system

Abstract

Provide a system. [Solution] a human sensor means for detecting human movement; a server means for receiving data from the human presence sensor means; A generating AI means for generating optimal settings for an air conditioner based on the data received by the server means; a terminal means for transmitting the air conditioner settings generated by the generating AI means to the air conditioner; and an air conditioner control means for controlling the air conditioner.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional air conditioning control systems have difficulty optimizing energy consumption while maintaining comfort in living spaces. In particular, manually adjusting air conditioning settings takes time to find the optimal settings and reduces energy efficiency. Furthermore, they lack mechanisms for quickly responding to certain environmental changes and human movement. This can result in energy waste and an uncomfortable living environment. Therefore, there is a need for a new air conditioning control system that automatically maintains comfort in living spaces while efficiently utilizing energy resources. [Means for solving the problem]

[0005] The present invention provides a system that uses a motion sensor to detect human movement and transmits the data to a server. The server generates optimal air conditioner settings using a generation AI based on the received data. The generated air conditioner settings are then transmitted to the air conditioner to control it. This allows the air conditioner settings to be optimized in real time based on human movement and environmental data, providing a comfortable living space while efficiently utilizing energy resources.

[0006] Specifically, the present invention comprises a system including the following means:

[0007] 1. Human motion detection sensor

[0008] 2. Server means for receiving data from the human presence sensor means

[0009] 3. A generating AI means for generating optimal settings for an air conditioner based on the data received by the server means.

[0010] 4. A terminal means for transmitting the air conditioner settings generated by the generating AI means to the air conditioner.

[0011] 5. Air conditioner control means for controlling the air conditioner

[0012] This realizes an air conditioner control system that minimizes the power consumption of the air conditioner and improves user comfort.

[0013] The "human presence sensor means" is a sensor device for detecting human movement and collecting information about it.

[0014] "Server means" refers to a computer system for processing data received from sensors and inputting it into the generative AI model.

[0015] The "generative AI means" is an artificial intelligence model that automatically generates optimal settings for the air conditioner based on the data received.

[0016] The "terminal means" is a device that receives the air conditioner settings sent from the server and transmits them to the actual air conditioner.

[0017] The "air conditioner control means" is a mechanism that controls the air conditioner based on instructions received from the terminal means.

[0018] A "timestamp" is data for recording the time at which a particular event occurs.

[0019] "Environmental data" refers to data that indicates the ambient environmental conditions, such as temperature and humidity, that are relevant to the operation of the air conditioner.

[0020] "Minimizing power consumption" refers to minimizing the amount of energy used when using an air conditioner.

[0021] "User comfort" refers to the comfort felt by the user through temperature and airflow adjustments by an air conditioner.

[0022] "Real-time" is a term that indicates that data acquisition, processing, and response are all instantaneous. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] This invention is a system that optimizes the operation of an air conditioner by combining a human sensor, a server, a generating AI, a terminal, and an air conditioner control means. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[0045] System Overview

[0046] Initialization Phase

[0047] server:

[0048] The server loads the generative AI model, initializes interfaces to receive data from various sensors and devices, and initializes logs to prepare for recording the system's operating status.

[0049] Device:

[0050] The sensors are set up to work properly, especially the motion sensors, which are placed in specific locations in the room and calibrated to detect human movement.

[0051] Acquiring and Sending Data

[0052] Device:

[0053] The motion sensor detects the user's movements in real time, while simultaneously recording environmental data (such as temperature and humidity). This data is then sent to the server at regular intervals.

[0054] server:

[0055] The server receives the data sent from the device and prepares it for input into the generative AI, including timestamps of detected movements and environmental data.

[0056] Processing by generative AI

[0057] server:

[0058] The server inputs the received data into the AI ​​generator, which then calculates the optimal air conditioner settings based on that data. The resulting settings include the air conditioner temperature, airflow, and operating mode.

[0059] Examples:

[0060] For example, if the motion sensor detects user movement in the morning, the generated AI will know that the room temperature is low at that time and change the air conditioner setting to 20 degrees to make the user comfortable. This setting information is then sent to the device.

[0061] Air conditioning control

[0062] Device:

[0063] Receives air conditioner setting information sent from the server. After receiving the information, changes the air conditioner settings according to the information. Specific setting changes include temperature adjustment, air volume adjustment, and operation mode change.

[0064] Examples:

[0065] If the motion sensor detects movement in the bedroom at night, the AI ​​will use that data to instruct the device to change the air conditioner setting to 23 degrees. The device will then adjust the air conditioner setting to 23 degrees.

[0066] Example of operation

[0067] Morning Scenario

[0068] User:

[0069] In the morning, I enter the living room.

[0070] Device:

[0071] A motion sensor installed in the room detects the user's movements.

[0072] server:

[0073] The server receives data from the sensors and inputs it into the generation AI.

[0074] Generation AI:

[0075] Based on the condition "morning" and other environmental data, the generating AI generates instructions to set the temperature in the living room to 20 degrees.

[0076] Device:

[0077] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[0078] Night Scenario

[0079] User:

[0080] At night, go into your bedroom before going to sleep.

[0081] Device:

[0082] A motion sensor installed in the bedroom detects the user's movements.

[0083] server:

[0084] The server receives data from the sensors and inputs it into the generation AI.

[0085] Generation AI:

[0086] Based on the condition "night" and other environmental data, the generating AI generates instructions to set the air conditioner to 23 degrees.

[0087] Device:

[0088] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[0089] In this way, the system of the present invention collects sensor data in real time and utilizes generative AI to optimize air conditioner operation, thereby achieving both efficient use of energy resources and user comfort.

[0090] The processing flow will be explained below.

[0091] Step 1:

[0092] Server: Loads the generative AI model and initializes the system. It loads the model into memory to allow the generative AI to calculate optimal air conditioner settings, and sets up interfaces to receive data from various sensors and devices.

[0093] Step 2:

[0094] Terminal: Detects user movement through a motion sensor, which also records environmental data (temperature and humidity) at specific intervals and forms this information into a data packet.

[0095] Step 3:

[0096] Terminal: The terminal creates a data packet, adds a timestamp to it, and sends it to the server. This information includes the person's movement, current temperature, and humidity.

[0097] Step 4:

[0098] Server: Receives data packets sent from the device, logs the received data, and formats the data for processing by the generation AI.

[0099] Step 5:

[0100] Server: Inputs the formatted data into the AI ​​generator, which calculates the optimal air conditioning settings based on the received data.

[0101] Step 6:

[0102] Server: Receives the output from the generation AI and creates operating instructions for the air conditioner. It creates instructions including specific settings (temperature, airflow, operation mode).

[0103] Step 7:

[0104] Server: Sends the generated air conditioner setting instructions to the terminal. These instructions indicate how to change the air conditioner settings.

[0105] Step 8:

[0106] Terminal: Receives instructions for air conditioner settings from the server. Based on this information, it generates specific commands to adjust the air conditioner settings.

[0107] Step 9:

[0108] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[0109] Step 10:

[0110] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, starts collecting data from the sensors again to prepare for the next cycle.

[0111] The above is the specific program processing flow for implementing the invention. This system detects user movements in real time and uses generative AI to optimize air conditioner settings, aiming to achieve both efficient use of energy resources and a comfortable living environment.

[0112] Example 1

[0113] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0114] In modern air conditioning systems, minimizing energy consumption while maximizing user comfort is a critical issue. Existing systems generally rely on manual settings or timer functions, making it difficult to achieve optimal control that adapts to user movements and environmental conditions. Furthermore, air conditioner power consumption is often not effectively managed. Therefore, a new air conditioning system that can achieve both user comfort and energy efficiency is needed.

[0115] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0116] In this invention, the server includes a detection means, a receiving means, a generating means, a transmitting means, and a control means, which enables real-time detection of human movement and optimization of air conditioning settings using a generative AI model based on the data, thereby achieving a balance between minimizing power consumption and comfort.

[0117] - "Detection means" means a sensor device for detecting human movement.

[0118] The "receiving means" is a function or device for receiving data from the detecting means.

[0119] The "generation means" refers to a generation AI model and its execution environment for generating air conditioning settings based on the data acquired by the receiving means.

[0120] The "transmission means" is a function or device for transmitting the air conditioning settings generated by the generation means to the air conditioner.

[0121] The "control means" is a function or device for controlling the air conditioner based on the air conditioning settings received from the transmission means.

[0122] A "timestamp" is data that indicates a specific time or date, and is used to record the occurrence time of a detected event or data.

[0123] "Environmental data" refers to data that indicates the surrounding environmental conditions such as temperature and humidity.

[0124] This invention is a system that combines a motion sensor, a server, a generative AI model, a terminal, and an air conditioner to optimize the operation of the air conditioner. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[0125] Initialization Phase

[0126] First, the server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model into memory using TENSORFLOW® or PyTorch. Next, the server initializes an interface for receiving data from multiple sensors and devices. This is done by building a REST API using the Python Flask framework. The server also prepares to use Logstash or ElasticSearch® for log management and record the system's operating status.

[0127] The device is set up to ensure the sensor works properly. For example, a Raspberry Pi or Arduino is used, and a PIR motion sensor is connected to the GPIO pin to check operation. The motion sensor is installed on the ceiling or wall of the room and angled to accurately detect user movement.

[0128] Acquiring and Sending Data

[0129] The device equipped with a motion sensor detects user movement in real time. In addition, it simultaneously records environmental data (temperature and humidity) using a DHT22 temperature and humidity sensor. This data is sent to the server at regular intervals. The data is sent using an HTTP POST request.

[0130] The server receives data sent from the device. Using the Flask framework, the received data includes timestamps of the user's movements and environmental data (temperature and humidity). The server preprocesses this data and prepares it for input to the generative AI. Specifically, it converts the data from JSON format to the input format of the generative AI.

[0131] Processing by generative AI

[0132] The server inputs the received data into a generative AI model, which then calculates the optimal air conditioning settings based on this data. The calculation results in the air conditioning unit's temperature settings, airflow settings, operating mode, etc.

[0133] For example, if a motion sensor detects user movement in the morning and the current room temperature is 22 degrees, the generative AI model will instruct the air conditioner to change the setting to 20 degrees. This calculation result is sent to the device.

[0134] Air conditioning control

[0135] The terminal receives the air conditioning setting information sent from the server. After receiving the information, it changes the air conditioning unit settings based on that information. Specifically, this includes adjusting the temperature, airflow, and operating mode. To do this, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate signals to the air conditioning unit.

[0136] Examples of concrete examples and prompts

[0137] In the morning scenario, when a user enters the living room, the device detects the user's movement and sends that data, along with environmental data, to the server. The server inputs the data into the generative AI model and calculates the optimal air conditioning settings. For example, if the room temperature is 22 degrees at 7 a.m., the generative AI will set the air conditioning temperature to 20 degrees. The device that receives this instruction will set the air conditioning temperature to 20 degrees.

[0138] In the night scenario, the same process is repeated when the user enters the bedroom. Based on the "night" condition and other environmental data, the generative AI generates an instruction to set the air conditioner to 23 degrees. The device receives this instruction and adjusts the air conditioner temperature to 23 degrees.

[0139] Example prompt sentence:

[0140] "Optimize air conditioning settings when someone enters the room. Current conditions are 22 degrees and the time is 7am."

[0141] As a result, the system of the present invention collects sensor data in real time and uses generative AI to optimize the operation of air conditioning equipment, thereby achieving both efficient energy consumption and user comfort.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model using TensorFlow or PyTorch into memory. This is done during the initialization phase of the program. The input is the trained data for the model, and the output is a ready-to-run generative AI model.

[0145] Step 2:

[0146] The server initializes the data receiving interface. This is achieved by building a REST API using the Python Flask framework. The input is the interface configuration information, and the output is the endpoint for receiving data.

[0147] Step 3:

[0148] The server initializes the logs and prepares to record system activity. Specifically, a log analysis tool such as Logstash or Elasticsearch is used. The input is a log configuration file, and the output is a log management system ready to record system activity.

[0149] Step 4:

[0150] The terminal sets up the sensor and adjusts the position of the motion sensor. Using Raspberry Pi or Arduino, connect the PIR motion sensor to the GPIO pin and check its operation. The input is the sensor installation information, and the output is a working sensor system.

[0151] Step 5:

[0152] The device detects user movements in real time through a motion sensor. Specifically, when the sensor detects movement, the Raspberry Pi records this data. The input is the user's movement, and the output is the movement detection data.

[0153] Step 6:

[0154] The device also simultaneously records environmental data and sends it to the server at regular intervals. Temperature and humidity are measured using the DHT22 and sent to the server via an HTTP POST request along with PIR motion sensor data. The input is temperature, humidity, and motion detection data, and the output is the sensor data sent to the server.

[0155] Step 7:

[0156] The server receives data sent from the device. It uses Flask as the framework and receives data at the receiving endpoint. The input is the transmitted sensor data, and the output is the received data.

[0157] Step 8:

[0158] The server preprocesses the received data and prepares it for input to the generative AI. It converts the JSON formatted data into a format that the generative AI model can accept. The input is the received JSON data, and the output is the input data for the generative AI.

[0159] Step 9:

[0160] The server inputs data into the generative AI, which then calculates the optimal air conditioning settings. The generative AI model calculates the optimal temperature settings, airflow settings, and operation mode based on the input data. The input is processed environmental data and motion detection data, and the output is air conditioning setting information.

[0161] Step 10:

[0162] The server sends the generated air conditioning setting information to the terminal. The setting information obtained from the generative AI model is converted to JSON format and sent to the terminal. The input is the generated air conditioning setting information, and the output is the setting instructions sent to the terminal.

[0163] Step 11:

[0164] The terminal receives the air conditioning setting information sent from the server. The terminal receives the setting information and stores it in its internal memory. The input is the air conditioning setting information from the server, and the output is the recorded setting information.

[0165] Step 12:

[0166] The terminal changes the air conditioner settings based on the received information. Specifically, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate infrared signal to the air conditioner. The input is the saved air conditioner setting information, and the output is the air conditioner with the changed setting.

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] Maintaining the comfort of customers and employees in real-world spaces such as stores while minimizing the energy consumption of air conditioning equipment is a difficult challenge. This becomes particularly complex when different temperatures and airflow volumes need to be adjusted in multiple zones within a store. Conventional air conditioning systems have difficulty responding to these real-time conditions, which can result in energy waste and customer discomfort.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes a detection means for detecting human movement, an information processing means for receiving data from the detection means, a generation AI model means for generating optimal air conditioning settings based on the data received by the information processing means, a communication means for transmitting the air conditioning settings generated by the generation AI model means to air conditioning equipment, a control means for controlling the air conditioning equipment, and a means for adjusting the temperature and air volume of multiple zones based on the data. This makes it possible to maintain optimal air conditioning settings in real time in multiple zones within a store, ensuring the comfort of customers and employees while minimizing energy consumption.

[0172] The "detection means" is a sensor device for detecting human movement.

[0173] The "information processing means" is a server or computer system that aggregates data received from the detection means and inputs it into the generative AI model means.

[0174] "Generative AI model means" refers to an artificial intelligence model that calculates optimal air conditioning settings based on data received by the information processing means.

[0175] "Communication means" refers to the network interface and communication protocol used to transmit the air conditioning settings generated by the generation AI model means to the air conditioning equipment.

[0176] "Control means" refers to a system or device for operating and adjusting air conditioning equipment based on air conditioning settings received from communication means.

[0177] "Multiple zones" refers to different areas or sections within a store or other physical space.

[0178] "Real-time" means that data is collected and processed immediately, and air conditioning settings are adjusted immediately based on the results.

[0179] "Minimizing energy consumption" means keeping the energy required to operate air conditioning equipment as low as possible.

[0180] "Comfort" refers to the quality of the indoor environment that keeps customers and employees comfortable.

[0181] A system for realizing this application example includes a detection means, an information processing means, a generative AI model means, a communication means, a control means, and a means for adjusting the temperature and airflow for multiple zones.

[0182] System Configuration

[0183] Detection Methods:

[0184] Motion sensors installed in each zone of the store detect customer movement, allowing information on the presence of people in each zone and environmental data (temperature, humidity, etc.) to be collected in real time.

[0185] Information processing means:

[0186] The data obtained from these sensors is sent to a server, which processes the data in real time and prepares it for input into the generative AI model. Specific data processing includes adding timestamps and converting environmental data formats.

[0187] Generative AI model means:

[0188] The server generates optimal air conditioning settings based on the collected data using a generative AI model that parses and processes prompts based on specific time of day and environmental conditions to ensure user comfort while maximizing energy efficiency.

[0189] Communication Method:

[0190] The generated air conditioning settings are sent to the air conditioning equipment via a communication method, such as Wi-Fi or a wired network.

[0191] Control means:

[0192] The air conditioning equipment adjusts the temperature and airflow based on the received settings. Specifically, each air conditioning equipment operates automatically to maintain optimal air conditioning in each zone.

[0193] Program processing

[0194] The program of this system performs processing in the following manner.

[0195] 1. Data Collection:

[0196] Motion sensors detect customer movements in each zone of the store and send the data to a server.

[0197] 2. Data preprocessing:

[0198] The server formats the received data, adds a timestamp, and organizes the environmental data.

[0199] 3. Input to the generative AI model:

[0200] The server inputs the preprocessed data into a generative AI model, which analyzes the prompt and calculates the optimal air conditioning settings. For example,

[0201] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[0202] 4. Send results:

[0203] The generated air conditioning settings are sent from the server to the air conditioning equipment.

[0204] 5. Air conditioning control:

[0205] The air conditioning equipment automatically adjusts the temperature and airflow according to the received settings. For example, if the store is crowded in the afternoon, the temperature will be set to 22 degrees.

[0206] Specific examples

[0207] For example, if the store starts to get busy at 2 p.m., the motion sensor sends this information to the server. The server automatically uses a generative AI model to calculate the temperature settings for each zone. The server then sends the calculated settings to the air conditioners, which adjust the temperature and airflow for each zone. This minimizes energy consumption while maintaining a comfortable environment for customers and employees.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] The detection means collects information on people's movements and presence in each zone, as well as environmental data (temperature, humidity, etc.) in real time. The input is the physical movement of people and environmental information from the detection means (human presence sensors). The output is the collected data (sensor information with a timestamp).

[0211] Step 2:

[0212] The server converts the sensor information received from the detection means into a specified format and adds a timestamp. The input is the sensor information. The output is formatted data (in a format that can be saved in a database).

[0213] Step 3:

[0214] The server prepares the formatted data for input to the generative AI model. Specifically, it combines environmental data (temperature, humidity) and human movement data and converts it into a format that the generative AI model can understand. The input is the formatted data. The output is the input data for the generative AI model.

[0215] Step 4:

[0216] The server sends prompts containing real-time sensor information and environmental data to the generative AI model. For example,

[0217] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[0218] The input is a prompt and sensor information, and the output is the optimal air conditioning settings from a generative AI model.

[0219] Step 5:

[0220] The generative AI model analyzes the prompt text and sensor information, calculates the optimal air conditioning settings (temperature, air volume, operation mode) for each zone, and returns the results to the server. The input is the prompt text to the generative AI model. The output is the optimal air conditioning settings.

[0221] Step 6:

[0222] The server sends the optimal air conditioning settings returned by the generative AI model to the air conditioning equipment. Specifically, it sends instructions to the air conditioning equipment using a communication method. The input is the optimal air conditioning settings. The output is a control instruction to the air conditioning equipment.

[0223] Step 7:

[0224] The air conditioning equipment automatically adjusts the temperature and airflow based on the received optimal air conditioning settings. This optimizes the temperature and airflow for each zone in real time. The input is the control command from the server. The output is the adjusted temperature and airflow for each zone.

