up or sets-back the temperature according to such a set-point schedule. For example,
a set-point schedule could be configured to adjust the temperature to 60 degrees Fahrenheit at
6:00 a.m., then to 67 degrees at 6:30 a.m., and up to 73 degrees at 8:00 a.m., etc. The first
computing device 118 can include a display unit that can be configured with additional
controls, which could, for example, switch the display between Fahrenheit and Celsius for
the temperature, between standard and military time, and between showing a single day's
schedule versus showing a week's schedule. There is a control to review the schedules, one to
program new schedules, and one to manually control the heating or cooling of the house. In
addition to the additional controls programmed and displayed on the display unit, physical
buttons of fans, air conditioners etc. can be programmed and used for working with a first
user interface of the display unit as well. This is similar to the operation of a PDA.
[00105] In an exemplary embodiment, the learning algorithms (such as supervised,
unsupervised or reinforced algorithms)can be implemented with the help of software tools(such as TensorFlow). TensorFlow is an open source software library to help developers to design, build, and train learning models. TensorFlow can function by sorting through layers of data (also known as nodes) as part of learning. In the first layer, the system 100 determines the basic features of object. As deeper movements occur, it looks for more refined information regarding the object. The sorting of images is done at a faster rate, thus giving users more valuable information. TensorFlow is available on different operating systems such as Linux, Windows, MacOS and also on mobile operating platforms like iOS and Android. One of the salient features of TensorFlow is that it is capable of running on multiple CPUs and GPUs. The computations in TensorFlow are reported as stateful dataflow graphs.

[00106] Examples of supervised learning algorithms can be Regression, Decision
Tree, Random Forest, KNN, Logistic Regression etc. Examples of unsupervised learning algorithms can be Apriori algorithm, K-means clustering etc. An exemplary reinforcement learning algorithm can be Markov Decision Process.
[00107] In an exemplary embodiment, the learning engine 114 can implement a neural
network that receives sensor signals from the one or more sensors 116 and provides an output classifying the sensed activity and occupation levels. The controller 106 communicates with the neural network and automatically conditions the air conditioners, heaters, fans etc. in response to a difference between a current temperature in the enclosed space and selected setpoint temperature (either manually or automatically). The one or more sensors 116 can also includes a passive infrared pyroelectric sensor that provides a voltage signal indicating occupancy and activity levels. A signal conditioner preferably provides a mean and variance of the sensor signal to the neural network. The neural network includes a plurality of layers with selectively adjustable strengths so that the neural network output corresponds to the sensed activity and occupancy levels as desired.
[00108] In an exemplary embodiment, the neural network includes an input layer of
neurons that receives the information from the signal conditioner. The signals received at the input layer are processed by a hidden layer of neurons after being communicated to the hidden layer through selectively adjustable strengths or weights. In one example, the hidden layer neurons implement a sigmoidal type function. The results of the processing within the hidden layer are then communicated to an output layer through another set of selectively adjustable strengths or weights. The output layer neurons preferably perform the same computations as the neurons in the hidden layer.
[00109] Neural networks are known. Those skilled in the art who have the benefit of
this description will be able to select an appropriate neural network architecture with selectively adjustable weights or strengths so that the processing within the neural network provides an output that is useful by the controller 106 such as a microprocessor to make a determination regarding current occupancy and activity levels within the zone of interest. The neural network preferably has selectively adjustable strengths or weights and so that the neural network can be trained to provide an output consistent with a particular activity and occupancy level sensed by the one or more sensors 116. It is known how to adjust strengths and weights within a neural network and those skilled in the art who have this benefit of this description will be able to do so using conventional techniques. In the illustrated example, the output from the output layer of the neural network is provided with

