system

Abstract

We provide the system. [Solution] An information acquisition device that acquires operation information from the user and transmits said operation information as data, An analysis device that analyzes the aforementioned data using an artificial intelligence model and infers the user's intent, A command generation device that generates operation commands for multiple devices based on the aforementioned inference results and transmits said operation commands, A control device that controls multiple devices in accordance with the aforementioned operation command, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information systems, users need to operate various applications and devices individually, and the complexity of operations is increasing. For this reason, the inability to share information and cooperate with each other between different applications and devices has become a factor hindering the user experience. In addition, in the prior art, there is a lack of means for effectively understanding the user's intention and seamlessly cooperating between multiple devices. Therefore, a new system for managing them in a unified manner and improving the convenience of users is required.

Means for Solving the Problems

[0005] <000003O>This invention provides a system comprising an information acquisition device for handling operation information obtained from a user, a generative artificial intelligence model for analyzing said information, a command generation device for managing operation commands generated based on the analysis results, and a control device for controlling multiple devices. This enables users to coordinate between different applications and devices with a single operation, and realizes an automated operating environment by distributing the generated commands to each device. This simplifies the execution of actions desired by the user and promotes seamless integrated understanding across different technologies.

[0006] An "information acquisition device" is a device that collects user operation information and transmits that information as data to the system.

[0007] A "generative artificial intelligence model" is a model that uses algorithms and processes to analyze collected data and infer user intent and related actions.

[0008] An "analysis device" is a device that uses a generative artificial intelligence model to analyze data and interpret the user's behavioral intentions.

[0009] A "command generation device" is a device that generates operation commands for multiple devices based on the inference results obtained through analysis, and transmits them at the appropriate timing.

[0010] A "control device" is a device that receives operation commands and appropriately controls multiple devices accordingly.

[0011] "Operation information" is a term that refers to data generated when a user operates a specific system or application.

[0012] "Equipment" refers to physical devices or software systems designed to receive operation commands transmitted from a command generation device and to operate accordingly. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system that efficiently manages and coordinates multiple applications and devices that a user operates on a daily basis, through collaboration between the user, terminal, and server. The system aims to improve user convenience by acquiring user operation information and generating and executing appropriate operation commands based on that information.

[0035] Users operate various applications using devices such as smartphones and tablets. These devices transmit user operation data to a server. After receiving the data, the server analyzes it using a generative artificial intelligence model to infer the user's intent. For example, if a user selects "away mode," the server infers a series of actions associated with this mode (e.g., turning off lights, stopping the air conditioner, activating the security system).

[0036] Based on the inference results, the server generates the necessary commands for operation via a command generation device and sends them back to the terminal. The terminal then distributes and controls the commands to each IoT device in order to execute the transmitted commands. This allows the user to automatically adjust all related devices to the desired state with just the selection of "away mode".

[0037] As a concrete example, when a user presses the "Leave" button on their smartphone app to go to work, the server generates a command to turn off all relevant devices in the home and sends it to the device. The device then executes the command sequentially, turning off the lights, the air conditioner, and locking the doors. This allows the user to leave home with peace of mind.

[0038] As described above, the system of the present invention provides a concrete form for conveniently and efficiently managing the user's life by combining command generation based on inference by a generative artificial intelligence model with the control of multiple devices by a terminal.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches a smartphone application and selects a specific mode (e.g., "Away Mode"). This selection allows the application to collect data indicating the user's intent.

[0042] Step 2:

[0043] The terminal initiates communication processing to send data containing user selection information to the server. This data includes the selected mode and related context information.

[0044] Step 3:

[0045] The server receives data sent from the terminal and inputs it into a generative artificial intelligence model. The server uses this model to analyze the user's intent and perform inferences based on that intent. Here, it determines the action associated with each mode.

[0046] Step 4:

[0047] The server generates a series of operation commands using a command generation device based on the inference results. Each command is designed to cause multiple IoT devices to perform a specific action. The generated commands are then sent to the terminals.