[0225] Step 8:

[0226] The user checks that the climate control settings are set appropriately to maintain comfort, and can make manual adjustments if necessary. The input is user feedback. The output is additional setting adjustments.

[0227] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0228] This invention is a system that combines a human presence sensor and an emotion engine to optimize air conditioner settings using generative AI. The purpose of this system is to minimize energy consumption while increasing comfort, taking into account the user's emotional state.

[0229] System Overview

[0230] Initialization Phase

[0231] server:

[0232] The server loads the generative AI model and initializes the system. It also loads the emotion engine so that it can process user emotion data. It also sets up interfaces to receive data from various sensors and devices, and initializes the system log to prepare for recording operation status.

[0233] Device:

[0234] Human sensors and sensors that capture the user's facial expressions and voice are installed in appropriate locations in the room, allowing for real-time detection of human movements and emotional states.

[0235] Acquiring and Sending Data

[0236] Device:

[0237] The motion sensor detects the user's movements. At the same time, the emotion engine analyzes the user's facial expressions and voice to extract emotional data. This information is then packaged into data packets along with a timestamp and sent to the server.

[0238] server:

[0239] The server receives data packets sent from the device and logs the data. The received data includes the user's movements, environmental data (temperature and humidity), and emotional state.

[0240] Processing by generative AI

[0241] server:

[0242] The received data is input into the generation AI, which uses this data to calculate the optimal air conditioner settings taking into account the user's emotional state. The calculation results in the air conditioner's temperature setting, airflow setting, operation mode, etc.

[0243] Examples:

[0244] For example, if the user has a tired expression, the AI ​​generator will set a relaxing temperature and airflow rate. This setting information is sent to the device, and the air conditioner settings are automatically changed.

[0245] Air conditioning control

[0246] Device:

[0247] Receives the air conditioner setting information sent from the server, and then generates specific commands to adjust the air conditioner settings based on that information.

[0248] Examples:

[0249] If the user is feeling stressed before going to bed at night, the emotion engine will detect this and the AI ​​generator will instruct the air conditioner to adjust the temperature to a relaxing 23 degrees. The device will then send this instruction to the air conditioner, changing the setting to 23 degrees.

[0250] Example of operation

[0251] Morning Scenario

[0252] User:

[0253] In the morning, I enter the living room.

[0254] Device:

[0255] A motion sensor installed in the room detects the user's movements, and an emotion engine analyzes their emotional state from their facial expressions.

[0256] server:

[0257] It receives data from sensors and emotion engines and feeds it into generative AI.

[0258] Generation AI:

[0259] Based on the morning state and emotional data, it generates instructions to set the temperature in the living room to 20 degrees.

[0260] Device:

[0261] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[0262] Night Scenario

[0263] User:

[0264] At night, go into your bedroom before going to sleep.

[0265] Device:

[0266] A motion sensor installed in the bedroom detects the user's movements, and an emotion engine analyzes the user's stress level.

[0267] server:

[0268] It receives data from sensors and emotion engines and feeds it into generative AI.

[0269] Generation AI:

[0270] Based on the time of night and emotional data, instructions are generated to set the air conditioner to a relaxing 23 degrees.

[0271] Device:

[0272] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[0273] In this way, the system of the present invention uses an emotion engine and generative AI to optimize air conditioner settings, thereby achieving efficient use of energy resources and improving user comfort.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] Server: Loads the generative AI model and emotion engine, initializes the system, sets up interfaces to receive data from various sensors and devices, and initializes the system log.

[0277] Step 2:

[0278] Terminal: Activate the human presence sensor and emotion sensor installed in the room. The human presence sensor detects the user's movements, and the emotion sensor collects emotion data from the user's facial expressions and voice.

[0279] Step 3:

[0280] Terminal: The motion sensor detects the user's movements, and the emotion sensor analyzes the user's emotional state. This data, along with environmental data (temperature, humidity), is formed into a data packet, which is then time-stamped and sent to the server.

[0281] Step 4:

[0282] Server: Receives and logs data packets sent from the device, including user movement, environmental data, and emotional data.

[0283] Step 5:

[0284] Server: Inputs the received data into the Generative AI and Emotion Engine. The Generative AI calculates the optimal air conditioning settings based on the user's movements and emotional data. The Emotion Engine adjusts the settings output by the Generative AI based on the user's emotional state.

[0285] Step 6:

[0286] Server: Receives the output from the generation AI and the adjustment results from the emotion engine, and creates operating instructions for the air conditioner. It generates instructions including specific settings (temperature, air volume, operation mode) and sends them to the terminal.

[0287] Step 7:

[0288] Server: Sends the generated air conditioner setting instructions to the device. These instructions contain details of how the air conditioner should be set up.

[0289] Step 8:

[0290] Terminal: Receives air conditioner setting instructions sent from the server. Based on that information, it generates specific commands to adjust the air conditioner settings.

[0291] Step 9:

[0292] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the air conditioner temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[0293] Step 10:

[0294] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, it starts collecting data from the sensors again to prepare for the next cycle.

[0295] Specific examples

[0296] Morning Scenario

[0297] Step 1:

[0298] User: In the morning, I walk into the living room.

[0299] Step 2:

[0300] Terminal: A motion sensor installed in the room detects the user's movements, and an emotion sensor analyzes the user's face.

[0301] Step 3:

[0302] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[0303] Step 4:

[0304] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[0305] Step 5:

[0306] Generative AI: Calculates the optimal temperature setting (e.g., 20 degrees) based on the time of day and the living room conditions. If the emotion engine determines that the user is in a relaxed state, it will fine-tune the temperature setting.

[0307] Step 6:

[0308] Server: Sends the generated air conditioner setting (temperature 20 degrees) to the device.

[0309] Step 7:

[0310] Terminal: Sends instructions to the air conditioner to set the temperature to 20 degrees.

[0311] Night Scenario

[0312] Step 1:

[0313] User: Enters bedroom at night before going to sleep.

[0314] Step 2:

[0315] Device: A motion sensor installed in the bedroom detects the user's movements, and an emotion sensor analyzes the user's face.

[0316] Step 3:

[0317] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[0318] Step 4:

[0319] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[0320] Step 5:

[0321] Generative AI: Calculates the optimal temperature setting (e.g., 23°C) based on the time of night and the bedroom conditions. If the emotion engine determines that the user is stressed, it will fine-tune the temperature setting.

[0322] Step 6:

[0323] Server: Sends the generated air conditioner setting (temperature 23 degrees) to the device.

[0324] Step 7:

[0325] Terminal: Sends instructions to the air conditioner to set the temperature to 23 degrees.

[0326] Example 2

[0327] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0328] Conventional air conditioning control systems simply set air conditioning settings based on temperature and humidity without considering the user's emotional state, which can result in insufficient user comfort. Furthermore, they are often not optimized for energy efficiency. Therefore, there is a need for an air conditioning control system that achieves both comfort and energy efficiency while also taking the user's emotional state into account.

[0329] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis means for analyzing the emotional state of the user, an AI generation means for balancing minimizing the air conditioner's power consumption and user comfort, and a central processing unit for processing information including timestamps and environmental data. This enables optimal air conditioner settings that reflect the user's emotional state in real time.

[0330] The "sensor means for detecting human movement" is a device for detecting the movement of a user in a room in real time.

[0331] "Central Processing Unit Means" means a computer system for receiving, analyzing, and processing information from sensor means and other input devices.

[0332] "Generative AI means" is an artificial intelligence technology that calculates and generates optimal air conditioning settings based on received data.

[0333] The "communication terminal means" is a device or system for transmitting the air conditioner settings generated by the generation AI means to the air conditioner.

[0334] The "control device means" is a control unit for controlling the air conditioner based on the air conditioner settings received from the communication terminal means.

[0335] The "emotion analysis means" refers to software and hardware for analyzing the user's facial expressions and voice data to extract the user's emotional state.

[0336] A "timestamp" is information that indicates the date and time when data was recorded or transmitted.

[0337] "Environmental data" is information indicating environmental parameters such as indoor temperature and humidity.

[0338] The present invention proposes a system that analyzes a person's emotional state and optimizes air conditioner settings based on the results. Specific embodiments of the system will be described in detail below.

[0339] Initialization Phase

[0340] server:

[0341] The server first loads a generative AI model. This model incorporates an algorithm that analyzes the user's emotional data and uses the results to determine optimal air conditioning settings. Generative AI models are often implemented using the TensorFlow library. The server then loads an emotion engine, which is responsible for extracting the user's emotional state from facial expressions and voice data. The server also sets up interfaces for receiving data from various sensors and devices, and initializes the system log.

[0342] Device:

[0343] The device is configured to install motion sensors, cameras, and microphones in appropriate locations in the room, enabling real-time detection of people's movements and emotional states. For example, motion sensors are installed in the four corners of the room, cameras on the ceiling, and microphones on the desk. The device checks the operation of these sensors to ensure they are working properly.

[0344] Acquiring and Sending Data

[0345] Device:

[0346] A motion sensor installed on the device detects the user's movements. This data is sent to the device as user movement detection data. At the same time, a camera and microphone capture the user's facial expressions and voice, which the emotion engine analyzes to extract emotional data. For example, if the user is tired, the emotion engine outputs "fatigue" as the emotional data. This data is compiled into a single data packet along with a timestamp and sent to the server.

[0347] server:

[0348] The server receives data packets sent from the device and records them in the system log. The data packets include the user's emotional state based on their movements, facial expressions, and voice data, as well as environmental data (temperature and humidity). For example, the received data is recorded as follows:

[0349] 2023-10-01T08:30:00Z - Motion: Detected, Emotion: Fatigue, Temperature: 22°C, Humidity: 45%

[0350] Processing by generative AI

[0351] server:

[0352] The server inputs the received data into a generative AI model, which then uses this data to calculate optimal air conditioning settings that take into account the user's emotional state and environmental data. For example, if the user is tired, the generative AI model calculates a relaxing temperature of 24 degrees and a medium airflow setting.

[0353] Examples:

[0354] As an example of a prompt, the following text is fed into the generative AI model:

[0355] The user is currently tired and needs to adjust the temperature setting to 24 degrees and the fan speed to medium.

[0356] Air conditioning control

[0357] Device:

[0358] The device receives the air conditioner setting information sent from the server. Based on that information, the device generates specific commands to adjust the air conditioner settings. For example, it generates commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID and sends them to the air conditioner.

[0359] Examples:

[0360] When a user enters their bedroom at night before going to sleep, the motion sensor detects movement and the emotion engine detects "stress." The AI ​​then calculates a relaxing temperature of 23 degrees and sends a command to the air conditioner on the device. As a result, the air conditioner is set to 23 degrees, providing the user with the optimal environment.

[0361] In this way, the system of the present invention can reflect the user's emotional state in real time and achieve optimal air conditioning settings, maximizing energy efficiency while providing a comfortable environment.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] Initialize the server:

[0365] The server first loads the generative AI model using the TensorFlow library. Next, it loads the emotion engine, which functions as an emotion analysis means, enabling it to analyze the user's facial expressions and voice data. It also sets up interfaces for receiving data from various sensors and devices. Finally, it initializes the system log, preparing to record the system's operating status.

[0366] Input: Generative AI model, emotion engine

[0367] Output: Loaded model and engine, set up receiving interface

[0368] Step 2:

[0369] Initialize the device:

[0370] The device installs a motion sensor, camera, and microphone in appropriate locations in the room. For example, motion sensors are installed in the four corners of the room, a camera on the ceiling, and a microphone on the desk. Next, the operation of these sensors is checked to ensure they are working properly.

[0371] Input: Motion sensor, camera, microphone

[0372] Output: Installed sensors and operation check report

[0373] Step 3:

[0374] Data acquisition by device:

[0375] A motion sensor installed on the device detects the user's movements. At the same time, a camera and microphone capture the user's facial expressions and voice. This allows the user's movement data, facial expression data, and voice data to be acquired.

[0376] Input: User movements, facial expressions, and voice

[0377] Output: Movement data, facial expression data, audio data

[0378] Step 4:

[0379] Data sent by the device:

[0380] The emotion analysis means analyzes the acquired facial expression data and voice data to extract emotion data. The extracted emotion data, movement data, and environmental data (temperature and humidity) are compiled into a single data packet along with a timestamp and sent to the server.

[0381] Input: movement data, facial expression data, audio data, environmental data

[0382] Output: Data packet (including emotion data, movement data, and environment data)

[0383] Step 5:

[0384] Server receives data:

[0385] The server receives data packets sent from the terminal, records the received data in the system log, and prepares it for analysis.

[0386] Input: Data packet

[0387] Output: Logged data

[0388] Step 6:

[0389] Data input to the server-generated AI model:

[0390] The server inputs the received data into the generative AI model. For example, it inputs a prompt sentence based on the contents of the data packet into the generative AI model.

[0391] Input: Data packet (emotion data, movement data, environmental data)

[0392] Output: Input data to the generative AI model

[0393] Step 7:

[0394] Generative AI model calculates air conditioning settings:

[0395] The generative AI model calculates optimal air conditioning settings based on input data. For example, if the user is tired, it will set the temperature to 24 degrees and the fan speed to medium, which is considered relaxing.

[0396] Input: Input data to the generative AI model

[0397] Output: Calculated air conditioner settings (temperature, airflow, operation mode)

[0398] Step 8:

[0399] Server sends configuration instructions:

[0400] The server sends the calculation results to the device, which then generates specific commands to adjust the air conditioner settings.

[0401] Input: Calculated air conditioner settings

[0402] Output: Configuration instructions to the terminal

[0403] Step 9:

[0404] Applying the settings via terminal:

[0405] The terminal generates control commands for the air conditioner based on the received setting information. The generated commands are sent to the air conditioner to change the settings. For example, commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID are sent to the air conditioner.

[0406] Input: Setting instructions (temperature, air volume, operation mode)

[0407] Output: Air conditioning control command

[0408] In this way, the system operates continuously, automatically adjusting air conditioning settings to reflect the user's emotional state, providing a comfortable environment.

[0409] (Application example 2)

[0410] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0411] In brick-and-mortar stores, air conditioning settings are important for improving customer comfort. However, it has been difficult with conventional technology to grasp the preferences and emotional state of each customer in real time and set the optimal temperature. Furthermore, adjusting the temperature to maintain comfort while minimizing energy consumption is time-consuming and inefficient.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a human presence sensor means for detecting human movement, an emotion analysis means for analyzing the emotional state of visitors and generating emotion data, and an AI generation means for generating optimal air conditioner settings based on data received by the network means. This makes it possible to grasp the emotional state and movement of visitors in real time and set the air conditioner optimally. Furthermore, it is possible to achieve both improved comfort for visitors and more efficient power consumption.

[0413] The "human sensor means" is a sensor device for detecting human movement.

[0414] "Network Means" means the communications infrastructure for receiving and transmitting data from sensors and other devices.

[0415] The "generative AI means" is an artificial intelligence system that generates optimal air conditioning settings based on the data it receives.

[0416] The "emotion analysis means" is a device or system for analyzing the facial expressions and voices of customers and generating emotion data.

[0417] The "terminal means" is a device for transmitting the generated air conditioner settings to the air conditioner.

[0418] "Air conditioner control means" refers to a control system for regulating the operation of an air conditioner.

[0419] "Operational means" refers to the means for operating the system with the aim of improving the comfort of customers and streamlining power consumption.

[0420] A "timestamp" is information that indicates the date and time when data was generated or recorded.

[0421] "Environmental data" is information relating to the surrounding environmental conditions such as temperature and humidity.

[0422] To implement this invention, it is necessary to build a system that can balance customer comfort and energy efficiency in a physical store. This system mainly uses the following hardware and software:

[0423] Hardware:

[0424] 1. Human presence sensor: A device that detects human movement within the store.

[0425] 2. Camera and microphone: Devices that capture customers' facial expressions and voices.

[0426] 3. Air conditioner: The air conditioner itself is responsible for adjusting the temperature and airflow according to the system's instructions.

[0427] software:

[0428] 1. Generative AI model: An artificial intelligence system that calculates and generates optimal air conditioner settings based on received data.

[0429] 2. Emotion analysis engine: Generates emotional data from customers' facial expressions and voices.

[0430] 3. Network communication software: This is the communication software used to send data from sensors and analysis engines to the server.

[0431] System operation description:

[0432] The server first loads the generative AI model and emotion analysis engine, which prepares the system for analyzing the emotional state and behavior of customers in real time. Next, motion sensors, cameras, and microphones are installed in appropriate locations within the store, preparing to capture both people's behavior and emotion data in real time.

[0433] The sensor device and analysis engine generate data packets with timestamps based on the detected and analyzed data, which are then sent to a server. The server analyzes the received data and calculates the optimal settings for the air conditioner (temperature, airflow, operation mode, etc.).

[0434] The generated air conditioner settings are sent to the air conditioner via the terminal means, and the air conditioner control means then adjusts the air conditioner based on these settings, ensuring the comfort of customers while also achieving efficient power consumption.

[0435] Examples:

[0436] For example, if there are many customers at lunchtime, the camera and microphone will detect the facial expressions and voices of customers who feel it is hot. Based on this information, the AI ​​will suggest setting the air conditioner temperature to 22 degrees as the optimal setting, and that setting will be reflected on the air conditioner.

[0437] At night, when there are only a few customers, the emotion analysis engine will analyze the relaxed state, and the generative AI will suggest setting the air conditioner to a relaxing temperature (for example, 25 degrees). This setting will also be immediately reflected in the air conditioner.

[0438] Example prompt sentence:

[0439] The prompt for the generative AI model is:

[0440] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[0441] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0442] Step 1:

[0443] The server loads the generative AI model and the sentiment analysis engine. These models are required to analyze the emotional state of customers in real time and generate optimal air conditioning settings. The model file of the sentiment analysis engine and the data of the generative AI model are taken as input, and these are made available as output.

[0444] Step 2:

[0445] The terminal is installed with motion sensors, cameras, and microphones in appropriate locations within the physical store, allowing it to acquire both customer movement and emotional data in real time. The input is information about the installation location of the sensors, cameras, and microphones, and the output is information indicating that installation is complete and the sensor is ready to operate.

[0446] Step 3:

[0447] The motion sensor detects the movement of customers and acquires timestamps and environmental data (current temperature and humidity). This data is packetized as sensor information and sent to the server. The input is raw data from the sensor, and the output is data packets and transmission status.

[0448] Step 4:

[0449] The emotion analysis engine analyzes the facial expressions and voices of customers captured through the camera and microphone to generate emotion data. This data is also packetized with a timestamp and sent to the server. The input is video and audio data, and the output is emotion data packets and transmission status.

[0450] Step 5:

[0451] The server receives data packets sent from the motion sensor and emotion analysis engine and logs them, with the data packets as input and the recorded log data and the data readiness status as output.

[0452] Step 6:

[0453] The generative AI model calculates optimal air conditioner settings based on data stored on the server. Specifically, it analyzes the user's emotions and environmental conditions in the form of prompt sentences based on the emotional and environmental data received as input, and obtains the optimal air conditioner temperature, airflow, and operation mode settings as output.

[0454] Step 7:

[0455] The server sends the air conditioner setting information calculated by the generative AI model to the terminal. The input includes the calculation result of the generative AI model, and the output includes the transmission status and transmission completion notification to the terminal.

[0456] Step 8:

[0457] The terminal adjusts the settings of the air conditioner through the air conditioner control means based on the air conditioner setting information received from the server. The input is the air conditioner setting information, and the output is the adjustment completion and the current setting status of the air conditioner.