four possible output values. A first value is a binary 00, which indicates that the zone of interest (in the enclosed space) is not occupied. A second output, which is a binary 01, indicates that the zone is occupied but the level of activity is low. This can correspond, for example, to having one or two individuals in a zone sitting and reading a book or watching television. A third output, which is a binary 10, corresponds to an occupied zone with a medium level of activity. An example would be one or more individuals in a room moving about performing basic tasks such as cooking or cleaning. A fourth output, which in the illustrated example is a binary 11, indicates an occupied room with a high activity level. An example of this would be where one or more individuals are exercising or playing within the zone.
[00110] In an exemplary embodiment, the output layer can include two neurons. Each
neuron provides a single bit binary output (i.e., a 1 or a 0). The combined binary outputs from
the two neurons of the output layer provide the two binary digit values in the above-
mentioned four outputs. The output from the neural network is processed by the
controller 106, using a look up table, for example, so that the controller determines the
occupancy and activity level within the zone of the enclosed space. The controller 106 can
also be programmed to process the output in another manner. Given this description, those
skilled in the art will be able to choose the approach that best suits their particular needs.
[00111] In an exemplary embodiment, the controller 106 preferably conditions any of
fans, air conditioners, heaters etc. in response to a difference between the first user-selected set point temperature and the current actual temperature within the zone of the enclosed space. When the controller 106 determines different occupancy and activity levels, the controller 106 preferably automatically conditions the response of at least one of fans, air conditioners etc. by adjusting one or more control parameters. In one example, the controller 106 automatically adjusts the set point temperature (i.e., changes it from the first user selected value) responsive to one or more of the determined occupancy and activity level outputs from the neural network. For example, during a cooling season if the first user-selected set point temperature will provide adequate cooling for a low or medium activity level, the controller 106 may not make any adjustment to the response of fans, air conditioners etc. to a temperature difference between the current temperature and the set point temperature. On the other hand, when the activity level is high, the controller 106 can automatically lower the set point temperature by a few degrees to provide additional cooling to compensate for the heat generated by the high activity level within the zone. In this

manner, the controller 106 automatically provides a more consistent comfort level within the zone.
[00112] Other control parameters that can be automatically adjusted by the controller
106 include compressor frequency (of a compressor (present in air conditioners) that compresses refrigerant), fan speed, or control signal gain. Given this description, those skilled in the art will realize that a variety of control parameters can be adjusted responsive to different occupancy and activity levels sensed within a zone.
[00113] Given this description, those skilled in the art will be able to suitably program
a controller 106 to perform the desired operation based upon the neural network output information. The neural network preferably is trainable to provide the output consistent with particular sensor inputs. The neural network in one example is pre-trained before being provided to a customer with certain predetermined outputs corresponding to expected input sensor signal levels. In another example, the one or more sensors 116 can be capable of allowing an individual to use user interface to retrain the neural network in order to provide outputs consistent with particular sensor input levels that can be experienced within a particular location. In this manner, this allows an individual to customize the response of the controller 106 by selectively adjusting the weights or strengths within the neural network. The adjustment of those weights or strengths within the neural network is accomplished in a conventional manner.
[00114] While one example implementation of this invention has been illustrated and
discussed, those skilled in the art will realize that a variety of modifications can be made.
More complex neural network architectures, for example, could be implemented. More
possible neural network outputs than those illustrated also can be implemented. The
illustrated example shows how the inventive arrangement provides a customizable, enhanced
temperature control system that more conveniently and economically provides a consistent
temperature comfort level without requiring continued manual adjustment by an individual.
[00115] FIG. 4 illustrates an exemplary flow diagram representation of a method for
wireless thermal management in an enclosed space in accordance with an embodiment of the present disclosure.
[00116] According to an embodiment, the method 400 can include at a step 402,
receiving, at a computing device, from one or more sensors operatively coupled to it and provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space.