[0048] Step 5:

[0049] The terminal receives commands from the server and applies them to each IoT device. Each device then starts performing actions according to the received commands, such as turning off lights or stopping air conditioners.

[0050] Step 6:

[0051] The terminal monitors responses from each device and feeds back the operation results to the server based on those responses. This information is used to verify that the commands were executed correctly.

[0052] Step 7:

[0053] Through a smartphone app, users can verify that all commands have been executed and that each device is functioning as expected. The verification results serve as feedback to help users act with confidence.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern times, it is difficult for users to efficiently manage multiple home appliances and electronic devices, and the effort required to operate them individually is increasing. Furthermore, it is currently difficult to accurately understand the user's intentions and control devices accordingly. Therefore, there is a need for systems that simplify user operation and enable more efficient and automated control of multiple devices.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes data transmission means for acquiring operation information from the user and transmitting it as data, data analysis means for analyzing the received data and inferring the user's intent, and command generation means for generating operation commands based on the inference results. This enables efficient control of the device based on the user's intent.

[0059] A "data transmission means" is a device that has the function of transmitting user operation information to a central device via a terminal.

[0060] A "data analysis means" is a device that analyzes received operation information using a generating artificial intelligence model and has the function of inferring the user's intent.

[0061] A "command generation means" is a device that has the function of generating specific operation commands for multiple pieces of equipment based on the analyzed inference results.

[0062] A "command transmission means" is a device that has the function of transmitting the generated operation command to the target equipment.

[0063] "Equipment control means" refers to a device that has the function of controlling the operation of multiple pieces of equipment in accordance with the user's operation commands.

[0064] A "central device" is the central device that receives operation information from the user, analyzes it, generates commands, and transmits commands.

[0065] A "generative artificial intelligence model" is a model that utilizes artificial intelligence technology to analyze user interaction data and infer intent.

[0066] This invention is a system that enables efficient management and coordination of multiple devices in a user's daily life through the cooperation of a user, a terminal, and a server. The user operates various applications using a terminal such as a smartphone or tablet. The terminal acquires this operation information and transmits it as data to the server.

[0067] The server analyzes the received data using an artificial intelligence model to infer the user's intent. In this process, the server uses the prompt "Please tell me what action should be taken when the user selects 'away mode'," and the model infers the appropriate command.

[0068] Based on the inference results, the server uses a command generation device to generate specific operation commands for multiple devices. These generated commands are then sent to the terminal. The terminal receives these commands, distributes them to each IoT device, and controls each device accordingly.

[0069] For example, if the user selects "away mode," the server generates a series of commands, such as turning off the lights, stopping the air conditioner, and activating the security system, and sends them to the terminal. The terminal then executes these commands, automatically adjusting each specified device to the desired state.

[0070] As a result, users can efficiently manage multiple devices with simple operations, improving the convenience and comfort of their daily lives.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user selects a specific operating mode by operating an application on their smartphone or tablet. The input is the user's selected mode information, which the device then receives.

[0074] Step 2:

[0075] The terminal sends the acquired operation information as data to the server. The input is user operation information, and the output is accurate data sent to the server. This process is handled by a data communication module.

[0076] Step 3:

[0077] The server uses a generative AI model to analyze the received operation data. The input is operation data sent from the terminal, and the output is the result of inferring the user's intent. In this analysis, the inference is performed using the prompt message "Please tell me what action should be taken when the user selects away mode."

[0078] Step 4:

[0079] Based on the analysis results, the server uses a command generation device to generate specific operation commands for multiple pieces of equipment. The input is the result of inferring the user's intent, and the output is operation commands for multiple pieces of equipment. In this step, it is determined which piece of equipment each command instructs to perform what operation.

[0080] Step 5:

[0081] The server sends the generated commands to the terminal. The input is the operation commands generated by the server, and the output is the commands accurately delivered to the terminal. This communication takes place through a command transmission module.