[0458] Example prompt sentence:

[0459] The prompt for the generative AI model is:

[0460] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[0461] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0462] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0463] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0464] [Second embodiment]

[0465] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0466] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0467] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0468] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0469] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0470] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0471] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0472] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0473] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0474] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0475] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0476] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0477] This invention is a system that optimizes the operation of an air conditioner by combining a human sensor, a server, a generating AI, a terminal, and an air conditioner control means. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[0478] System Overview

[0479] Initialization Phase

[0480] server:

[0481] The server loads the generative AI model, initializes interfaces to receive data from various sensors and devices, and initializes logs to prepare for recording the system's operating status.

[0482] Device:

[0483] The sensors are set up to work properly, especially the motion sensors, which are placed in specific locations in the room and calibrated to detect human movement.

[0484] Acquiring and Sending Data

[0485] Device:

[0486] The motion sensor detects the user's movements in real time, while simultaneously recording environmental data (such as temperature and humidity). This data is then sent to the server at regular intervals.

[0487] server:

[0488] The server receives the data sent from the device and prepares it for input into the generative AI, including timestamps of detected movements and environmental data.

[0489] Processing by generative AI

[0490] server:

[0491] The server inputs the received data into the AI ​​generator, which then calculates the optimal air conditioner settings based on that data. The resulting settings include the air conditioner temperature, airflow, and operating mode.

[0492] Examples:

[0493] For example, if the motion sensor detects user movement in the morning, the generated AI will know that the room temperature is low at that time and change the air conditioner setting to 20 degrees to make the user comfortable. This setting information is then sent to the device.

[0494] Air conditioning control

[0495] Device:

[0496] Receives air conditioner setting information sent from the server. After receiving the information, changes the air conditioner settings according to the information. Specific setting changes include temperature adjustment, air volume adjustment, and operation mode change.

[0497] Examples:

[0498] If the motion sensor detects movement in the bedroom at night, the AI ​​will use that data to instruct the device to change the air conditioner setting to 23 degrees. The device will then adjust the air conditioner setting to 23 degrees.

[0499] Example of operation

[0500] Morning Scenario

[0501] User:

[0502] In the morning, I enter the living room.

[0503] Device:

[0504] A motion sensor installed in the room detects the user's movements.

[0505] server:

[0506] The server receives data from the sensors and inputs it into the generation AI.

[0507] Generation AI:

[0508] Based on the condition "morning" and other environmental data, the generating AI generates instructions to set the temperature in the living room to 20 degrees.

[0509] Device:

[0510] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[0511] Night Scenario

[0512] User:

[0513] At night, go into your bedroom before going to sleep.

[0514] Device:

[0515] A motion sensor installed in the bedroom detects the user's movements.

[0516] server:

[0517] The server receives data from the sensors and inputs it into the generation AI.

[0518] Generation AI:

[0519] Based on the condition "night" and other environmental data, the generating AI generates instructions to set the air conditioner to 23 degrees.

[0520] Device:

[0521] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[0522] In this way, the system of the present invention collects sensor data in real time and utilizes generative AI to optimize air conditioner operation, thereby achieving both efficient use of energy resources and user comfort.

[0523] The processing flow will be explained below.

[0524] Step 1:

[0525] Server: Loads the generative AI model and initializes the system. It loads the model into memory to allow the generative AI to calculate optimal air conditioner settings, and sets up interfaces to receive data from various sensors and devices.

[0526] Step 2:

[0527] Terminal: Detects user movement through a motion sensor, which also records environmental data (temperature and humidity) at specific intervals and forms this information into a data packet.

[0528] Step 3:

[0529] Terminal: The terminal creates a data packet, adds a timestamp to it, and sends it to the server. This information includes the person's movement, current temperature, and humidity.

[0530] Step 4:

[0531] Server: Receives data packets sent from the device, logs the received data, and formats the data for processing by the generation AI.

[0532] Step 5:

[0533] Server: Inputs the formatted data into the AI ​​generator, which calculates the optimal air conditioning settings based on the received data.

[0534] Step 6:

[0535] Server: Receives the output from the generation AI and creates operating instructions for the air conditioner. It creates instructions including specific settings (temperature, airflow, operation mode).

[0536] Step 7:

[0537] Server: Sends the generated air conditioner setting instructions to the terminal. These instructions indicate how to change the air conditioner settings.

[0538] Step 8:

[0539] Terminal: Receives instructions for air conditioner settings from the server. Based on this information, it generates specific commands to adjust the air conditioner settings.

[0540] Step 9:

[0541] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[0542] Step 10:

[0543] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, starts collecting data from the sensors again to prepare for the next cycle.

[0544] The above is the specific program processing flow for implementing the invention. This system detects user movements in real time and uses generative AI to optimize air conditioner settings, aiming to achieve both efficient use of energy resources and a comfortable living environment.

[0545] Example 1

[0546] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0547] In modern air conditioning systems, minimizing energy consumption while maximizing user comfort is a critical issue. Existing systems generally rely on manual settings or timer functions, making it difficult to achieve optimal control that adapts to user movements and environmental conditions. Furthermore, air conditioner power consumption is often not effectively managed. Therefore, a new air conditioning system that can achieve both user comfort and energy efficiency is needed.

[0548] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0549] In this invention, the server includes a detection means, a receiving means, a generating means, a transmitting means, and a control means, which enables real-time detection of human movement and optimization of air conditioning settings using a generative AI model based on the data, thereby achieving a balance between minimizing power consumption and comfort.

[0550] - "Detection means" means a sensor device for detecting human movement.

[0551] The "receiving means" is a function or device for receiving data from the detecting means.

[0552] The "generation means" refers to a generation AI model and its execution environment for generating air conditioning settings based on the data acquired by the receiving means.

[0553] The "transmission means" is a function or device for transmitting the air conditioning settings generated by the generation means to the air conditioner.

[0554] The "control means" is a function or device for controlling the air conditioner based on the air conditioning settings received from the transmission means.

[0555] A "timestamp" is data that indicates a specific time or date, and is used to record the occurrence time of a detected event or data.

[0556] "Environmental data" refers to data that indicates the surrounding environmental conditions such as temperature and humidity.

[0557] This invention is a system that combines a motion sensor, a server, a generative AI model, a terminal, and an air conditioner to optimize the operation of the air conditioner. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[0558] Initialization Phase

[0559] First, the server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model into memory using TensorFlow or PyTorch. Next, the server initializes an interface for receiving data from multiple sensors and devices. This is done by building a REST API using the Python Flask framework. The server also prepares to use Logstash or Elasticsearch for log management to record the system's operating status.

[0560] The device is set up to ensure the sensor works properly. For example, a Raspberry Pi or Arduino is used, and a PIR motion sensor is connected to the GPIO pin to check operation. The motion sensor is installed on the ceiling or wall of the room and angled to accurately detect user movement.

[0561] Acquiring and Sending Data

[0562] The device equipped with a motion sensor detects user movement in real time. In addition, it simultaneously records environmental data (temperature and humidity) using a DHT22 temperature and humidity sensor. This data is sent to the server at regular intervals. The data is sent using an HTTP POST request.

[0563] The server receives data sent from the device. Using the Flask framework, the received data includes timestamps of the user's movements and environmental data (temperature and humidity). The server preprocesses this data and prepares it for input to the generative AI. Specifically, it converts the data from JSON format to the input format of the generative AI.

[0564] Processing by generative AI

[0565] The server inputs the received data into a generative AI model, which then calculates the optimal air conditioning settings based on this data. The calculation results in the air conditioning unit's temperature settings, airflow settings, operating mode, etc.

[0566] For example, if a motion sensor detects user movement in the morning and the current room temperature is 22 degrees, the generative AI model will instruct the air conditioner to change the setting to 20 degrees. This calculation result is sent to the device.

[0567] Air conditioning control

[0568] The terminal receives the air conditioning setting information sent from the server. After receiving the information, it changes the air conditioning unit settings based on that information. Specifically, this includes adjusting the temperature, airflow, and operating mode. To do this, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate signals to the air conditioning unit.

[0569] Examples of concrete examples and prompts

[0570] In the morning scenario, when a user enters the living room, the device detects the user's movement and sends that data, along with environmental data, to the server. The server inputs the data into the generative AI model and calculates the optimal air conditioning settings. For example, if the room temperature is 22 degrees at 7 a.m., the generative AI will set the air conditioning temperature to 20 degrees. The device that receives this instruction will set the air conditioning temperature to 20 degrees.

[0571] In the night scenario, the same process is repeated when the user enters the bedroom. Based on the "night" condition and other environmental data, the generative AI generates an instruction to set the air conditioner to 23 degrees. The device receives this instruction and adjusts the air conditioner temperature to 23 degrees.

[0572] Example prompt sentence:

[0573] "Optimize air conditioning settings when someone enters the room. Current conditions are 22 degrees and the time is 7am."

[0574] As a result, the system of the present invention collects sensor data in real time and uses generative AI to optimize the operation of air conditioning equipment, thereby achieving both efficient energy consumption and user comfort.

[0575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0576] Step 1:

[0577] The server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model using TensorFlow or PyTorch into memory. This is done during the initialization phase of the program. The input is the trained data for the model, and the output is a ready-to-run generative AI model.

[0578] Step 2:

[0579] The server initializes the data receiving interface. This is achieved by building a REST API using the Python Flask framework. The input is the interface configuration information, and the output is the endpoint for receiving data.

[0580] Step 3:

[0581] The server initializes the logs and prepares to record system activity. Specifically, a log analysis tool such as Logstash or Elasticsearch is used. The input is a log configuration file, and the output is a log management system ready to record system activity.

[0582] Step 4:

[0583] The terminal sets up the sensor and adjusts the position of the motion sensor. Using Raspberry Pi or Arduino, connect the PIR motion sensor to the GPIO pin and check its operation. The input is the sensor installation information, and the output is a working sensor system.

[0584] Step 5:

[0585] The device detects user movements in real time through a motion sensor. Specifically, when the sensor detects movement, the Raspberry Pi records this data. The input is the user's movement, and the output is the movement detection data.

[0586] Step 6:

[0587] The device also simultaneously records environmental data and sends it to the server at regular intervals. Temperature and humidity are measured using the DHT22 and sent to the server via an HTTP POST request along with PIR motion sensor data. The input is temperature, humidity, and motion detection data, and the output is the sensor data sent to the server.

[0588] Step 7:

[0589] The server receives data sent from the device. It uses Flask as the framework and receives data at the receiving endpoint. The input is the transmitted sensor data, and the output is the received data.

[0590] Step 8:

[0591] The server preprocesses the received data and prepares it for input to the generative AI. It converts the JSON formatted data into a format that the generative AI model can accept. The input is the received JSON data, and the output is the input data for the generative AI.

[0592] Step 9:

[0593] The server inputs data into the generative AI, which then calculates the optimal air conditioning settings. The generative AI model calculates the optimal temperature settings, airflow settings, and operation mode based on the input data. The input is processed environmental data and motion detection data, and the output is air conditioning setting information.

[0594] Step 10:

[0595] The server sends the generated air conditioning setting information to the terminal. The setting information obtained from the generative AI model is converted to JSON format and sent to the terminal. The input is the generated air conditioning setting information, and the output is the setting instructions sent to the terminal.

[0596] Step 11:

[0597] The terminal receives the air conditioning setting information sent from the server. The terminal receives the setting information and stores it in its internal memory. The input is the air conditioning setting information from the server, and the output is the recorded setting information.

[0598] Step 12:

[0599] The terminal changes the air conditioner settings based on the received information. Specifically, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate infrared signal to the air conditioner. The input is the saved air conditioner setting information, and the output is the air conditioner with the changed setting.

[0600] (Application example 1)

[0601] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0602] Maintaining the comfort of customers and employees in real-world spaces such as stores while minimizing the energy consumption of air conditioning equipment is a difficult challenge. This becomes particularly complex when different temperatures and airflow volumes need to be adjusted in multiple zones within a store. Conventional air conditioning systems have difficulty responding to these real-time conditions, which can result in energy waste and customer discomfort.

[0603] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0604] In this invention, the server includes a detection means for detecting human movement, an information processing means for receiving data from the detection means, a generation AI model means for generating optimal air conditioning settings based on the data received by the information processing means, a communication means for transmitting the air conditioning settings generated by the generation AI model means to air conditioning equipment, a control means for controlling the air conditioning equipment, and a means for adjusting the temperature and air volume of multiple zones based on the data. This makes it possible to maintain optimal air conditioning settings in real time in multiple zones within a store, ensuring the comfort of customers and employees while minimizing energy consumption.

[0605] The "detection means" is a sensor device for detecting human movement.

[0606] The "information processing means" is a server or computer system that aggregates data received from the detection means and inputs it into the generative AI model means.

[0607] "Generative AI model means" refers to an artificial intelligence model that calculates optimal air conditioning settings based on data received by the information processing means.

[0608] "Communication means" refers to the network interface and communication protocol used to transmit the air conditioning settings generated by the generation AI model means to the air conditioning equipment.

[0609] "Control means" refers to a system or device for operating and adjusting air conditioning equipment based on air conditioning settings received from communication means.

[0610] "Multiple zones" refers to different areas or sections within a store or other physical space.

[0611] "Real-time" means that data is collected and processed immediately, and air conditioning settings are adjusted immediately based on the results.

[0612] "Minimizing energy consumption" means keeping the energy required to operate air conditioning equipment as low as possible.

[0613] "Comfort" refers to the quality of the indoor environment that keeps customers and employees comfortable.

[0614] A system for realizing this application example includes a detection means, an information processing means, a generative AI model means, a communication means, a control means, and a means for adjusting the temperature and airflow for multiple zones.

[0615] System Configuration

[0616] Detection Methods:

[0617] Motion sensors installed in each zone of the store detect customer movement, allowing information on the presence of people in each zone and environmental data (temperature, humidity, etc.) to be collected in real time.

[0618] Information processing means:

[0619] The data obtained from these sensors is sent to a server, which processes the data in real time and prepares it for input into the generative AI model. Specific data processing includes adding timestamps and converting environmental data formats.

[0620] Generative AI model means:

[0621] The server generates optimal air conditioning settings based on the collected data using a generative AI model that parses and processes prompts based on specific time of day and environmental conditions to ensure user comfort while maximizing energy efficiency.

[0622] Communication Method:

[0623] The generated air conditioning settings are sent to the air conditioning equipment via a communication method, such as Wi-Fi or a wired network.

[0624] Control means:

[0625] The air conditioning equipment adjusts the temperature and airflow based on the received settings. Specifically, each air conditioning equipment operates automatically to maintain optimal air conditioning in each zone.

[0626] Program processing

[0627] The program of this system performs processing in the following manner.

[0628] 1. Data Collection:

[0629] Motion sensors detect customer movements in each zone of the store and send the data to a server.

[0630] 2. Data preprocessing:

[0631] The server formats the received data, adds a timestamp, and organizes the environmental data.

[0632] 3. Input to the generative AI model:

[0633] The server inputs the preprocessed data into a generative AI model, which analyzes the prompt and calculates the optimal air conditioning settings. For example,

[0634] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[0635] 4. Send results:

[0636] The generated air conditioning settings are sent from the server to the air conditioning equipment.

[0637] 5. Air conditioning control:

[0638] The air conditioning equipment automatically adjusts the temperature and airflow according to the received settings. For example, if the store is crowded in the afternoon, the temperature will be set to 22 degrees.

[0639] Specific examples

[0640] For example, if the store starts to get busy at 2 p.m., the motion sensor sends this information to the server. The server automatically uses a generative AI model to calculate the temperature settings for each zone. The server then sends the calculated settings to the air conditioners, which adjust the temperature and airflow for each zone. This minimizes energy consumption while maintaining a comfortable environment for customers and employees.

[0641] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0642] Step 1:

[0643] The detection means collects information on people's movements and presence in each zone, as well as environmental data (temperature, humidity, etc.) in real time. The input is the physical movement of people and environmental information from the detection means (human presence sensors). The output is the collected data (sensor information with a timestamp).

[0644] Step 2:

[0645] The server converts the sensor information received from the detection means into a specified format and adds a timestamp. The input is the sensor information. The output is formatted data (in a format that can be saved in a database).

[0646] Step 3:

[0647] The server prepares the formatted data for input to the generative AI model. Specifically, it combines environmental data (temperature, humidity) and human movement data and converts it into a format that the generative AI model can understand. The input is the formatted data. The output is the input data for the generative AI model.

[0648] Step 4:

[0649] The server sends prompts containing real-time sensor information and environmental data to the generative AI model. For example,

[0650] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[0651] The input is a prompt and sensor information, and the output is the optimal air conditioning settings from a generative AI model.

[0652] Step 5:

[0653] The generative AI model analyzes the prompt text and sensor information, calculates the optimal air conditioning settings (temperature, air volume, operation mode) for each zone, and returns the results to the server. The input is the prompt text to the generative AI model. The output is the optimal air conditioning settings.

[0654] Step 6:

[0655] The server sends the optimal air conditioning settings returned by the generative AI model to the air conditioning equipment. Specifically, it sends instructions to the air conditioning equipment using a communication method. The input is the optimal air conditioning settings. The output is a control instruction to the air conditioning equipment.

[0656] Step 7:

[0657] The air conditioning equipment automatically adjusts the temperature and airflow based on the received optimal air conditioning settings. This optimizes the temperature and airflow for each zone in real time. The input is the control command from the server. The output is the adjusted temperature and airflow for each zone.

[0658] Step 8:

[0659] The user checks that the climate control settings are set appropriately to maintain comfort, and can make manual adjustments if necessary. The input is user feedback. The output is additional setting adjustments.

[0660] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0661] This invention is a system that combines a human presence sensor and an emotion engine to optimize air conditioner settings using generative AI. The purpose of this system is to minimize energy consumption while increasing comfort, taking into account the user's emotional state.

[0662] System Overview

[0663] Initialization Phase

[0664] server:

[0665] The server loads the generative AI model and initializes the system. It also loads the emotion engine so that it can process user emotion data. It also sets up interfaces to receive data from various sensors and devices, and initializes the system log to prepare for recording operation status.

[0666] Device:

[0667] Human sensors and sensors that capture the user's facial expressions and voice are installed in appropriate locations in the room, allowing for real-time detection of human movements and emotional states.

[0668] Acquiring and Sending Data

[0669] Device:

[0670] The motion sensor detects the user's movements. At the same time, the emotion engine analyzes the user's facial expressions and voice to extract emotional data. This information is then packaged into data packets along with a timestamp and sent to the server.

[0671] server:

[0672] The server receives data packets sent from the device and logs the data. The received data includes the user's movements, environmental data (temperature and humidity), and emotional state.

[0673] Processing by generative AI

[0674] server:

[0675] The received data is input into the generation AI, which uses this data to calculate the optimal air conditioner settings taking into account the user's emotional state. The calculation results in the air conditioner's temperature setting, airflow setting, operation mode, etc.

[0676] Examples:

[0677] For example, if the user has a tired expression, the AI ​​generator will set a relaxing temperature and airflow rate. This setting information is sent to the device, and the air conditioner settings are automatically changed.

[0678] Air conditioning control

[0679] Device:

[0680] Receives the air conditioner setting information sent from the server, and then generates specific commands to adjust the air conditioner settings based on that information.

[0681] Examples:

[0682] If the user is feeling stressed before going to bed at night, the emotion engine will detect this and the AI ​​generator will instruct the air conditioner to adjust the temperature to a relaxing 23 degrees. The device will then send this instruction to the air conditioner, changing the setting to 23 degrees.

[0683] Example of operation

[0684] Morning Scenario

[0685] User:

[0686] In the morning, I enter the living room.