[00117] In an embodiment, the method 400 can include at a step 404, receiving, at the
computing device from a gateway, a first set of data packets from a first user, pertaining to
desired thermal attributes for the enclosed space, wherein, upon receipt of the first set of data
packets, a first signal is generated at the computing device, and wherein, based on any or a
combination of the plurality of first parameters, the first set of data packets and energy
parameters of a second device, a second signal is generated at the computing device.
[00118] In an embodiment, the method 400 can include at a step 406, analysing, by a
learning engine, over a defined time period, at least one or a combination of the first set of
data packets, the first signal and the second signal to enable automatic transmission of the
first signal and the second signal to a first device and the second device respectively.
[00119] In an embodiment, the method 400 can further include a step of operating, at
the computing device, the first device to, upon generation of the first signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[00120] In an embodiment, the method can further include a step of operating, at the
computing device, the second devices electively to, upon generation of the second signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
[00121] In an embodiment, the method 400 can include a step of providing, by a
learning engine, training of a trend of the received first set of data packets from the first user.
[00122] In an embodiment, at the steps of operating the first device and operating the
second device, the controller can include an infrared (IR) blaster to operate the first device and the second device.
[00123] In an embodiment, the first device can be a fan, the second device can be at
least one of an air conditioner, air cooler and ventilator, and wherein the plurality of first parameters associated with the enclosed space can be at least one of temperature, humidity and precipitation.
[00124] In an embodiment, at the step of providing training, the learning engine can
include a neural network, and wherein the neural network, upon training, is configured to
provide learning capabilities to operate the first device and the second device.
[00125] The learning engine implemented in the method 400 can be same as learning
engine 114. Similarly, the gateway that is implemented in the method 400 can be same as the gateway 110.

[00126] FIGs. 5A to 5D illustrate exemplary representations of an internet of things
(IoT) connected fan of FIG. 1 in accordance with an embodiment of the present disclosure.
As shown in FIG. 5B, the IR blaster 502 can be integrated with the fan 500. The IR blaster
502 can act as a remote controller to control air conditioners, air coolers etc.
[00127] As shown in FIG. 5C, the one or more sensors 504 (such as temperature,
humidity, light sensors etc.) can be integrated or coupled to the fan 500. The one or more
sensors504 can be configured to understand thermal comfort conditions of occupants.
[00128] Referring to FIG. 5D, the fan 500 can be integrated with a communication
module 506. The communication module 506 can be at least one of Wi-Fi module, Bluetooth,
GSM module etc. The communication module 506 can be connected to internet in order to
transfer data between sensing devices 504, fan 500 and remote users. The first user can have
a computing device in order to operate fan 500, IR blaster 502, air conditioner etc.
[00129] FIG. 6 illustrates an exemplary representation of an application-programming
interface (API) for wireless thermal management in an enclosed space in accordance with an embodiment of the present disclosure.
[00130] Referring to FIG. 6, the API or mobile application can be installed in the
computing device of the first user. The computing device can have a graphical user interface (GUI) screen through which the remote users/occupants can control operation of fans, air conditioners, water coolers, ventilators etc. The first users can modulate speed of fans, air conditioners etc. The first users can switch on or off the fans, air conditioners as well. The API can store all the first user-preferred data in the computing device, and this data can be utilized by the learning engine to maintain accurate or perfect temperature as per user preferences. This provides comfort and relaxation to users/occupants, and improves health of users.
[00131] While the foregoing describes various embodiments of the invention, other and
further embodiments of the invention can be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE PRESENT DISCLOSURE
[00132] The present disclosure provides a learning system and method for wireless
thermal management in an enclosed space (such as home, office etc.) in real-time.

[00133] The present disclosure provides a simple and cost effective system and method
for controlling operation of fans, air conditioners etc. automatically.
[00134] The present disclosure provides a reliable and fast system and method for
wireless thermal management in an enclosed space with enhanced sustainability.
[00135] The present disclosure provides a precise, accurate and time-efficient system
and method for controlling operation and speed of fans, air conditioners etc. in an enclosed
space based on user preferences.
[00136] The present disclosure provides a smart system and method for controlling
operation and/or speed of fans, air conditioners etc. in an enclosed space to enhance
occupants/users' comfort.
[00137] The present disclosure provides an energy conserving system and method for
controlling operation and speed of fans, air conditioners etc. in an enclosed space.