[0082] Step 6:

[0083] The terminal controls each IoT device based on the commands it receives. The input is operation commands received from the server, and the output is each device starting to act according to those commands. Specific actions include turning off lights, shutting off air conditioners, and activating security systems.

[0084] Through the steps described above, users can automatically manage multiple pieces of equipment within their environment with simple operations.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] Many current smart home systems face the challenge of requiring separate applications for each individual device, making integrated operation difficult. Furthermore, manually operating each security device every time a user leaves the home is cumbersome and inconvenient. Therefore, there is a need for a system that allows users to easily control multiple security devices in an integrated manner, enabling them to leave home with peace of mind.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes information acquisition means for acquiring operation information from a user and transmitting said operation information as data; analysis means for analyzing said data using a generating artificial intelligence model and inferring the user's intent; and security control means for generating operation commands for a plurality of remotely located security devices based on the inference results and transmitting said operation commands. This makes it possible for a user to easily control a series of security devices through a smart device and manage security efficiently and centrally.

[0090] "Operation information" refers to data generated when a user operates a device, and it reflects the user's intentions.

[0091] "Information acquisition means" refers to a means of acquiring user operation information and transmitting that information as data within the system.

[0092] "Analysis means" refers to a method for analyzing user interaction information using a generating artificial intelligence model and inferring the user's intent.

[0093] The "command generation means" is a means for generating and transmitting operation commands to multiple devices based on the inference results of the analysis means.

[0094] "Control means" refers to means for controlling multiple devices in accordance with operation commands transmitted from command generation means.

[0095] "Security control means" refers to means for generating operation commands for multiple remotely located security devices based on the inference results of the analysis means, and for transmitting and controlling said commands.

[0096] A "generative artificial intelligence model" is an artificial intelligence model used to analyze user interaction information and infer their intentions.

[0097] The system for implementing this invention begins with the user providing operation information through a device such as a smartphone. When the user presses, for example, the "Go Out" button, that operation information is transmitted to the server through the information acquisition means. The server uses the acquired operation information to utilize a generation artificial intelligence model to infer the user's intent. Based on the inferred intent, the command generation means generates appropriate operation commands for multiple security devices and transmits those commands through the control means.

[0098] Specific hardware includes smartphones and IoT devices (e.g., smart locks, security cameras, alarm systems). Software-wise, a program using Python to send HTTP requests functions, for example, by using the requests library to send commands to IoT devices. Furthermore, a generative artificial intelligence model analyzes user intent and optimizes device operation according to the situation.

[0099] For example, when a user presses the "Security Activate" button on a smartphone app, all doors in the house automatically lock, security cameras begin recording, and alarms are activated. This feature allows users to leave their homes with peace of mind.

[0100] An example of a prompt to input into the generating AI model is, "I want to enhance the security of my home while I'm out, so please implement a system that automatically locks the doors, starts camera recording, and activates an alarm with the press of a button on my smartphone."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user inputs operational information using a smartphone application. This input information includes mode selections such as "Going Out." This operational information is transmitted from the terminal to the server via an information acquisition mechanism. The input is the user's selection information, and the output is the data transmitted to the server.

[0104] Step 2:

[0105] The server inputs the received operation information into a generating artificial intelligence model to infer the user's intent. The input here is operation information sent from the terminal, and the AI ​​model performs data analysis based on this data. The output is the inferred user intent.

[0106] Step 3:

[0107] Based on the inference results, the server uses a command generation mechanism to generate specific operating commands for multiple security devices. The input is the inferred user intent, and data processing involves command translation within the system. The output is the operating commands for the security devices.

[0108] Step 4:

[0109] Upon receiving operation commands sent to the terminal, the control system sends signals to each IoT device. Specifically, these include actions such as locking a smart lock, starting recording on a security camera, and activating an alarm. The input is the operation command sent from the server, and the output is the state change of the IoT device.