[0687] Device:

[0688] A motion sensor installed in the room detects the user's movements, and an emotion engine analyzes their emotional state from their facial expressions.

[0689] server:

[0690] It receives data from sensors and emotion engines and feeds it into generative AI.

[0691] Generation AI:

[0692] Based on the morning state and emotional data, it generates instructions to set the temperature in the living room to 20 degrees.

[0693] Device:

[0694] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[0695] Night Scenario

[0696] User:

[0697] At night, go into your bedroom before going to sleep.

[0698] Device:

[0699] A motion sensor installed in the bedroom detects the user's movements, and an emotion engine analyzes the user's stress level.

[0700] server:

[0701] It receives data from sensors and emotion engines and feeds it into generative AI.

[0702] Generation AI:

[0703] Based on the time of night and emotional data, instructions are generated to set the air conditioner to a relaxing 23 degrees.

[0704] Device:

[0705] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[0706] In this way, the system of the present invention uses an emotion engine and generative AI to optimize air conditioner settings, thereby achieving efficient use of energy resources and improving user comfort.

[0707] The processing flow will be explained below.

[0708] Step 1:

[0709] Server: Loads the generative AI model and emotion engine, initializes the system, sets up interfaces to receive data from various sensors and devices, and initializes the system log.

[0710] Step 2:

[0711] Terminal: Activate the human presence sensor and emotion sensor installed in the room. The human presence sensor detects the user's movements, and the emotion sensor collects emotion data from the user's facial expressions and voice.

[0712] Step 3:

[0713] Terminal: The motion sensor detects the user's movements, and the emotion sensor analyzes the user's emotional state. This data, along with environmental data (temperature, humidity), is formed into a data packet, which is then time-stamped and sent to the server.

[0714] Step 4:

[0715] Server: Receives and logs data packets sent from the device, including user movement, environmental data, and emotional data.

[0716] Step 5:

[0717] Server: Inputs the received data into the Generative AI and Emotion Engine. The Generative AI calculates the optimal air conditioning settings based on the user's movements and emotional data. The Emotion Engine adjusts the settings output by the Generative AI based on the user's emotional state.

[0718] Step 6:

[0719] Server: Receives the output from the generation AI and the adjustment results from the emotion engine, and creates operating instructions for the air conditioner. It generates instructions including specific settings (temperature, air volume, operation mode) and sends them to the terminal.

[0720] Step 7:

[0721] Server: Sends the generated air conditioner setting instructions to the device. These instructions contain details of how the air conditioner should be set up.

[0722] Step 8:

[0723] Terminal: Receives air conditioner setting instructions sent from the server. Based on that information, it generates specific commands to adjust the air conditioner settings.

[0724] Step 9:

[0725] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the air conditioner temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[0726] Step 10:

[0727] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, it starts collecting data from the sensors again to prepare for the next cycle.

[0728] Specific examples

[0729] Morning Scenario

[0730] Step 1:

[0731] User: In the morning, I walk into the living room.

[0732] Step 2:

[0733] Terminal: A motion sensor installed in the room detects the user's movements, and an emotion sensor analyzes the user's face.

[0734] Step 3:

[0735] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[0736] Step 4:

[0737] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[0738] Step 5:

[0739] Generative AI: Calculates the optimal temperature setting (e.g., 20 degrees) based on the time of day and the living room conditions. If the emotion engine determines that the user is in a relaxed state, it will fine-tune the temperature setting.

[0740] Step 6:

[0741] Server: Sends the generated air conditioner setting (temperature 20 degrees) to the device.

[0742] Step 7:

[0743] Terminal: Sends instructions to the air conditioner to set the temperature to 20 degrees.

[0744] Night Scenario

[0745] Step 1:

[0746] User: Enters bedroom at night before going to sleep.

[0747] Step 2:

[0748] Device: A motion sensor installed in the bedroom detects the user's movements, and an emotion sensor analyzes the user's face.

[0749] Step 3:

[0750] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[0751] Step 4:

[0752] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[0753] Step 5:

[0754] Generative AI: Calculates the optimal temperature setting (e.g., 23°C) based on the time of night and the bedroom conditions. If the emotion engine determines that the user is stressed, it will fine-tune the temperature setting.

[0755] Step 6:

[0756] Server: Sends the generated air conditioner setting (temperature 23 degrees) to the device.

[0757] Step 7:

[0758] Terminal: Sends instructions to the air conditioner to set the temperature to 23 degrees.

[0759] Example 2

[0760] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0761] Conventional air conditioning control systems simply set air conditioning settings based on temperature and humidity without considering the user's emotional state, which can result in insufficient user comfort. Furthermore, they are often not optimized for energy efficiency. Therefore, there is a need for an air conditioning control system that achieves both comfort and energy efficiency while also taking the user's emotional state into account.

[0762] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis means for analyzing the emotional state of the user, an AI generation means for balancing minimizing the air conditioner's power consumption and user comfort, and a central processing unit for processing information including timestamps and environmental data. This enables optimal air conditioner settings that reflect the user's emotional state in real time.

[0763] The "sensor means for detecting human movement" is a device for detecting the movement of a user in a room in real time.

[0764] "Central Processing Unit Means" means a computer system for receiving, analyzing, and processing information from sensor means and other input devices.

[0765] "Generative AI means" is an artificial intelligence technology that calculates and generates optimal air conditioning settings based on received data.

[0766] The "communication terminal means" is a device or system for transmitting the air conditioner settings generated by the generation AI means to the air conditioner.

[0767] The "control device means" is a control unit for controlling the air conditioner based on the air conditioner settings received from the communication terminal means.

[0768] The "emotion analysis means" refers to software and hardware for analyzing the user's facial expressions and voice data to extract the user's emotional state.

[0769] A "timestamp" is information that indicates the date and time when data was recorded or transmitted.

[0770] "Environmental data" is information indicating environmental parameters such as indoor temperature and humidity.

[0771] The present invention proposes a system that analyzes a person's emotional state and optimizes air conditioner settings based on the results. Specific embodiments of the system will be described in detail below.

[0772] Initialization Phase

[0773] server:

[0774] The server first loads a generative AI model. This model incorporates an algorithm that analyzes the user's emotional data and uses the results to determine optimal air conditioning settings. Generative AI models are often implemented using the TensorFlow library. The server then loads an emotion engine, which is responsible for extracting the user's emotional state from facial expressions and voice data. The server also sets up interfaces for receiving data from various sensors and devices, and initializes the system log.

[0775] Device:

[0776] The device is configured to install motion sensors, cameras, and microphones in appropriate locations in the room, enabling real-time detection of people's movements and emotional states. For example, motion sensors are installed in the four corners of the room, cameras on the ceiling, and microphones on the desk. The device checks the operation of these sensors to ensure they are working properly.

[0777] Acquiring and Sending Data

[0778] Device:

[0779] A motion sensor installed on the device detects the user's movements. This data is sent to the device as user movement detection data. At the same time, a camera and microphone capture the user's facial expressions and voice, which the emotion engine analyzes to extract emotional data. For example, if the user is tired, the emotion engine outputs "fatigue" as the emotional data. This data is compiled into a single data packet along with a timestamp and sent to the server.

[0780] server:

[0781] The server receives data packets sent from the device and records them in the system log. The data packets include the user's emotional state based on their movements, facial expressions, and voice data, as well as environmental data (temperature and humidity). For example, the received data is recorded as follows:

[0782] 2023-10-01T08:30:00Z - Motion: Detected, Emotion: Fatigue, Temperature: 22°C, Humidity: 45%

[0783] Processing by generative AI

[0784] server:

[0785] The server inputs the received data into a generative AI model, which then uses this data to calculate optimal air conditioning settings that take into account the user's emotional state and environmental data. For example, if the user is tired, the generative AI model calculates a relaxing temperature of 24 degrees and a medium airflow setting.

[0786] Examples:

[0787] As an example of a prompt, the following text is fed into the generative AI model:

[0788] The user is currently tired and needs to adjust the temperature setting to 24 degrees and the fan speed to medium.

[0789] Air conditioning control

[0790] Device:

[0791] The device receives the air conditioner setting information sent from the server. Based on that information, the device generates specific commands to adjust the air conditioner settings. For example, it generates commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID and sends them to the air conditioner.

[0792] Examples:

[0793] When a user enters their bedroom at night before going to sleep, the motion sensor detects movement and the emotion engine detects "stress." The AI ​​then calculates a relaxing temperature of 23 degrees and sends a command to the air conditioner on the device. As a result, the air conditioner is set to 23 degrees, providing the user with the optimal environment.

[0794] In this way, the system of the present invention can reflect the user's emotional state in real time and achieve optimal air conditioning settings, maximizing energy efficiency while providing a comfortable environment.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Step 1:

[0797] Initialize the server:

[0798] The server first loads the generative AI model using the TensorFlow library. Next, it loads the emotion engine, which functions as an emotion analysis means, enabling it to analyze the user's facial expressions and voice data. It also sets up interfaces for receiving data from various sensors and devices. Finally, it initializes the system log, preparing to record the system's operating status.

[0799] Input: Generative AI model, emotion engine

[0800] Output: Loaded model and engine, set up receiving interface

[0801] Step 2:

[0802] Initialize the device:

[0803] The device installs a motion sensor, camera, and microphone in appropriate locations in the room. For example, motion sensors are installed in the four corners of the room, a camera on the ceiling, and a microphone on the desk. Next, the operation of these sensors is checked to ensure they are working properly.

[0804] Input: Motion sensor, camera, microphone

[0805] Output: Installed sensors and operation check report

[0806] Step 3:

[0807] Data acquisition by device:

[0808] A motion sensor installed on the device detects the user's movements. At the same time, a camera and microphone capture the user's facial expressions and voice. This allows the user's movement data, facial expression data, and voice data to be acquired.

[0809] Input: User movements, facial expressions, and voice

[0810] Output: Movement data, facial expression data, audio data

[0811] Step 4:

[0812] Data sent by the device:

[0813] The emotion analysis means analyzes the acquired facial expression data and voice data to extract emotion data. The extracted emotion data, movement data, and environmental data (temperature and humidity) are compiled into a single data packet along with a timestamp and sent to the server.

[0814] Input: movement data, facial expression data, audio data, environmental data

[0815] Output: Data packet (including emotion data, movement data, and environment data)

[0816] Step 5:

[0817] Server receives data:

[0818] The server receives data packets sent from the terminal, records the received data in the system log, and prepares it for analysis.

[0819] Input: Data packet

[0820] Output: Logged data

[0821] Step 6:

[0822] Data input to the server-generated AI model:

[0823] The server inputs the received data into the generative AI model. For example, it inputs a prompt sentence based on the contents of the data packet into the generative AI model.

[0824] Input: Data packet (emotion data, movement data, environmental data)

[0825] Output: Input data to the generative AI model

[0826] Step 7:

[0827] Generative AI model calculates air conditioning settings:

[0828] The generative AI model calculates optimal air conditioning settings based on input data. For example, if the user is tired, it will set the temperature to 24 degrees and the fan speed to medium, which is considered relaxing.

[0829] Input: Input data to the generative AI model

[0830] Output: Calculated air conditioner settings (temperature, airflow, operation mode)

[0831] Step 8:

[0832] Server sends configuration instructions:

[0833] The server sends the calculation results to the device, which then generates specific commands to adjust the air conditioner settings.

[0834] Input: Calculated air conditioner settings

[0835] Output: Configuration instructions to the terminal

[0836] Step 9:

[0837] Applying the settings via terminal:

[0838] The terminal generates control commands for the air conditioner based on the received setting information. The generated commands are sent to the air conditioner to change the settings. For example, commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID are sent to the air conditioner.

[0839] Input: Setting instructions (temperature, air volume, operation mode)

[0840] Output: Air conditioning control command

[0841] In this way, the system operates continuously, automatically adjusting air conditioning settings to reflect the user's emotional state, providing a comfortable environment.

[0842] (Application example 2)

[0843] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0844] In brick-and-mortar stores, air conditioning settings are important for improving customer comfort. However, it has been difficult with conventional technology to grasp the preferences and emotional state of each customer in real time and set the optimal temperature. Furthermore, adjusting the temperature to maintain comfort while minimizing energy consumption is time-consuming and inefficient.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a human presence sensor means for detecting human movement, an emotion analysis means for analyzing the emotional state of visitors and generating emotion data, and an AI generation means for generating optimal air conditioner settings based on data received by the network means. This makes it possible to grasp the emotional state and movement of visitors in real time and set the air conditioner optimally. Furthermore, it is possible to achieve both improved comfort for visitors and more efficient power consumption.

[0846] The "human sensor means" is a sensor device for detecting human movement.

[0847] "Network Means" means the communications infrastructure for receiving and transmitting data from sensors and other devices.

[0848] The "generative AI means" is an artificial intelligence system that generates optimal air conditioning settings based on the data it receives.

[0849] The "emotion analysis means" is a device or system for analyzing the facial expressions and voices of customers and generating emotion data.

[0850] The "terminal means" is a device for transmitting the generated air conditioner settings to the air conditioner.

[0851] "Air conditioner control means" refers to a control system for regulating the operation of an air conditioner.

[0852] "Operational means" refers to the means for operating the system with the aim of improving the comfort of customers and streamlining power consumption.

[0853] A "timestamp" is information that indicates the date and time when data was generated or recorded.

[0854] "Environmental data" is information relating to the surrounding environmental conditions such as temperature and humidity.

[0855] To implement this invention, it is necessary to build a system that can balance customer comfort and energy efficiency in a physical store. This system mainly uses the following hardware and software:

[0856] Hardware:

[0857] 1. Human presence sensor: A device that detects human movement within the store.

[0858] 2. Camera and microphone: Devices that capture customers' facial expressions and voices.

[0859] 3. Air conditioner: The air conditioner itself is responsible for adjusting the temperature and airflow according to the system's instructions.

[0860] software:

[0861] 1. Generative AI model: An artificial intelligence system that calculates and generates optimal air conditioner settings based on received data.

[0862] 2. Emotion analysis engine: Generates emotional data from customers' facial expressions and voices.

[0863] 3. Network communication software: This is the communication software used to send data from sensors and analysis engines to the server.

[0864] System operation description:

[0865] The server first loads the generative AI model and emotion analysis engine, which prepares the system for analyzing the emotional state and behavior of customers in real time. Next, motion sensors, cameras, and microphones are installed in appropriate locations within the store, preparing to capture both people's behavior and emotion data in real time.

[0866] The sensor device and analysis engine generate data packets with timestamps based on the detected and analyzed data, which are then sent to a server. The server analyzes the received data and calculates the optimal settings for the air conditioner (temperature, airflow, operation mode, etc.).

[0867] The generated air conditioner settings are sent to the air conditioner via the terminal means, and the air conditioner control means then adjusts the air conditioner based on these settings, ensuring the comfort of customers while also achieving efficient power consumption.

[0868] Examples:

[0869] For example, if there are many customers at lunchtime, the camera and microphone will detect the facial expressions and voices of customers who feel it is hot. Based on this information, the AI ​​will suggest setting the air conditioner temperature to 22 degrees as the optimal setting, and that setting will be reflected on the air conditioner.

[0870] At night, when there are only a few customers, the emotion analysis engine will analyze the relaxed state, and the generative AI will suggest setting the air conditioner to a relaxing temperature (for example, 25 degrees). This setting will also be immediately reflected in the air conditioner.

[0871] Example prompt sentence:

[0872] The prompt for the generative AI model is:

[0873] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[0874] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0875] Step 1:

[0876] The server loads the generative AI model and the sentiment analysis engine. These models are required to analyze the emotional state of customers in real time and generate optimal air conditioning settings. The model file of the sentiment analysis engine and the data of the generative AI model are taken as input, and these are made available as output.

[0877] Step 2:

[0878] The terminal is installed with motion sensors, cameras, and microphones in appropriate locations within the physical store, allowing it to acquire both customer movement and emotional data in real time. The input is information about the installation location of the sensors, cameras, and microphones, and the output is information indicating that installation is complete and the sensor is ready to operate.

[0879] Step 3:

[0880] The motion sensor detects the movement of customers and acquires timestamps and environmental data (current temperature and humidity). This data is packetized as sensor information and sent to the server. The input is raw data from the sensor, and the output is data packets and transmission status.

[0881] Step 4:

[0882] The emotion analysis engine analyzes the facial expressions and voices of customers captured through the camera and microphone to generate emotion data. This data is also packetized with a timestamp and sent to the server. The input is video and audio data, and the output is emotion data packets and transmission status.

[0883] Step 5:

[0884] The server receives data packets sent from the motion sensor and emotion analysis engine and logs them, with the data packets as input and the recorded log data and the data readiness status as output.

[0885] Step 6:

[0886] The generative AI model calculates optimal air conditioner settings based on data stored on the server. Specifically, it analyzes the user's emotions and environmental conditions in the form of prompt sentences based on the emotional and environmental data received as input, and obtains the optimal air conditioner temperature, airflow, and operation mode settings as output.

[0887] Step 7:

[0888] The server sends the air conditioner setting information calculated by the generative AI model to the terminal. The input includes the calculation result of the generative AI model, and the output includes the transmission status and transmission completion notification to the terminal.

[0889] Step 8:

[0890] The terminal adjusts the settings of the air conditioner through the air conditioner control means based on the air conditioner setting information received from the server. The input is the air conditioner setting information, and the output is the adjustment completion and the current setting status of the air conditioner.

[0891] Example prompt sentence:

[0892] The prompt for the generative AI model is:

[0893] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[0894] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0895] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0896] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0897] [Third embodiment]

[0898] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0899] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0900] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0901] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0902] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0903] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0904] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0905] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0906] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0907] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0908] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0909] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0910] This invention is a system that optimizes the operation of an air conditioner by combining a human sensor, a server, a generating AI, a terminal, and an air conditioner control means. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[0911] System Overview

[0912] Initialization Phase

[0913] server:

[0914] The server loads the generative AI model, initializes interfaces to receive data from various sensors and devices, and initializes logs to prepare for recording the system's operating status.

[0915] Device:

[0916] The sensors are set up to work properly, especially the motion sensors, which are placed in specific locations in the room and calibrated to detect human movement.

[0917] Acquiring and Sending Data

[0918] Device:

[0919] The motion sensor detects the user's movements in real time, while simultaneously recording environmental data (such as temperature and humidity). This data is then sent to the server at regular intervals.

[0920] server:

[0921] The server receives the data sent from the device and prepares it for input into the generative AI, including timestamps of detected movements and environmental data.

[0922] Processing by generative AI

[0923] server:

[0924] The server inputs the received data into the AI ​​generator, which then calculates the optimal air conditioner settings based on that data. The resulting settings include the air conditioner temperature, airflow, and operating mode.

[0925] Examples:

[0926] For example, if the motion sensor detects user movement in the morning, the generated AI will know that the room temperature is low at that time and change the air conditioner setting to 20 degrees to make the user comfortable. This setting information is then sent to the device.

[0927] Air conditioning control

[0928] Device:

[0929] Receives air conditioner setting information sent from the server. After receiving the information, changes the air conditioner settings according to the information. Specific setting changes include temperature adjustment, air volume adjustment, and operation mode change.

[0930] Examples:

[0931] If the motion sensor detects movement in the bedroom at night, the AI ​​will use that data to instruct the device to change the air conditioner setting to 23 degrees. The device will then adjust the air conditioner setting to 23 degrees.

[0932] Example of operation

[0933] Morning Scenario

[0934] User:

[0935] In the morning, I enter the living room.

[0936] Device:

[0937] A motion sensor installed in the room detects the user's movements.