We Claim

1.A system for wireless thermal management in an enclosed space, said system
comprising:
a first device provided in the enclosed space for thermal regulation, said first device being operatively coupled to a gateway and operable upon receipt of a first signal;
a second device provided in the enclosed space for thermal regulation, said second device being operatively coupled to the gateway and operable upon receipt of a second signal;
a controller operatively coupled to the gateway, said controller comprising one or more processors operatively coupled to a memory, said memory storing instructions executable by the one or more processors to enable the controller to:
receive, from one or more sensors provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space; and
receive, from the gateway, a first set of data packets from a first user pertaining to desired thermal attributes for the enclosed space, wherein the controller is configured to, upon receipt of the first set of data packets, generate the first signal, and wherein the controller is configured to, based on any or a combination of the plurality of first parameters, the first set of data packets, and energy parameters of the second device, generate the second signal; and
a learning engine operatively coupled to the gateway and adapted to, over a defined time period, analyse at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to the respective first device and the second device.
2. The system as claimed in claim 1, wherein the learning engine is any neural network
configured to learn based on historical data, pertaining to the first user, based on the first set
of data packets received from the first user in the past across a defined time period.

3. The system as claimed in claim 1, wherein the first device and the second device can be operated to modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
4. The system as claimed in claim 1, wherein the energy parameters for said second device can be any or a combination of energy consumption by said second device, and efficiency of said second device.
5. The system as claimed in claim 1, wherein the second signal is selectively generated for the second device such that the cumulative energy consumption of the second device is least.
6. The system as claimed in claim 1, wherein the gateway is operable by wireless means selected from a group comprising Wi-Fi, Bluetooth, mobile connectivity, infra-red, radio frequency and a combination thereof.
7. A controller for wireless thermal management in an enclosed space, said controller comprising:
one or more processors operatively coupled to a memory, the memory storing instructions executable by the one or more processors to:
receive, from one or more sensors provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space; and
receive, from a gateway, a first set of data packets from a first user, said first set of data packets pertaining to desired thermal attributes for the enclosed space, wherein the controller is configured to, upon receipt of the first set of data packets, generate a first signal, wherein the controller is configured to, based on any or a combination of the first set of data packets, energy parameters of a second device and the plurality of first parameters, generate a second signal, and
wherein a learning engine operatively coupled to the controller and adapted to, over a defined time period, analyse at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to a first device and the second device respectively.

8. The controller as claimed in claim 7, wherein the first device is operated to, upon generation of the first signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space, and the second device is selectively operated to, upon generation of the second signal, modulate the current thermal attributes of the enclosed space to the corresponding desired thermal attributes for the enclosed space.
9. A method for wireless thermal management in an enclosed space, said method comprising steps of:
receiving, at a computing device, from one or more sensors operatively coupled to it and provided in the enclosed space, a plurality of first parameters pertaining to current thermal attributes of the enclosed space;
receiving, at the computing device from a gateway, a first set of data packets from a first user, pertaining to desired thermal attributes for the enclosed space, wherein, upon receipt of the first set of data packets, a first signal is generated at the computing device, and wherein, based on any or a combination of the plurality of first parameters, the first set of data packets and energy parameters of a second device, a second signal is generated at the computing device; and
analysing, by a learning engine, over a defined time period, at least one or a combination of the first set of data packets, the first signal and the second signal to enable automatic transmission of the first signal and the second signal to a first device and the second device respectively.
10. The method as claimed in claim 9, wherein the method comprises steps of: operating, at
the computing device, the first device to, upon generation of the first signal, modulate the
current thermal attributes of the enclosed space to the corresponding desired thermal
attributes for the enclosed space; and operating, at the computing device, the second
device selectively to, upon generation of the second signal, modulate the current thermal
attributes of the enclosed space to the corresponding desired thermal attributes for the
enclosed space.