[0938] server:

[0939] The server receives data from the sensors and inputs it into the generation AI.

[0940] Generation AI:

[0941] Based on the condition "morning" and other environmental data, the generating AI generates instructions to set the temperature in the living room to 20 degrees.

[0942] Device:

[0943] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[0944] Night Scenario

[0945] User:

[0946] At night, go into your bedroom before going to sleep.

[0947] Device:

[0948] A motion sensor installed in the bedroom detects the user's movements.

[0949] server:

[0950] The server receives data from the sensors and inputs it into the generation AI.

[0951] Generation AI:

[0952] Based on the condition "night" and other environmental data, the generating AI generates instructions to set the air conditioner to 23 degrees.

[0953] Device:

[0954] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[0955] In this way, the system of the present invention collects sensor data in real time and utilizes generative AI to optimize air conditioner operation, thereby achieving both efficient use of energy resources and user comfort.

[0956] The processing flow will be explained below.

[0957] Step 1:

[0958] Server: Loads the generative AI model and initializes the system. It loads the model into memory to allow the generative AI to calculate optimal air conditioner settings, and sets up interfaces to receive data from various sensors and devices.

[0959] Step 2:

[0960] Terminal: Detects user movement through a motion sensor, which also records environmental data (temperature and humidity) at specific intervals and forms this information into a data packet.

[0961] Step 3:

[0962] Terminal: The terminal creates a data packet, adds a timestamp to it, and sends it to the server. This information includes the person's movement, current temperature, and humidity.

[0963] Step 4:

[0964] Server: Receives data packets sent from the device, logs the received data, and formats the data for processing by the generation AI.

[0965] Step 5:

[0966] Server: Inputs the formatted data into the AI ​​generator, which calculates the optimal air conditioning settings based on the received data.

[0967] Step 6:

[0968] Server: Receives the output from the generation AI and creates operating instructions for the air conditioner. It creates instructions including specific settings (temperature, airflow, operation mode).

[0969] Step 7:

[0970] Server: Sends the generated air conditioner setting instructions to the terminal. These instructions indicate how to change the air conditioner settings.

[0971] Step 8:

[0972] Terminal: Receives instructions for air conditioner settings from the server. Based on this information, it generates specific commands to adjust the air conditioner settings.

[0973] Step 9:

[0974] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[0975] Step 10:

[0976] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, starts collecting data from the sensors again to prepare for the next cycle.

[0977] The above is the specific program processing flow for implementing the invention. This system detects user movements in real time and uses generative AI to optimize air conditioner settings, aiming to achieve both efficient use of energy resources and a comfortable living environment.

[0978] Example 1

[0979] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0980] In modern air conditioning systems, minimizing energy consumption while maximizing user comfort is a critical issue. Existing systems generally rely on manual settings or timer functions, making it difficult to achieve optimal control that adapts to user movements and environmental conditions. Furthermore, air conditioner power consumption is often not effectively managed. Therefore, a new air conditioning system that can achieve both user comfort and energy efficiency is needed.

[0981] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0982] In this invention, the server includes a detection means, a receiving means, a generating means, a transmitting means, and a control means, which enables real-time detection of human movement and optimization of air conditioning settings using a generative AI model based on the data, thereby achieving a balance between minimizing power consumption and comfort.

[0983] - "Detection means" means a sensor device for detecting human movement.

[0984] The "receiving means" is a function or device for receiving data from the detecting means.

[0985] The "generation means" refers to a generation AI model and its execution environment for generating air conditioning settings based on the data acquired by the receiving means.

[0986] The "transmission means" is a function or device for transmitting the air conditioning settings generated by the generation means to the air conditioner.

[0987] The "control means" is a function or device for controlling the air conditioner based on the air conditioning settings received from the transmission means.

[0988] A "timestamp" is data that indicates a specific time or date, and is used to record the occurrence time of a detected event or data.

[0989] "Environmental data" refers to data that indicates the surrounding environmental conditions such as temperature and humidity.

[0990] This invention is a system that combines a motion sensor, a server, a generative AI model, a terminal, and an air conditioner to optimize the operation of the air conditioner. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[0991] Initialization Phase

[0992] First, the server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model into memory using TensorFlow or PyTorch. Next, the server initializes an interface for receiving data from multiple sensors and devices. This is done by building a REST API using the Python Flask framework. The server also prepares to use Logstash or Elasticsearch for log management to record the system's operating status.

[0993] The device is set up to ensure the sensor works properly. For example, a Raspberry Pi or Arduino is used, and a PIR motion sensor is connected to the GPIO pin to check operation. The motion sensor is installed on the ceiling or wall of the room and angled to accurately detect user movement.

[0994] Acquiring and Sending Data

[0995] The device equipped with a motion sensor detects user movement in real time. In addition, it simultaneously records environmental data (temperature and humidity) using a DHT22 temperature and humidity sensor. This data is sent to the server at regular intervals. The data is sent using an HTTP POST request.

[0996] The server receives data sent from the device. Using the Flask framework, the received data includes timestamps of the user's movements and environmental data (temperature and humidity). The server preprocesses this data and prepares it for input to the generative AI. Specifically, it converts the data from JSON format to the input format of the generative AI.

[0997] Processing by generative AI

[0998] The server inputs the received data into a generative AI model, which then calculates the optimal air conditioning settings based on this data. The calculation results in the air conditioning unit's temperature settings, airflow settings, operating mode, etc.

[0999] For example, if a motion sensor detects user movement in the morning and the current room temperature is 22 degrees, the generative AI model will instruct the air conditioner to change the setting to 20 degrees. This calculation result is sent to the device.

[1000] Air conditioning control

[1001] The terminal receives the air conditioning setting information sent from the server. After receiving the information, it changes the air conditioning unit settings based on that information. Specifically, this includes adjusting the temperature, airflow, and operating mode. To do this, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate signals to the air conditioning unit.

[1002] Examples of concrete examples and prompts

[1003] In the morning scenario, when a user enters the living room, the device detects the user's movement and sends that data, along with environmental data, to the server. The server inputs the data into the generative AI model and calculates the optimal air conditioning settings. For example, if the room temperature is 22 degrees at 7 a.m., the generative AI will set the air conditioning temperature to 20 degrees. The device that receives this instruction will set the air conditioning temperature to 20 degrees.

[1004] In the night scenario, the same process is repeated when the user enters the bedroom. Based on the "night" condition and other environmental data, the generative AI generates an instruction to set the air conditioner to 23 degrees. The device receives this instruction and adjusts the air conditioner temperature to 23 degrees.

[1005] Example prompt sentence:

[1006] "Optimize air conditioning settings when someone enters the room. Current conditions are 22 degrees and the time is 7am."

[1007] As a result, the system of the present invention collects sensor data in real time and uses generative AI to optimize the operation of air conditioning equipment, thereby achieving both efficient energy consumption and user comfort.

[1008] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1009] Step 1:

[1010] The server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model using TensorFlow or PyTorch into memory. This is done during the initialization phase of the program. The input is the trained data for the model, and the output is a ready-to-run generative AI model.

[1011] Step 2:

[1012] The server initializes the data receiving interface. This is achieved by building a REST API using the Python Flask framework. The input is the interface configuration information, and the output is the endpoint for receiving data.

[1013] Step 3:

[1014] The server initializes the logs and prepares to record system activity. Specifically, a log analysis tool such as Logstash or Elasticsearch is used. The input is a log configuration file, and the output is a log management system ready to record system activity.

[1015] Step 4:

[1016] The terminal sets up the sensor and adjusts the position of the motion sensor. Using Raspberry Pi or Arduino, connect the PIR motion sensor to the GPIO pin and check its operation. The input is the sensor installation information, and the output is a working sensor system.

[1017] Step 5:

[1018] The device detects user movements in real time through a motion sensor. Specifically, when the sensor detects movement, the Raspberry Pi records this data. The input is the user's movement, and the output is the movement detection data.

[1019] Step 6:

[1020] The device also simultaneously records environmental data and sends it to the server at regular intervals. Temperature and humidity are measured using the DHT22 and sent to the server via an HTTP POST request along with PIR motion sensor data. The input is temperature, humidity, and motion detection data, and the output is the sensor data sent to the server.

[1021] Step 7:

[1022] The server receives data sent from the device. It uses Flask as the framework and receives data at the receiving endpoint. The input is the transmitted sensor data, and the output is the received data.

[1023] Step 8:

[1024] The server preprocesses the received data and prepares it for input to the generative AI. It converts the JSON formatted data into a format that the generative AI model can accept. The input is the received JSON data, and the output is the input data for the generative AI.

[1025] Step 9:

[1026] The server inputs data into the generative AI, which then calculates the optimal air conditioning settings. The generative AI model calculates the optimal temperature settings, airflow settings, and operation mode based on the input data. The input is processed environmental data and motion detection data, and the output is air conditioning setting information.

[1027] Step 10:

[1028] The server sends the generated air conditioning setting information to the terminal. The setting information obtained from the generative AI model is converted to JSON format and sent to the terminal. The input is the generated air conditioning setting information, and the output is the setting instructions sent to the terminal.

[1029] Step 11:

[1030] The terminal receives the air conditioning setting information sent from the server. The terminal receives the setting information and stores it in its internal memory. The input is the air conditioning setting information from the server, and the output is the recorded setting information.

[1031] Step 12:

[1032] The terminal changes the air conditioner settings based on the received information. Specifically, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate infrared signal to the air conditioner. The input is the saved air conditioner setting information, and the output is the air conditioner with the changed setting.

[1033] (Application example 1)

[1034] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1035] Maintaining the comfort of customers and employees in real-world spaces such as stores while minimizing the energy consumption of air conditioning equipment is a difficult challenge. This becomes particularly complex when different temperatures and airflow volumes need to be adjusted in multiple zones within a store. Conventional air conditioning systems have difficulty responding to these real-time conditions, which can result in energy waste and customer discomfort.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1037] In this invention, the server includes a detection means for detecting human movement, an information processing means for receiving data from the detection means, a generation AI model means for generating optimal air conditioning settings based on the data received by the information processing means, a communication means for transmitting the air conditioning settings generated by the generation AI model means to air conditioning equipment, a control means for controlling the air conditioning equipment, and a means for adjusting the temperature and air volume of multiple zones based on the data. This makes it possible to maintain optimal air conditioning settings in real time in multiple zones within a store, ensuring the comfort of customers and employees while minimizing energy consumption.

[1038] The "detection means" is a sensor device for detecting human movement.

[1039] The "information processing means" is a server or computer system that aggregates data received from the detection means and inputs it into the generative AI model means.

[1040] "Generative AI model means" refers to an artificial intelligence model that calculates optimal air conditioning settings based on data received by the information processing means.

[1041] "Communication means" refers to the network interface and communication protocol used to transmit the air conditioning settings generated by the generation AI model means to the air conditioning equipment.

[1042] "Control means" refers to a system or device for operating and adjusting air conditioning equipment based on air conditioning settings received from communication means.

[1043] "Multiple zones" refers to different areas or sections within a store or other physical space.

[1044] "Real-time" means that data is collected and processed immediately, and air conditioning settings are adjusted immediately based on the results.

[1045] "Minimizing energy consumption" means keeping the energy required to operate air conditioning equipment as low as possible.

[1046] "Comfort" refers to the quality of the indoor environment that keeps customers and employees comfortable.

[1047] A system for realizing this application example includes a detection means, an information processing means, a generative AI model means, a communication means, a control means, and a means for adjusting the temperature and airflow for multiple zones.

[1048] System Configuration

[1049] Detection Methods:

[1050] Motion sensors installed in each zone of the store detect customer movement, allowing information on the presence of people in each zone and environmental data (temperature, humidity, etc.) to be collected in real time.

[1051] Information processing means:

[1052] The data obtained from these sensors is sent to a server, which processes the data in real time and prepares it for input into the generative AI model. Specific data processing includes adding timestamps and converting environmental data formats.

[1053] Generative AI model means:

[1054] The server generates optimal air conditioning settings based on the collected data using a generative AI model that parses and processes prompts based on specific time of day and environmental conditions to ensure user comfort while maximizing energy efficiency.

[1055] Communication Method:

[1056] The generated air conditioning settings are sent to the air conditioning equipment via a communication method, such as Wi-Fi or a wired network.

[1057] Control means:

[1058] The air conditioning equipment adjusts the temperature and airflow based on the received settings. Specifically, each air conditioning equipment operates automatically to maintain optimal air conditioning in each zone.

[1059] Program processing

[1060] The program of this system performs processing in the following manner.

[1061] 1. Data Collection:

[1062] Motion sensors detect customer movements in each zone of the store and send the data to a server.

[1063] 2. Data preprocessing:

[1064] The server formats the received data, adds a timestamp, and organizes the environmental data.

[1065] 3. Input to the generative AI model:

[1066] The server inputs the preprocessed data into a generative AI model, which analyzes the prompt and calculates the optimal air conditioning settings. For example,

[1067] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[1068] 4. Send results:

[1069] The generated air conditioning settings are sent from the server to the air conditioning equipment.

[1070] 5. Air conditioning control:

[1071] The air conditioning equipment automatically adjusts the temperature and airflow according to the received settings. For example, if the store is crowded in the afternoon, the temperature will be set to 22 degrees.

[1072] Specific examples

[1073] For example, if the store starts to get busy at 2 p.m., the motion sensor sends this information to the server. The server automatically uses a generative AI model to calculate the temperature settings for each zone. The server then sends the calculated settings to the air conditioners, which adjust the temperature and airflow for each zone. This minimizes energy consumption while maintaining a comfortable environment for customers and employees.

[1074] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1075] Step 1:

[1076] The detection means collects information on people's movements and presence in each zone, as well as environmental data (temperature, humidity, etc.) in real time. The input is the physical movement of people and environmental information from the detection means (human presence sensors). The output is the collected data (sensor information with a timestamp).

[1077] Step 2:

[1078] The server converts the sensor information received from the detection means into a specified format and adds a timestamp. The input is the sensor information. The output is formatted data (in a format that can be saved in a database).

[1079] Step 3:

[1080] The server prepares the formatted data for input to the generative AI model. Specifically, it combines environmental data (temperature, humidity) and human movement data and converts it into a format that the generative AI model can understand. The input is the formatted data. The output is the input data for the generative AI model.

[1081] Step 4:

[1082] The server sends prompts containing real-time sensor information and environmental data to the generative AI model. For example,

[1083] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[1084] The input is a prompt and sensor information, and the output is the optimal air conditioning settings from a generative AI model.

[1085] Step 5:

[1086] The generative AI model analyzes the prompt text and sensor information, calculates the optimal air conditioning settings (temperature, air volume, operation mode) for each zone, and returns the results to the server. The input is the prompt text to the generative AI model. The output is the optimal air conditioning settings.

[1087] Step 6:

[1088] The server sends the optimal air conditioning settings returned by the generative AI model to the air conditioning equipment. Specifically, it sends instructions to the air conditioning equipment using a communication method. The input is the optimal air conditioning settings. The output is a control instruction to the air conditioning equipment.

[1089] Step 7:

[1090] The air conditioning equipment automatically adjusts the temperature and airflow based on the received optimal air conditioning settings. This optimizes the temperature and airflow for each zone in real time. The input is the control command from the server. The output is the adjusted temperature and airflow for each zone.

[1091] Step 8:

[1092] The user checks that the climate control settings are set appropriately to maintain comfort, and can make manual adjustments if necessary. The input is user feedback. The output is additional setting adjustments.

[1093] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1094] This invention is a system that combines a human presence sensor and an emotion engine to optimize air conditioner settings using generative AI. The purpose of this system is to minimize energy consumption while increasing comfort, taking into account the user's emotional state.

[1095] System Overview

[1096] Initialization Phase

[1097] server:

[1098] The server loads the generative AI model and initializes the system. It also loads the emotion engine so that it can process user emotion data. It also sets up interfaces to receive data from various sensors and devices, and initializes the system log to prepare for recording operation status.

[1099] Device:

[1100] Human sensors and sensors that capture the user's facial expressions and voice are installed in appropriate locations in the room, allowing for real-time detection of human movements and emotional states.

[1101] Acquiring and Sending Data

[1102] Device:

[1103] The motion sensor detects the user's movements. At the same time, the emotion engine analyzes the user's facial expressions and voice to extract emotional data. This information is then packaged into data packets along with a timestamp and sent to the server.

[1104] server:

[1105] The server receives data packets sent from the device and logs the data. The received data includes the user's movements, environmental data (temperature and humidity), and emotional state.

[1106] Processing by generative AI

[1107] server:

[1108] The received data is input into the generation AI, which uses this data to calculate the optimal air conditioner settings taking into account the user's emotional state. The calculation results in the air conditioner's temperature setting, airflow setting, operation mode, etc.

[1109] Examples:

[1110] For example, if the user has a tired expression, the AI ​​generator will set a relaxing temperature and airflow rate. This setting information is sent to the device, and the air conditioner settings are automatically changed.

[1111] Air conditioning control

[1112] Device:

[1113] Receives the air conditioner setting information sent from the server, and then generates specific commands to adjust the air conditioner settings based on that information.

[1114] Examples:

[1115] If the user is feeling stressed before going to bed at night, the emotion engine will detect this and the AI ​​generator will instruct the air conditioner to adjust the temperature to a relaxing 23 degrees. The device will then send this instruction to the air conditioner, changing the setting to 23 degrees.

[1116] Example of operation

[1117] Morning Scenario

[1118] User:

[1119] In the morning, I enter the living room.

[1120] Device:

[1121] A motion sensor installed in the room detects the user's movements, and an emotion engine analyzes their emotional state from their facial expressions.

[1122] server:

[1123] It receives data from sensors and emotion engines and feeds it into generative AI.

[1124] Generation AI:

[1125] Based on the morning state and emotional data, it generates instructions to set the temperature in the living room to 20 degrees.

[1126] Device:

[1127] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[1128] Night Scenario

[1129] User:

[1130] At night, go into your bedroom before going to sleep.

[1131] Device:

[1132] A motion sensor installed in the bedroom detects the user's movements, and an emotion engine analyzes the user's stress level.

[1133] server:

[1134] It receives data from sensors and emotion engines and feeds it into generative AI.

[1135] Generation AI:

[1136] Based on the time of night and emotional data, instructions are generated to set the air conditioner to a relaxing 23 degrees.

[1137] Device:

[1138] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[1139] In this way, the system of the present invention uses an emotion engine and generative AI to optimize air conditioner settings, thereby achieving efficient use of energy resources and improving user comfort.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] Server: Loads the generative AI model and emotion engine, initializes the system, sets up interfaces to receive data from various sensors and devices, and initializes the system log.

[1143] Step 2:

[1144] Terminal: Activate the human presence sensor and emotion sensor installed in the room. The human presence sensor detects the user's movements, and the emotion sensor collects emotion data from the user's facial expressions and voice.

[1145] Step 3:

[1146] Terminal: The motion sensor detects the user's movements, and the emotion sensor analyzes the user's emotional state. This data, along with environmental data (temperature, humidity), is formed into a data packet, which is then time-stamped and sent to the server.

[1147] Step 4:

[1148] Server: Receives and logs data packets sent from the device, including user movement, environmental data, and emotional data.

[1149] Step 5:

[1150] Server: Inputs the received data into the Generative AI and Emotion Engine. The Generative AI calculates the optimal air conditioning settings based on the user's movements and emotional data. The Emotion Engine adjusts the settings output by the Generative AI based on the user's emotional state.

[1151] Step 6:

[1152] Server: Receives the output from the generation AI and the adjustment results from the emotion engine, and creates operating instructions for the air conditioner. It generates instructions including specific settings (temperature, air volume, operation mode) and sends them to the terminal.

[1153] Step 7:

[1154] Server: Sends the generated air conditioner setting instructions to the device. These instructions contain details of how the air conditioner should be set up.

[1155] Step 8:

[1156] Terminal: Receives air conditioner setting instructions sent from the server. Based on that information, it generates specific commands to adjust the air conditioner settings.

[1157] Step 9:

[1158] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the air conditioner temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[1159] Step 10:

[1160] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, it starts collecting data from the sensors again to prepare for the next cycle.

[1161] Specific examples

[1162] Morning Scenario

[1163] Step 1:

[1164] User: In the morning, I walk into the living room.

[1165] Step 2:

[1166] Terminal: A motion sensor installed in the room detects the user's movements, and an emotion sensor analyzes the user's face.

[1167] Step 3:

[1168] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[1169] Step 4:

[1170] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[1171] Step 5:

[1172] Generative AI: Calculates the optimal temperature setting (e.g., 20 degrees) based on the time of day and the living room conditions. If the emotion engine determines that the user is in a relaxed state, it will fine-tune the temperature setting.

[1173] Step 6:

[1174] Server: Sends the generated air conditioner setting (temperature 20 degrees) to the device.

[1175] Step 7:

[1176] Terminal: Sends instructions to the air conditioner to set the temperature to 20 degrees.

[1177] Night Scenario

[1178] Step 1:

[1179] User: Enters bedroom at night before going to sleep.

[1180] Step 2:

[1181] Device: A motion sensor installed in the bedroom detects the user's movements, and an emotion sensor analyzes the user's face.

[1182] Step 3:

[1183] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[1184] Step 4:

[1185] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[1186] Step 5:

[1187] Generative AI: Calculates the optimal temperature setting (e.g., 23°C) based on the time of night and the bedroom conditions. If the emotion engine determines that the user is stressed, it will fine-tune the temperature setting.

[1188] Step 6:

[1189] Server: Sends the generated air conditioner setting (temperature 23 degrees) to the device.

[1190] Step 7:

[1191] Terminal: Sends instructions to the air conditioner to set the temperature to 23 degrees.

[1192] Example 2

[1193] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1194] Conventional air conditioning control systems simply set air conditioning settings based on temperature and humidity without considering the user's emotional state, which can result in insufficient user comfort. Furthermore, they are often not optimized for energy efficiency. Therefore, there is a need for an air conditioning control system that achieves both comfort and energy efficiency while also taking the user's emotional state into account.

[1195] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis means for analyzing the emotional state of the user, an AI generation means for balancing minimizing the air conditioner's power consumption and user comfort, and a central processing unit for processing information including timestamps and environmental data. This enables optimal air conditioner settings that reflect the user's emotional state in real time.

[1196] The "sensor means for detecting human movement" is a device for detecting the movement of a user in a room in real time.

[1197] "Central Processing Unit Means" means a computer system for receiving, analyzing, and processing information from sensor means and other input devices.

[1198] "Generative AI means" is an artificial intelligence technology that calculates and generates optimal air conditioning settings based on received data.

[1199] The "communication terminal means" is a device or system for transmitting the air conditioner settings generated by the generation AI means to the air conditioner.

[1200] The "control device means" is a control unit for controlling the air conditioner based on the air conditioner settings received from the communication terminal means.

[1201] The "emotion analysis means" refers to software and hardware for analyzing the user's facial expressions and voice data to extract the user's emotional state.

[1202] A "timestamp" is information that indicates the date and time when data was recorded or transmitted.

[1203] "Environmental data" is information indicating environmental parameters such as indoor temperature and humidity.

[1204] The present invention proposes a system that analyzes a person's emotional state and optimizes air conditioner settings based on the results. Specific embodiments of the system will be described in detail below.

[1205] Initialization Phase

[1206] server:

[1207] The server first loads a generative AI model. This model incorporates an algorithm that analyzes the user's emotional data and uses the results to determine optimal air conditioning settings. Generative AI models are often implemented using the TensorFlow library. The server then loads an emotion engine, which is responsible for extracting the user's emotional state from facial expressions and voice data. The server also sets up interfaces for receiving data from various sensors and devices, and initializes the system log.

[1208] Device:

[1209] The device is configured to install motion sensors, cameras, and microphones in appropriate locations in the room, enabling real-time detection of people's movements and emotional states. For example, motion sensors are installed in the four corners of the room, cameras on the ceiling, and microphones on the desk. The device checks the operation of these sensors to ensure they are working properly.

[1210] Acquiring and Sending Data

[1211] Device:

[1212] A motion sensor installed on the device detects the user's movements. This data is sent to the device as user movement detection data. At the same time, a camera and microphone capture the user's facial expressions and voice, which the emotion engine analyzes to extract emotional data. For example, if the user is tired, the emotion engine outputs "fatigue" as the emotional data. This data is compiled into a single data packet along with a timestamp and sent to the server.

[1213] server:

[1214] The server receives data packets sent from the device and records them in the system log. The data packets include the user's emotional state based on their movements, facial expressions, and voice data, as well as environmental data (temperature and humidity). For example, the received data is recorded as follows:

[1215] 2023-10-01T08:30:00Z - Motion: Detected, Emotion: Fatigue, Temperature: 22°C, Humidity: 45%

[1216] Processing by generative AI

[1217] server:

[1218] The server inputs the received data into a generative AI model, which then uses this data to calculate optimal air conditioning settings that take into account the user's emotional state and environmental data. For example, if the user is tired, the generative AI model calculates a relaxing temperature of 24 degrees and a medium airflow setting.

[1219] Examples:

[1220] As an example of a prompt, the following text is fed into the generative AI model:

[1221] The user is currently tired and needs to adjust the temperature setting to 24 degrees and the fan speed to medium.

[1222] Air conditioning control

[1223] Device:

[1224] The device receives the air conditioner setting information sent from the server. Based on that information, the device generates specific commands to adjust the air conditioner settings. For example, it generates commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID and sends them to the air conditioner.

[1225] Examples:

[1226] When a user enters their bedroom at night before going to sleep, the motion sensor detects movement and the emotion engine detects "stress." The AI ​​then calculates a relaxing temperature of 23 degrees and sends a command to the air conditioner on the device. As a result, the air conditioner is set to 23 degrees, providing the user with the optimal environment.

[1227] In this way, the system of the present invention can reflect the user's emotional state in real time and achieve optimal air conditioning settings, maximizing energy efficiency while providing a comfortable environment.

[1228] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1229] Step 1:

[1230] Initialize the server:

[1231] The server first loads the generative AI model using the TensorFlow library. Next, it loads the emotion engine, which functions as an emotion analysis means, enabling it to analyze the user's facial expressions and voice data. It also sets up interfaces for receiving data from various sensors and devices. Finally, it initializes the system log, preparing to record the system's operating status.

[1232] Input: Generative AI model, emotion engine

[1233] Output: Loaded model and engine, set up receiving interface

[1234] Step 2:

[1235] Initialize the device:

[1236] The device installs a motion sensor, camera, and microphone in appropriate locations in the room. For example, motion sensors are installed in the four corners of the room, a camera on the ceiling, and a microphone on the desk. Next, the operation of these sensors is checked to ensure they are working properly.

[1237] Input: Motion sensor, camera, microphone

[1238] Output: Installed sensors and operation check report

[1239] Step 3:

[1240] Data acquisition by device:

[1241] A motion sensor installed on the device detects the user's movements. At the same time, a camera and microphone capture the user's facial expressions and voice. This allows the user's movement data, facial expression data, and voice data to be acquired.

[1242] Input: User movements, facial expressions, and voice

[1243] Output: Movement data, facial expression data, audio data

[1244] Step 4:

[1245] Data sent by the device:

[1246] The emotion analysis means analyzes the acquired facial expression data and voice data to extract emotion data. The extracted emotion data, movement data, and environmental data (temperature and humidity) are compiled into a single data packet along with a timestamp and sent to the server.

[1247] Input: movement data, facial expression data, audio data, environmental data

[1248] Output: Data packet (including emotion data, movement data, and environment data)

[1249] Step 5:

[1250] Server receives data:

[1251] The server receives data packets sent from the terminal, records the received data in the system log, and prepares it for analysis.

[1252] Input: Data packet

[1253] Output: Logged data

[1254] Step 6:

[1255] Data input to the server-generated AI model:

[1256] The server inputs the received data into the generative AI model. For example, it inputs a prompt sentence based on the contents of the data packet into the generative AI model.

[1257] Input: Data packet (emotion data, movement data, environmental data)

[1258] Output: Input data to the generative AI model

[1259] Step 7:

[1260] Generative AI model calculates air conditioning settings:

[1261] The generative AI model calculates optimal air conditioning settings based on input data. For example, if the user is tired, it will set the temperature to 24 degrees and the fan speed to medium, which is considered relaxing.

[1262] Input: Input data to the generative AI model

[1263] Output: Calculated air conditioner settings (temperature, airflow, operation mode)

[1264] Step 8:

[1265] Server sends configuration instructions:

[1266] The server sends the calculation results to the device, which then generates specific commands to adjust the air conditioner settings.

[1267] Input: Calculated air conditioner settings

[1268] Output: Configuration instructions to the terminal

[1269] Step 9:

[1270] Applying the settings via terminal:

[1271] The terminal generates control commands for the air conditioner based on the received setting information. The generated commands are sent to the air conditioner to change the settings. For example, commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID are sent to the air conditioner.

[1272] Input: Setting instructions (temperature, air volume, operation mode)

[1273] Output: Air conditioning control command

[1274] In this way, the system operates continuously, automatically adjusting air conditioning settings to reflect the user's emotional state, providing a comfortable environment.

[1275] (Application example 2)

[1276] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1277] In brick-and-mortar stores, air conditioning settings are important for improving customer comfort. However, it has been difficult with conventional technology to grasp the preferences and emotional state of each customer in real time and set the optimal temperature. Furthermore, adjusting the temperature to maintain comfort while minimizing energy consumption is time-consuming and inefficient.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a human presence sensor means for detecting human movement, an emotion analysis means for analyzing the emotional state of visitors and generating emotion data, and an AI generation means for generating optimal air conditioner settings based on data received by the network means. This makes it possible to grasp the emotional state and movement of visitors in real time and set the air conditioner optimally. Furthermore, it is possible to achieve both improved comfort for visitors and more efficient power consumption.

[1279] The "human sensor means" is a sensor device for detecting human movement.

[1280] "Network Means" means the communications infrastructure for receiving and transmitting data from sensors and other devices.

[1281] The "generative AI means" is an artificial intelligence system that generates optimal air conditioning settings based on the data it receives.

[1282] The "emotion analysis means" is a device or system for analyzing the facial expressions and voices of customers and generating emotion data.

[1283] The "terminal means" is a device for transmitting the generated air conditioner settings to the air conditioner.

[1284] "Air conditioner control means" refers to a control system for regulating the operation of an air conditioner.

[1285] "Operational means" refers to the means for operating the system with the aim of improving the comfort of customers and streamlining power consumption.

[1286] A "timestamp" is information that indicates the date and time when data was generated or recorded.

[1287] "Environmental data" is information relating to the surrounding environmental conditions such as temperature and humidity.

[1288] To implement this invention, it is necessary to build a system that can balance customer comfort and energy efficiency in a physical store. This system mainly uses the following hardware and software:

[1289] Hardware:

[1290] 1. Human presence sensor: A device that detects human movement within the store.

[1291] 2. Camera and microphone: Devices that capture customers' facial expressions and voices.

[1292] 3. Air conditioner: The air conditioner itself is responsible for adjusting the temperature and airflow according to the system's instructions.

[1293] software:

[1294] 1. Generative AI model: An artificial intelligence system that calculates and generates optimal air conditioner settings based on received data.

[1295] 2. Emotion analysis engine: Generates emotional data from customers' facial expressions and voices.

[1296] 3. Network communication software: This is the communication software used to send data from sensors and analysis engines to the server.

[1297] System operation description:

[1298] The server first loads the generative AI model and emotion analysis engine, which prepares the system for analyzing the emotional state and behavior of customers in real time. Next, motion sensors, cameras, and microphones are installed in appropriate locations within the store, preparing to capture both people's behavior and emotion data in real time.

[1299] The sensor device and analysis engine generate data packets with timestamps based on the detected and analyzed data, which are then sent to a server. The server analyzes the received data and calculates the optimal settings for the air conditioner (temperature, airflow, operation mode, etc.).

[1300] The generated air conditioner settings are sent to the air conditioner via the terminal means, and the air conditioner control means then adjusts the air conditioner based on these settings, ensuring the comfort of customers while also achieving efficient power consumption.

[1301] Examples:

[1302] For example, if there are many customers at lunchtime, the camera and microphone will detect the facial expressions and voices of customers who feel it is hot. Based on this information, the AI ​​will suggest setting the air conditioner temperature to 22 degrees as the optimal setting, and that setting will be reflected on the air conditioner.

[1303] At night, when there are only a few customers, the emotion analysis engine will analyze the relaxed state, and the generative AI will suggest setting the air conditioner to a relaxing temperature (for example, 25 degrees). This setting will also be immediately reflected in the air conditioner.

[1304] Example prompt sentence:

[1305] The prompt for the generative AI model is:

[1306] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[1307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1308] Step 1:

[1309] The server loads the generative AI model and the sentiment analysis engine. These models are required to analyze the emotional state of customers in real time and generate optimal air conditioning settings. The model file of the sentiment analysis engine and the data of the generative AI model are taken as input, and these are made available as output.

[1310] Step 2:

[1311] The terminal is installed with motion sensors, cameras, and microphones in appropriate locations within the physical store, allowing it to acquire both customer movement and emotional data in real time. The input is information about the installation location of the sensors, cameras, and microphones, and the output is information indicating that installation is complete and the sensor is ready to operate.

[1312] Step 3:

[1313] The motion sensor detects the movement of customers and acquires timestamps and environmental data (current temperature and humidity). This data is packetized as sensor information and sent to the server. The input is raw data from the sensor, and the output is data packets and transmission status.

[1314] Step 4:

[1315] The emotion analysis engine analyzes the facial expressions and voices of customers captured through the camera and microphone to generate emotion data. This data is also packetized with a timestamp and sent to the server. The input is video and audio data, and the output is emotion data packets and transmission status.

[1316] Step 5:

[1317] The server receives data packets sent from the motion sensor and emotion analysis engine and logs them, with the data packets as input and the recorded log data and the data readiness status as output.

[1318] Step 6:

[1319] The generative AI model calculates optimal air conditioner settings based on data stored on the server. Specifically, it analyzes the user's emotions and environmental conditions in the form of prompt sentences based on the emotional and environmental data received as input, and obtains the optimal air conditioner temperature, airflow, and operation mode settings as output.

[1320] Step 7:

[1321] The server sends the air conditioner setting information calculated by the generative AI model to the terminal. The input includes the calculation result of the generative AI model, and the output includes the transmission status and transmission completion notification to the terminal.

[1322] Step 8:

[1323] The terminal adjusts the settings of the air conditioner through the air conditioner control means based on the air conditioner setting information received from the server. The input is the air conditioner setting information, and the output is the adjustment completion and the current setting status of the air conditioner.

[1324] Example prompt sentence:

[1325] The prompt for the generative AI model is:

[1326] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[1327] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1328] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1329] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1330] [Fourth embodiment]

[1331] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1332] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1333] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1334] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1335] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1336] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1337] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1338] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1339] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1340] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1341] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1342] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1343] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1344] This invention is a system that optimizes the operation of an air conditioner by combining a human sensor, a server, a generating AI, a terminal, and an air conditioner control means. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[1345] System Overview

[1346] Initialization Phase

[1347] server:

[1348] The server loads the generative AI model, initializes interfaces to receive data from various sensors and devices, and initializes logs to prepare for recording the system's operating status.

[1349] Device:

[1350] The sensors are set up to work properly, especially the motion sensors, which are placed in specific locations in the room and calibrated to detect human movement.

[1351] Acquiring and Sending Data

[1352] Device:

[1353] The motion sensor detects the user's movements in real time, while simultaneously recording environmental data (such as temperature and humidity). This data is then sent to the server at regular intervals.

[1354] server:

[1355] The server receives the data sent from the device and prepares it for input into the generative AI, including timestamps of detected movements and environmental data.

[1356] Processing by generative AI

[1357] server:

[1358] The server inputs the received data into the AI ​​generator, which then calculates the optimal air conditioner settings based on that data. The resulting settings include the air conditioner temperature, airflow, and operating mode.

[1359] Examples:

[1360] For example, if the motion sensor detects user movement in the morning, the generated AI will know that the room temperature is low at that time and change the air conditioner setting to 20 degrees to make the user comfortable. This setting information is then sent to the device.

[1361] Air conditioning control

[1362] Device:

[1363] Receives air conditioner setting information sent from the server. After receiving the information, changes the air conditioner settings according to the information. Specific setting changes include temperature adjustment, air volume adjustment, and operation mode change.

[1364] Examples:

[1365] If the motion sensor detects movement in the bedroom at night, the AI ​​will use that data to instruct the device to change the air conditioner setting to 23 degrees. The device will then adjust the air conditioner setting to 23 degrees.

[1366] Example of operation

[1367] Morning Scenario

[1368] User:

[1369] In the morning, I enter the living room.

[1370] Device:

[1371] A motion sensor installed in the room detects the user's movements.

[1372] server:

[1373] The server receives data from the sensors and inputs it into the generation AI.

[1374] Generation AI:

[1375] Based on the condition "morning" and other environmental data, the generating AI generates instructions to set the temperature in the living room to 20 degrees.

[1376] Device:

[1377] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[1378] Night Scenario

[1379] User:

[1380] At night, go into your bedroom before going to sleep.

[1381] Device:

[1382] A motion sensor installed in the bedroom detects the user's movements.

[1383] server:

[1384] The server receives data from the sensors and inputs it into the generation AI.

[1385] Generation AI:

[1386] Based on the condition "night" and other environmental data, the generating AI generates instructions to set the air conditioner to 23 degrees.

[1387] Device:

[1388] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[1389] In this way, the system of the present invention collects sensor data in real time and utilizes generative AI to optimize air conditioner operation, thereby achieving both efficient use of energy resources and user comfort.

[1390] The processing flow will be explained below.

[1391] Step 1:

[1392] Server: Loads the generative AI model and initializes the system. It loads the model into memory to allow the generative AI to calculate optimal air conditioner settings, and sets up interfaces to receive data from various sensors and devices.

[1393] Step 2:

[1394] Terminal: Detects user movement through a motion sensor, which also records environmental data (temperature and humidity) at specific intervals and forms this information into a data packet.

[1395] Step 3:

[1396] Terminal: The terminal creates a data packet, adds a timestamp to it, and sends it to the server. This information includes the person's movement, current temperature, and humidity.

[1397] Step 4:

[1398] Server: Receives data packets sent from the device, logs the received data, and formats the data for processing by the generation AI.

[1399] Step 5:

[1400] Server: Inputs the formatted data into the AI ​​generator, which calculates the optimal air conditioning settings based on the received data.

[1401] Step 6:

[1402] Server: Receives the output from the generation AI and creates operating instructions for the air conditioner. It creates instructions including specific settings (temperature, airflow, operation mode).

[1403] Step 7:

[1404] Server: Sends the generated air conditioner setting instructions to the terminal. These instructions indicate how to change the air conditioner settings.

[1405] Step 8:

[1406] Terminal: Receives instructions for air conditioner settings from the server. Based on this information, it generates specific commands to adjust the air conditioner settings.

[1407] Step 9:

[1408] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[1409] Step 10:

[1410] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, starts collecting data from the sensors again to prepare for the next cycle.

[1411] The above is the specific program processing flow for implementing the invention. This system detects user movements in real time and uses generative AI to optimize air conditioner settings, aiming to achieve both efficient use of energy resources and a comfortable living environment.

[1412] Example 1

[1413] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1414] In modern air conditioning systems, minimizing energy consumption while maximizing user comfort is a critical issue. Existing systems generally rely on manual settings or timer functions, making it difficult to achieve optimal control that adapts to user movements and environmental conditions. Furthermore, air conditioner power consumption is often not effectively managed. Therefore, a new air conditioning system that can achieve both user comfort and energy efficiency is needed.

[1415] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1416] In this invention, the server includes a detection means, a receiving means, a generating means, a transmitting means, and a control means, which enables real-time detection of human movement and optimization of air conditioning settings using a generative AI model based on the data, thereby achieving a balance between minimizing power consumption and comfort.

[1417] - "Detection means" means a sensor device for detecting human movement.

[1418] The "receiving means" is a function or device for receiving data from the detecting means.

[1419] The "generation means" refers to a generation AI model and its execution environment for generating air conditioning settings based on the data acquired by the receiving means.

[1420] The "transmission means" is a function or device for transmitting the air conditioning settings generated by the generation means to the air conditioner.

[1421] The "control means" is a function or device for controlling the air conditioner based on the air conditioning settings received from the transmission means.

[1422] A "timestamp" is data that indicates a specific time or date, and is used to record the occurrence time of a detected event or data.

[1423] "Environmental data" refers to data that indicates the surrounding environmental conditions such as temperature and humidity.

[1424] This invention is a system that combines a motion sensor, a server, a generative AI model, a terminal, and an air conditioner to optimize the operation of the air conditioner. The purpose of this system is to minimize energy consumption while maintaining user comfort.

[1425] Initialization Phase

[1426] First, the server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model into memory using TensorFlow or PyTorch. Next, the server initializes an interface for receiving data from multiple sensors and devices. This is done by building a REST API using the Python Flask framework. The server also prepares to use Logstash or Elasticsearch for log management to record the system's operating status.

[1427] The device is set up to ensure the sensor works properly. For example, a Raspberry Pi or Arduino is used, and a PIR motion sensor is connected to the GPIO pin to check operation. The motion sensor is installed on the ceiling or wall of the room and angled to accurately detect user movement.

[1428] Acquiring and Sending Data

[1429] The device equipped with a motion sensor detects user movement in real time. In addition, it simultaneously records environmental data (temperature and humidity) using a DHT22 temperature and humidity sensor. This data is sent to the server at regular intervals. The data is sent using an HTTP POST request.

[1430] The server receives data sent from the device. Using the Flask framework, the received data includes timestamps of the user's movements and environmental data (temperature and humidity). The server preprocesses this data and prepares it for input to the generative AI. Specifically, it converts the data from JSON format to the input format of the generative AI.

[1431] Processing by generative AI

[1432] The server inputs the received data into a generative AI model, which then calculates the optimal air conditioning settings based on this data. The calculation results in the air conditioning unit's temperature settings, airflow settings, operating mode, etc.

[1433] For example, if a motion sensor detects user movement in the morning and the current room temperature is 22 degrees, the generative AI model will instruct the air conditioner to change the setting to 20 degrees. This calculation result is sent to the device.

[1434] Air conditioning control

[1435] The terminal receives the air conditioning setting information sent from the server. After receiving the information, it changes the air conditioning unit settings based on that information. Specifically, this includes adjusting the temperature, airflow, and operating mode. To do this, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate signals to the air conditioning unit.

[1436] Examples of concrete examples and prompts

[1437] In the morning scenario, when a user enters the living room, the device detects the user's movement and sends that data, along with environmental data, to the server. The server inputs the data into the generative AI model and calculates the optimal air conditioning settings. For example, if the room temperature is 22 degrees at 7 a.m., the generative AI will set the air conditioning temperature to 20 degrees. The device that receives this instruction will set the air conditioning temperature to 20 degrees.

[1438] In the night scenario, the same process is repeated when the user enters the bedroom. Based on the "night" condition and other environmental data, the generative AI generates an instruction to set the air conditioner to 23 degrees. The device receives this instruction and adjusts the air conditioner temperature to 23 degrees.

[1439] Example prompt sentence:

[1440] "Optimize air conditioning settings when someone enters the room. Current conditions are 22 degrees and the time is 7am."

[1441] As a result, the system of the present invention collects sensor data in real time and uses generative AI to optimize the operation of air conditioning equipment, thereby achieving both efficient energy consumption and user comfort.

[1442] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1443] Step 1:

[1444] The server loads the generative AI model. Specifically, the server loads a pre-trained generative AI model using TensorFlow or PyTorch into memory. This is done during the initialization phase of the program. The input is the trained data for the model, and the output is a ready-to-run generative AI model.

[1445] Step 2:

[1446] The server initializes the data receiving interface. This is achieved by building a REST API using the Python Flask framework. The input is the interface configuration information, and the output is the endpoint for receiving data.

[1447] Step 3:

[1448] The server initializes the logs and prepares to record system activity. Specifically, a log analysis tool such as Logstash or Elasticsearch is used. The input is a log configuration file, and the output is a log management system ready to record system activity.

[1449] Step 4:

[1450] The terminal sets up the sensor and adjusts the position of the motion sensor. Using Raspberry Pi or Arduino, connect the PIR motion sensor to the GPIO pin and check its operation. The input is the sensor installation information, and the output is a working sensor system.

[1451] Step 5:

[1452] The device detects user movements in real time through a motion sensor. Specifically, when the sensor detects movement, the Raspberry Pi records this data. The input is the user's movement, and the output is the movement detection data.

[1453] Step 6:

[1454] The device also simultaneously records environmental data and sends it to the server at regular intervals. Temperature and humidity are measured using the DHT22 and sent to the server via an HTTP POST request along with PIR motion sensor data. The input is temperature, humidity, and motion detection data, and the output is the sensor data sent to the server.

[1455] Step 7:

[1456] The server receives data sent from the device. It uses Flask as the framework and receives data at the receiving endpoint. The input is the transmitted sensor data, and the output is the received data.

[1457] Step 8:

[1458] The server preprocesses the received data and prepares it for input to the generative AI. It converts the JSON formatted data into a format that the generative AI model can accept. The input is the received JSON data, and the output is the input data for the generative AI.

[1459] Step 9:

[1460] The server inputs data into the generative AI, which then calculates the optimal air conditioning settings. The generative AI model calculates the optimal temperature settings, airflow settings, and operation mode based on the input data. The input is processed environmental data and motion detection data, and the output is air conditioning setting information.

[1461] Step 10:

[1462] The server sends the generated air conditioning setting information to the terminal. The setting information obtained from the generative AI model is converted to JSON format and sent to the terminal. The input is the generated air conditioning setting information, and the output is the setting instructions sent to the terminal.

[1463] Step 11:

[1464] The terminal receives the air conditioning setting information sent from the server. The terminal receives the setting information and stores it in its internal memory. The input is the air conditioning setting information from the server, and the output is the recorded setting information.

[1465] Step 12:

[1466] The terminal changes the air conditioner settings based on the received information. Specifically, it uses an IR remote control module (e.g., Broadlink RM4 mini) to send the appropriate infrared signal to the air conditioner. The input is the saved air conditioner setting information, and the output is the air conditioner with the changed setting.

[1467] (Application example 1)

[1468] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1469] Maintaining the comfort of customers and employees in real-world spaces such as stores while minimizing the energy consumption of air conditioning equipment is a difficult challenge. This becomes particularly complex when different temperatures and airflow volumes need to be adjusted in multiple zones within a store. Conventional air conditioning systems have difficulty responding to these real-time conditions, which can result in energy waste and customer discomfort.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1471] In this invention, the server includes a detection means for detecting human movement, an information processing means for receiving data from the detection means, a generation AI model means for generating optimal air conditioning settings based on the data received by the information processing means, a communication means for transmitting the air conditioning settings generated by the generation AI model means to air conditioning equipment, a control means for controlling the air conditioning equipment, and a means for adjusting the temperature and air volume of multiple zones based on the data. This makes it possible to maintain optimal air conditioning settings in real time in multiple zones within a store, ensuring the comfort of customers and employees while minimizing energy consumption.

[1472] The "detection means" is a sensor device for detecting human movement.

[1473] The "information processing means" is a server or computer system that aggregates data received from the detection means and inputs it into the generative AI model means.

[1474] "Generative AI model means" refers to an artificial intelligence model that calculates optimal air conditioning settings based on data received by the information processing means.

[1475] "Communication means" refers to the network interface and communication protocol used to transmit the air conditioning settings generated by the generation AI model means to the air conditioning equipment.

[1476] "Control means" refers to a system or device for operating and adjusting air conditioning equipment based on air conditioning settings received from communication means.

[1477] "Multiple zones" refers to different areas or sections within a store or other physical space.

[1478] "Real-time" means that data is collected and processed immediately, and air conditioning settings are adjusted immediately based on the results.

[1479] "Minimizing energy consumption" means keeping the energy required to operate air conditioning equipment as low as possible.

[1480] "Comfort" refers to the quality of the indoor environment that keeps customers and employees comfortable.

[1481] A system for realizing this application example includes a detection means, an information processing means, a generative AI model means, a communication means, a control means, and a means for adjusting the temperature and airflow for multiple zones.

[1482] System Configuration

[1483] Detection Methods:

[1484] Motion sensors installed in each zone of the store detect customer movement, allowing information on the presence of people in each zone and environmental data (temperature, humidity, etc.) to be collected in real time.

[1485] Information processing means:

[1486] The data obtained from these sensors is sent to a server, which processes the data in real time and prepares it for input into the generative AI model. Specific data processing includes adding timestamps and converting environmental data formats.

[1487] Generative AI model means:

[1488] The server generates optimal air conditioning settings based on the collected data using a generative AI model that parses and processes prompts based on specific time of day and environmental conditions to ensure user comfort while maximizing energy efficiency.

[1489] Communication Method:

[1490] The generated air conditioning settings are sent to the air conditioning equipment via a communication method, such as Wi-Fi or a wired network.

[1491] Control means:

[1492] The air conditioning equipment adjusts the temperature and airflow based on the received settings. Specifically, each air conditioning equipment operates automatically to maintain optimal air conditioning in each zone.

[1493] Program processing

[1494] The program of this system performs processing in the following manner.

[1495] 1. Data Collection:

[1496] Motion sensors detect customer movements in each zone of the store and send the data to a server.

[1497] 2. Data preprocessing:

[1498] The server formats the received data, adds a timestamp, and organizes the environmental data.

[1499] 3. Input to the generative AI model:

[1500] The server inputs the preprocessed data into a generative AI model, which analyzes the prompt and calculates the optimal air conditioning settings. For example,

[1501] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[1502] 4. Send results:

[1503] The generated air conditioning settings are sent from the server to the air conditioning equipment.

[1504] 5. Air conditioning control:

[1505] The air conditioning equipment automatically adjusts the temperature and airflow according to the received settings. For example, if the store is crowded in the afternoon, the temperature will be set to 22 degrees.

[1506] Specific examples

[1507] For example, if the store starts to get busy at 2 p.m., the motion sensor sends this information to the server. The server automatically uses a generative AI model to calculate the temperature settings for each zone. The server then sends the calculated settings to the air conditioners, which adjust the temperature and airflow for each zone. This minimizes energy consumption while maintaining a comfortable environment for customers and employees.

[1508] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1509] Step 1:

[1510] The detection means collects information on people's movements and presence in each zone, as well as environmental data (temperature, humidity, etc.) in real time. The input is the physical movement of people and environmental information from the detection means (human presence sensors). The output is the collected data (sensor information with a timestamp).

[1511] Step 2:

[1512] The server converts the sensor information received from the detection means into a specified format and adds a timestamp. The input is the sensor information. The output is formatted data (in a format that can be saved in a database).

[1513] Step 3:

[1514] The server prepares the formatted data for input to the generative AI model. Specifically, it combines environmental data (temperature, humidity) and human movement data and converts it into a format that the generative AI model can understand. The input is the formatted data. The output is the input data for the generative AI model.

[1515] Step 4:

[1516] The server sends prompts containing real-time sensor information and environmental data to the generative AI model. For example,

[1517] "It is currently 2:00 PM. The store temperature is 25°C, the humidity is 50%, and the motion sensor data shows there are many customers in all five zones. What is the optimal air conditioning setting to minimize energy consumption while still providing customer comfort?"

[1518] The input is a prompt and sensor information, and the output is the optimal air conditioning settings from a generative AI model.

[1519] Step 5:

[1520] The generative AI model analyzes the prompt text and sensor information, calculates the optimal air conditioning settings (temperature, air volume, operation mode) for each zone, and returns the results to the server. The input is the prompt text to the generative AI model. The output is the optimal air conditioning settings.

[1521] Step 6:

[1522] The server sends the optimal air conditioning settings returned by the generative AI model to the air conditioning equipment. Specifically, it sends instructions to the air conditioning equipment using a communication method. The input is the optimal air conditioning settings. The output is a control instruction to the air conditioning equipment.

[1523] Step 7:

[1524] The air conditioning equipment automatically adjusts the temperature and airflow based on the received optimal air conditioning settings. This optimizes the temperature and airflow for each zone in real time. The input is the control command from the server. The output is the adjusted temperature and airflow for each zone.

[1525] Step 8:

[1526] The user checks that the climate control settings are set appropriately to maintain comfort, and can make manual adjustments if necessary. The input is user feedback. The output is additional setting adjustments.

[1527] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1528] This invention is a system that combines a human presence sensor and an emotion engine to optimize air conditioner settings using generative AI. The purpose of this system is to minimize energy consumption while increasing comfort, taking into account the user's emotional state.

[1529] System Overview

[1530] Initialization Phase

[1531] server:

[1532] The server loads the generative AI model and initializes the system. It also loads the emotion engine so that it can process user emotion data. It also sets up interfaces to receive data from various sensors and devices, and initializes the system log to prepare for recording operation status.

[1533] Device:

[1534] Human sensors and sensors that capture the user's facial expressions and voice are installed in appropriate locations in the room, allowing for real-time detection of human movements and emotional states.

[1535] Acquiring and Sending Data

[1536] Device:

[1537] The motion sensor detects the user's movements. At the same time, the emotion engine analyzes the user's facial expressions and voice to extract emotional data. This information is then packaged into data packets along with a timestamp and sent to the server.

[1538] server:

[1539] The server receives data packets sent from the device and logs the data. The received data includes the user's movements, environmental data (temperature and humidity), and emotional state.

[1540] Processing by generative AI

[1541] server:

[1542] The received data is input into the generation AI, which uses this data to calculate the optimal air conditioner settings taking into account the user's emotional state. The calculation results in the air conditioner's temperature setting, airflow setting, operation mode, etc.

[1543] Examples:

[1544] For example, if the user has a tired expression, the AI ​​generator will set a relaxing temperature and airflow rate. This setting information is sent to the device, and the air conditioner settings are automatically changed.

[1545] Air conditioning control

[1546] Device:

[1547] Receives the air conditioner setting information sent from the server, and then generates specific commands to adjust the air conditioner settings based on that information.

[1548] Examples:

[1549] If the user is feeling stressed before going to bed at night, the emotion engine will detect this and the AI ​​generator will instruct the air conditioner to adjust the temperature to a relaxing 23 degrees. The device will then send this instruction to the air conditioner, changing the setting to 23 degrees.

[1550] Example of operation

[1551] Morning Scenario

[1552] User:

[1553] In the morning, I enter the living room.

[1554] Device:

[1555] A motion sensor installed in the room detects the user's movements, and an emotion engine analyzes their emotional state from their facial expressions.

[1556] server:

[1557] It receives data from sensors and emotion engines and feeds it into generative AI.

[1558] Generation AI:

[1559] Based on the morning state and emotional data, it generates instructions to set the temperature in the living room to 20 degrees.

[1560] Device:

[1561] The device that receives the instruction sets the air conditioner temperature to 20 degrees.

[1562] Night Scenario

[1563] User:

[1564] At night, go into your bedroom before going to sleep.

[1565] Device:

[1566] A motion sensor installed in the bedroom detects the user's movements, and an emotion engine analyzes the user's stress level.

[1567] server:

[1568] It receives data from sensors and emotion engines and feeds it into generative AI.

[1569] Generation AI:

[1570] Based on the time of night and emotional data, instructions are generated to set the air conditioner to a relaxing 23 degrees.

[1571] Device:

[1572] The device receives the instruction and adjusts the air conditioner temperature to 23 degrees.

[1573] In this way, the system of the present invention uses an emotion engine and generative AI to optimize air conditioner settings, thereby achieving efficient use of energy resources and improving user comfort.

[1574] The processing flow will be explained below.

[1575] Step 1:

[1576] Server: Loads the generative AI model and emotion engine, initializes the system, sets up interfaces to receive data from various sensors and devices, and initializes the system log.

[1577] Step 2:

[1578] Terminal: Activate the human presence sensor and emotion sensor installed in the room. The human presence sensor detects the user's movements, and the emotion sensor collects emotion data from the user's facial expressions and voice.

[1579] Step 3:

[1580] Terminal: The motion sensor detects the user's movements, and the emotion sensor analyzes the user's emotional state. This data, along with environmental data (temperature, humidity), is formed into a data packet, which is then time-stamped and sent to the server.

[1581] Step 4:

[1582] Server: Receives and logs data packets sent from the device, including user movement, environmental data, and emotional data.

[1583] Step 5:

[1584] Server: Inputs the received data into the Generative AI and Emotion Engine. The Generative AI calculates the optimal air conditioning settings based on the user's movements and emotional data. The Emotion Engine adjusts the settings output by the Generative AI based on the user's emotional state.

[1585] Step 6:

[1586] Server: Receives the output from the generation AI and the adjustment results from the emotion engine, and creates operating instructions for the air conditioner. It generates instructions including specific settings (temperature, air volume, operation mode) and sends them to the terminal.

[1587] Step 7:

[1588] Server: Sends the generated air conditioner setting instructions to the device. These instructions contain details of how the air conditioner should be set up.

[1589] Step 8:

[1590] Terminal: Receives air conditioner setting instructions sent from the server. Based on that information, it generates specific commands to adjust the air conditioner settings.

[1591] Step 9:

[1592] Terminal: Sends the generated command to the air conditioner control means and changes the air conditioner settings. For example, it sets the air conditioner temperature to 20 or 23 degrees, and adjusts the air volume and operation mode as necessary.

[1593] Step 10:

[1594] Server: Confirms that the air conditioner has started operating based on the new settings and records this in the system log. If necessary, it starts collecting data from the sensors again to prepare for the next cycle.

[1595] Specific examples

[1596] Morning Scenario

[1597] Step 1:

[1598] User: In the morning, I walk into the living room.

[1599] Step 2:

[1600] Terminal: A motion sensor installed in the room detects the user's movements, and an emotion sensor analyzes the user's face.

[1601] Step 3:

[1602] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[1603] Step 4:

[1604] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[1605] Step 5:

[1606] Generative AI: Calculates the optimal temperature setting (e.g., 20 degrees) based on the time of day and the living room conditions. If the emotion engine determines that the user is in a relaxed state, it will fine-tune the temperature setting.

[1607] Step 6:

[1608] Server: Sends the generated air conditioner setting (temperature 20 degrees) to the device.

[1609] Step 7:

[1610] Terminal: Sends instructions to the air conditioner to set the temperature to 20 degrees.

[1611] Night Scenario

[1612] Step 1:

[1613] User: Enters bedroom at night before going to sleep.

[1614] Step 2:

[1615] Device: A motion sensor installed in the bedroom detects the user's movements, and an emotion sensor analyzes the user's face.

[1616] Step 3:

[1617] Terminal: The data detected by the sensor is compiled into data packets and sent to the server.

[1618] Step 4:

[1619] Server: Receives data packets and feeds them into the generative AI and emotion engine.

[1620] Step 5:

[1621] Generative AI: Calculates the optimal temperature setting (e.g., 23°C) based on the time of night and the bedroom conditions. If the emotion engine determines that the user is stressed, it will fine-tune the temperature setting.

[1622] Step 6:

[1623] Server: Sends the generated air conditioner setting (temperature 23 degrees) to the device.

[1624] Step 7:

[1625] Terminal: Sends instructions to the air conditioner to set the temperature to 23 degrees.

[1626] Example 2

[1627] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1628] Conventional air conditioning control systems simply set air conditioning settings based on temperature and humidity without considering the user's emotional state, which can result in insufficient user comfort. Furthermore, they are often not optimized for energy efficiency. Therefore, there is a need for an air conditioning control system that achieves both comfort and energy efficiency while also taking the user's emotional state into account.

[1629] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an emotion analysis means for analyzing the emotional state of the user, an AI generation means for balancing minimizing the air conditioner's power consumption and user comfort, and a central processing unit for processing information including timestamps and environmental data. This enables optimal air conditioner settings that reflect the user's emotional state in real time.

[1630] The "sensor means for detecting human movement" is a device for detecting the movement of a user in a room in real time.

[1631] "Central Processing Unit Means" means a computer system for receiving, analyzing, and processing information from sensor means and other input devices.

[1632] "Generative AI means" is an artificial intelligence technology that calculates and generates optimal air conditioning settings based on received data.

[1633] The "communication terminal means" is a device or system for transmitting the air conditioner settings generated by the generation AI means to the air conditioner.

[1634] The "control device means" is a control unit for controlling the air conditioner based on the air conditioner settings received from the communication terminal means.

[1635] The "emotion analysis means" refers to software and hardware for analyzing the user's facial expressions and voice data to extract the user's emotional state.

[1636] A "timestamp" is information that indicates the date and time when data was recorded or transmitted.

[1637] "Environmental data" is information indicating environmental parameters such as indoor temperature and humidity.

[1638] The present invention proposes a system that analyzes a person's emotional state and optimizes air conditioner settings based on the results. Specific embodiments of the system will be described in detail below.

[1639] Initialization Phase

[1640] server:

[1641] The server first loads a generative AI model. This model incorporates an algorithm that analyzes the user's emotional data and uses the results to determine optimal air conditioning settings. Generative AI models are often implemented using the TensorFlow library. The server then loads an emotion engine, which is responsible for extracting the user's emotional state from facial expressions and voice data. The server also sets up interfaces for receiving data from various sensors and devices, and initializes the system log.

[1642] Device:

[1643] The device is configured to install motion sensors, cameras, and microphones in appropriate locations in the room, enabling real-time detection of people's movements and emotional states. For example, motion sensors are installed in the four corners of the room, cameras on the ceiling, and microphones on the desk. The device checks the operation of these sensors to ensure they are working properly.

[1644] Acquiring and Sending Data

[1645] Device:

[1646] A motion sensor installed on the device detects the user's movements. This data is sent to the device as user movement detection data. At the same time, a camera and microphone capture the user's facial expressions and voice, which the emotion engine analyzes to extract emotional data. For example, if the user is tired, the emotion engine outputs "fatigue" as the emotional data. This data is compiled into a single data packet along with a timestamp and sent to the server.

[1647] server:

[1648] The server receives data packets sent from the device and records them in the system log. The data packets include the user's emotional state based on their movements, facial expressions, and voice data, as well as environmental data (temperature and humidity). For example, the received data is recorded as follows:

[1649] 2023-10-01T08:30:00Z - Motion: Detected, Emotion: Fatigue, Temperature: 22°C, Humidity: 45%

[1650] Processing by generative AI

[1651] server:

[1652] The server inputs the received data into a generative AI model, which then uses this data to calculate optimal air conditioning settings that take into account the user's emotional state and environmental data. For example, if the user is tired, the generative AI model calculates a relaxing temperature of 24 degrees and a medium airflow setting.

[1653] Examples:

[1654] As an example of a prompt, the following text is fed into the generative AI model:

[1655] The user is currently tired and needs to adjust the temperature setting to 24 degrees and the fan speed to medium.

[1656] Air conditioning control

[1657] Device:

[1658] The device receives the air conditioner setting information sent from the server. Based on that information, the device generates specific commands to adjust the air conditioner settings. For example, it generates commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID and sends them to the air conditioner.

[1659] Examples:

[1660] When a user enters their bedroom at night before going to sleep, the motion sensor detects movement and the emotion engine detects "stress." The AI ​​then calculates a relaxing temperature of 23 degrees and sends a command to the air conditioner on the device. As a result, the air conditioner is set to 23 degrees, providing the user with the optimal environment.

[1661] In this way, the system of the present invention can reflect the user's emotional state in real time and achieve optimal air conditioning settings, maximizing energy efficiency while providing a comfortable environment.

[1662] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1663] Step 1:

[1664] Initialize the server:

[1665] The server first loads the generative AI model using the TensorFlow library. Next, it loads the emotion engine, which functions as an emotion analysis means, enabling it to analyze the user's facial expressions and voice data. It also sets up interfaces for receiving data from various sensors and devices. Finally, it initializes the system log, preparing to record the system's operating status.

[1666] Input: Generative AI model, emotion engine

[1667] Output: Loaded model and engine, set up receiving interface

[1668] Step 2:

[1669] Initialize the device:

[1670] The device installs a motion sensor, camera, and microphone in appropriate locations in the room. For example, motion sensors are installed in the four corners of the room, a camera on the ceiling, and a microphone on the desk. Next, the operation of these sensors is checked to ensure they are working properly.

[1671] Input: Motion sensor, camera, microphone

[1672] Output: Installed sensors and operation check report

[1673] Step 3:

[1674] Data acquisition by device:

[1675] A motion sensor installed on the device detects the user's movements. At the same time, a camera and microphone capture the user's facial expressions and voice. This allows the user's movement data, facial expression data, and voice data to be acquired.

[1676] Input: User movements, facial expressions, and voice

[1677] Output: Movement data, facial expression data, audio data

[1678] Step 4:

[1679] Data sent by the device:

[1680] The emotion analysis means analyzes the acquired facial expression data and voice data to extract emotion data. The extracted emotion data, movement data, and environmental data (temperature and humidity) are compiled into a single data packet along with a timestamp and sent to the server.

[1681] Input: movement data, facial expression data, audio data, environmental data

[1682] Output: Data packet (including emotion data, movement data, and environment data)

[1683] Step 5:

[1684] Server receives data:

[1685] The server receives data packets sent from the terminal, records the received data in the system log, and prepares it for analysis.

[1686] Input: Data packet

[1687] Output: Logged data

[1688] Step 6:

[1689] Data input to the server-generated AI model:

[1690] The server inputs the received data into the generative AI model. For example, it inputs a prompt sentence based on the contents of the data packet into the generative AI model.

[1691] Input: Data packet (emotion data, movement data, environmental data)

[1692] Output: Input data to the generative AI model

[1693] Step 7:

[1694] Generative AI model calculates air conditioning settings:

[1695] The generative AI model calculates optimal air conditioning settings based on input data. For example, if the user is tired, it will set the temperature to 24 degrees and the fan speed to medium, which is considered relaxing.

[1696] Input: Input data to the generative AI model

[1697] Output: Calculated air conditioner settings (temperature, airflow, operation mode)

[1698] Step 8:

[1699] Server sends configuration instructions:

[1700] The server sends the calculation results to the device, which then generates specific commands to adjust the air conditioner settings.

[1701] Input: Calculated air conditioner settings

[1702] Output: Configuration instructions to the terminal

[1703] Step 9:

[1704] Applying the settings via terminal:

[1705] The terminal generates control commands for the air conditioner based on the received setting information. The generated commands are sent to the air conditioner to change the settings. For example, commands such as AC_SET_TEMP=24 and AC_SET_FAN=MID are sent to the air conditioner.

[1706] Input: Setting instructions (temperature, air volume, operation mode)

[1707] Output: Air conditioning control command

[1708] In this way, the system operates continuously, automatically adjusting air conditioning settings to reflect the user's emotional state, providing a comfortable environment.

[1709] (Application example 2)

[1710] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1711] In brick-and-mortar stores, air conditioning settings are important for improving customer comfort. However, it has been difficult with conventional technology to grasp the preferences and emotional state of each customer in real time and set the optimal temperature. Furthermore, adjusting the temperature to maintain comfort while minimizing energy consumption is time-consuming and inefficient.

[1712] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a human presence sensor means for detecting human movement, an emotion analysis means for analyzing the emotional state of visitors and generating emotion data, and an AI generation means for generating optimal air conditioner settings based on data received by the network means. This makes it possible to grasp the emotional state and movement of visitors in real time and set the air conditioner optimally. Furthermore, it is possible to achieve both improved comfort for visitors and more efficient power consumption.

[1713] The "human sensor means" is a sensor device for detecting human movement.

[1714] "Network Means" means the communications infrastructure for receiving and transmitting data from sensors and other devices.

[1715] The "generative AI means" is an artificial intelligence system that generates optimal air conditioning settings based on the data it receives.

[1716] The "emotion analysis means" is a device or system for analyzing the facial expressions and voices of customers and generating emotion data.

[1717] The "terminal means" is a device for transmitting the generated air conditioner settings to the air conditioner.

[1718] "Air conditioner control means" refers to a control system for regulating the operation of an air conditioner.

[1719] "Operational means" refers to the means for operating the system with the aim of improving the comfort of customers and streamlining power consumption.

[1720] A "timestamp" is information that indicates the date and time when data was generated or recorded.

[1721] "Environmental data" is information relating to the surrounding environmental conditions such as temperature and humidity.

[1722] To implement this invention, it is necessary to build a system that can balance customer comfort and energy efficiency in a physical store. This system mainly uses the following hardware and software:

[1723] Hardware:

[1724] 1. Human presence sensor: A device that detects human movement within the store.

[1725] 2. Camera and microphone: Devices that capture customers' facial expressions and voices.

[1726] 3. Air conditioner: The air conditioner itself is responsible for adjusting the temperature and airflow according to the system's instructions.

[1727] software:

[1728] 1. Generative AI model: An artificial intelligence system that calculates and generates optimal air conditioner settings based on received data.

[1729] 2. Emotion analysis engine: Generates emotional data from customers' facial expressions and voices.

[1730] 3. Network communication software: This is the communication software used to send data from sensors and analysis engines to the server.

[1731] System operation description:

[1732] The server first loads the generative AI model and emotion analysis engine, which prepares the system for analyzing the emotional state and behavior of customers in real time. Next, motion sensors, cameras, and microphones are installed in appropriate locations within the store, preparing to capture both people's behavior and emotion data in real time.

[1733] The sensor device and analysis engine generate data packets with timestamps based on the detected and analyzed data, which are then sent to a server. The server analyzes the received data and calculates the optimal settings for the air conditioner (temperature, airflow, operation mode, etc.).

[1734] The generated air conditioner settings are sent to the air conditioner via the terminal means, and the air conditioner control means then adjusts the air conditioner based on these settings, ensuring the comfort of customers while also achieving efficient power consumption.

[1735] Examples:

[1736] For example, if there are many customers at lunchtime, the camera and microphone will detect the facial expressions and voices of customers who feel it is hot. Based on this information, the AI ​​will suggest setting the air conditioner temperature to 22 degrees as the optimal setting, and that setting will be reflected on the air conditioner.

[1737] At night, when there are only a few customers, the emotion analysis engine will analyze the relaxed state, and the generative AI will suggest setting the air conditioner to a relaxing temperature (for example, 25 degrees). This setting will also be immediately reflected in the air conditioner.

[1738] Example prompt sentence:

[1739] The prompt for the generative AI model is:

[1740] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[1741] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1742] Step 1:

[1743] The server loads the generative AI model and the sentiment analysis engine. These models are required to analyze the emotional state of customers in real time and generate optimal air conditioning settings. The model file of the sentiment analysis engine and the data of the generative AI model are taken as input, and these are made available as output.

[1744] Step 2:

[1745] The terminal is installed with motion sensors, cameras, and microphones in appropriate locations within the physical store, allowing it to acquire both customer movement and emotional data in real time. The input is information about the installation location of the sensors, cameras, and microphones, and the output is information indicating that installation is complete and the sensor is ready to operate.

[1746] Step 3:

[1747] The motion sensor detects the movement of customers and acquires timestamps and environmental data (current temperature and humidity). This data is packetized as sensor information and sent to the server. The input is raw data from the sensor, and the output is data packets and transmission status.

[1748] Step 4:

[1749] The emotion analysis engine analyzes the facial expressions and voices of customers captured through the camera and microphone to generate emotion data. This data is also packetized with a timestamp and sent to the server. The input is video and audio data, and the output is emotion data packets and transmission status.

[1750] Step 5:

[1751] The server receives data packets sent from the motion sensor and emotion analysis engine and logs them, with the data packets as input and the recorded log data and the data readiness status as output.

[1752] Step 6:

[1753] The generative AI model calculates optimal air conditioner settings based on data stored on the server. Specifically, it analyzes the user's emotions and environmental conditions in the form of prompt sentences based on the emotional and environmental data received as input, and obtains the optimal air conditioner temperature, airflow, and operation mode settings as output.

[1754] Step 7:

[1755] The server sends the air conditioner setting information calculated by the generative AI model to the terminal. The input includes the calculation result of the generative AI model, and the output includes the transmission status and transmission completion notification to the terminal.

[1756] Step 8:

[1757] The terminal adjusts the settings of the air conditioner through the air conditioner control means based on the air conditioner setting information received from the server. The input is the air conditioner setting information, and the output is the adjustment completion and the current setting status of the air conditioner.

[1758] Example prompt sentence:

[1759] The prompt for the generative AI model is:

[1760] "Analyze the user's emotional state and suggest optimal air conditioning settings based on the following information: 1. Detection data from the human presence sensor (presence_data) 2. Analysis data from the emotion engine (emotion_data) 3. Current temperature and humidity inside the store. For example, if a customer is feeling stressed, suggest a temperature and airflow that will help them relax, and if they are relaxed, maintain a comfortable temperature."

[1761] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1762] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1763] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1764] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1765] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1766] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1767] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1768] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1769] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1770] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1771] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1772] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1773] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1774] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1775] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1776] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1777] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1778] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1779] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1780] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1781] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1782] The following is further disclosed regarding the above embodiment.

[1783] (Claim 1)

[1784] a human sensor means for detecting human movement;

[1785] a server means for receiving data from the human presence sensor means;

[1786] A generating AI means for generating optimal settings for an air conditioner based on the data received by the server means;

[1787] a terminal means for transmitting the air conditioner settings generated by the generating AI means to the air conditioner;

[1788] and an air conditioner control means for controlling the air conditioner.

[1789] (Claim 2)

[1790] The system of claim 1, wherein the server means operates the generating AI means to balance minimizing the air conditioner's power consumption and user comfort based on the data received from the human presence sensor means.

[1791] (Claim 3)

[1792] 2. The system of claim 1, wherein the motion sensor means generates sensor information including a timestamp and environmental data, and the server means processes the information.

[1793] "Example 1"

[1794] (Claim 1)

[1795] A detection means for detecting human movement;

[1796] receiving means for receiving data from the detecting means;

[1797] a generating means for generating air conditioning settings based on the data received by the receiving means;

[1798] a transmitting means for transmitting the air conditioning settings generated by the generating means to an air conditioner;

[1799] and a control means for controlling the air conditioner.

[1800] (Claim 2)

[1801] 2. The system according to claim 1, wherein the receiving means operates the generating means to balance minimizing power consumption of the air conditioner and comfort based on the data received from the detecting means.

[1802] (Claim 3)

[1803] 2. The system of claim 1, wherein the sensing means generates information including a timestamp and environmental data, and the receiving means processes the information.

[1804] "Application Example 1"

[1805] (Claim 1)

[1806] A detection means for detecting human movement;

[1807] information processing means for receiving data from the detecting means;

[1808] a generating AI model means for generating optimal air conditioning settings based on the data received by the information processing means;

[1809] a communication means for transmitting the air conditioning settings generated by the generating AI model means to an air conditioning device;

[1810] a control means for controlling the air conditioning equipment;

[1811] The system includes a means for adjusting the temperature and airflow of multiple zones based on said data.

[1812] (Claim 2)

[1813] The system according to claim 1, wherein the information processing means operates the generating AI model means to balance minimizing power consumption of air conditioning equipment and user comfort based on the data received from the detection means.

[1814] (Claim 3)

[1815] 2. The system of claim 1, wherein the detecting means generates sensor information including a timestamp and environmental data, and the information processing means processes the information and collects sensor data in real time to optimize air conditioning settings for multiple zones.

[1816] "Example 2: Combining Emotion Engines"

[1817] (Claim 1)

[1818] a sensor means for detecting human movement;

[1819] central processing unit means for receiving data from said sensor means;

[1820] a generating AI means for generating optimal settings for the air conditioner based on the data received by the central processing unit means;

[1821] a communication terminal means for transmitting the air conditioner settings generated by the generating AI means to the air conditioner;

[1822] a control device for controlling the air conditioner;

[1823] and emotion analysis means for analyzing the emotional state of the user.

[1824] (Claim 2)

[1825] The system of claim 1, wherein the central processing unit means operates the generating AI means to balance minimizing power consumption of the air conditioner and user comfort based on data received from the sensor means and the emotion analysis means.

[1826] (Claim 3)

[1827] 2. The system of claim 1, wherein said sensor means generates information including a timestamp and environmental data, and said central processing unit means processes the information.

[1828] "Application example 2 when combining emotion engines"

[1829] (Claim 1)

[1830] a human sensor means for detecting human movement;

[1831] a network means for receiving data from the human presence sensor means;

[1832] A generating AI means for generating optimal settings for an air conditioner based on the data received by the network means;

[1833] emotion analysis means for analyzing the emotional state of visitors and generating emotion data;

[1834] a terminal means for transmitting the air conditioner settings generated by the generating AI means to the air conditioner;

[1835] air conditioner control means for controlling the air conditioner;

[1836] Operational measures to improve the comfort of customers and to improve the efficiency of power consumption

[1837] A system including:

[1838] (Claim 2)

[1839] The system of claim 1, wherein the network means operates the generating AI means to balance minimizing air conditioner power consumption and the comfort of customers based on data received from the human presence sensor means and emotion analysis means.

[1840] (Claim 3)

[1841] 2. The system according to claim 1, wherein the human presence sensor means generates sensor information including a timestamp and environmental data, the emotion analysis means analyzes facial expressions and voices of customers, and the network means processes the information. [Explanation of symbols]

[1842] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a human sensor means for detecting human movement; a server means for receiving data from the human presence sensor means; A generating AI means for generating optimal settings for an air conditioner based on the data received by the server means; a terminal means for transmitting the air conditioner settings generated by the generating AI means to the air conditioner; and an air conditioner control means for controlling the air conditioner.

2. The system according to claim 1, wherein the server means operates the generating AI means to balance minimizing the air conditioner's power consumption and user comfort based on the data received from the human presence sensor means.

3. 2. The system of claim 1, wherein the motion sensor means generates sensor information including a timestamp and environmental data, and the server means processes the information.

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