[0110] Step 5:

[0111] Users can monitor the operating status of each device in real time through the application. The terminal receives feedback information from IoT devices and reports its status to the server. The input is the feedback information from each device, and the output is the reflection of the feedback data to the server.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention is an advanced information system that incorporates an emotion engine that recognizes user emotions and integrates various information acquisition devices, analysis devices, command generation devices, and control devices. Its purpose is to significantly improve the user experience by generating optimal operation commands based on user emotion data and efficiently controlling various devices.

[0114] When a user uses a device such as a smartphone, this system extracts the user's selected mode, voice, facial expressions, and biometric information. This data is sent to an emotion engine, where the user's emotions are analyzed. The server integrates the emotion data obtained from the emotion engine with the user's operation information and performs analysis using a generative artificial intelligence model. From the results of the analysis, the system infers the operation commands that the user desires or needs.

[0115] The command generation device generates operation commands that are best suited to the user's emotional state based on inference. These commands are then distributed to each IoT device via the terminal. As a result, the device state can be automatically set to the optimal state for the user's environment.

[0116] As a concrete example, suppose a user returns home and desires a "relaxation mode" to reduce stress. When the user inputs this into the device, the device uses an emotion engine to understand the user's current emotional state from their facial expressions and tone of voice. The server analyzes the emotional data and this selection, generates commands to change the lighting to softer light and play calming music, and then executes them. In this way, a comfortable and relaxing environment is automatically created for the user.

[0117] As described above, the system incorporating the emotion engine of the present invention has the capability to realize interactions that take into account the user's emotional state, and to provide a user experience that is both convenient and satisfying.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] Upon returning home, the user selects "Relax Mode" through a smartphone application. This selection records the user's preferred mode on the device.

[0121] Step 2:

[0122] The device transmits the user's voice, facial expressions, and biometric information to the emotion engine. This prepares the device to analyze the user's current emotional state.

[0123] Step 3:

[0124] The emotion engine analyzes the user's emotions from the received data and sends that information to the server as emotion data. This data indicates the user's stress level and need for relaxation.

[0125] Step 4:

[0126] The server acquires emotional data from the emotion engine and user mode selection information, and analyzes them using a generative artificial intelligence model. The server then infers the optimal environment settings for the user's state.

[0127] Step 5:

[0128] Based on the inference results, the server generates specific control commands, such as adjusting the lighting intensity or playing music. These commands take into account the user's mental state.

[0129] Step 6:

[0130] The server sends the generated operation commands to the terminal. The terminal receives these commands and transmits them to each IoT device.

[0131] Step 7:

[0132] Each IoT device operates according to commands from the terminal. For example, smart lighting might be set to soft light, or a music system might play relaxation music.

[0133] Step 8:

[0134] The user receives a notification via their device when the setup is complete. This lets the user know that the entire home environment has been automatically configured.

[0135] Through this series of steps, the system creates an environment that matches the user's emotional state, providing a comfortable living experience.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0138] In today's world, enabling automatic and optimal device operation that responds to a user's emotional state is crucial for improving the user experience. However, existing systems often fail to accurately analyze emotional data and generate optimal commands based on that analysis, making it difficult to properly reflect the user's intentions.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for transmitting various biometric information and voice and facial expression data acquired from the user to an emotion engine; means for analyzing the user's emotional state using the emotion engine and inferring the emotional state using an artificial intelligence model; and means for generating optimal operation commands for multiple devices based on the user's emotional state and selected mode using the inference results. This enables automatic and optimal device control based on emotions, accurately reflecting the user's intentions.

[0141] A "user" is an entity that uses a system to select an operating mode and inputs their own emotions and state of mind.

[0142] A "terminal" is a device that receives input from the user, acquires voice, facial expressions, and biometric information, and is a communication device that transmits data to the emotion engine and receives and executes control commands.

[0143] An "emotion engine" is an analytical device with specialized functions to analyze a user's voice, facial expressions, biometric information, etc., in order to identify and infer the user's emotional state.

[0144] A "generative AI model" is an artificial intelligence system that recognizes patterns in emotional states by learning from large amounts of data, and predicts and infers user intentions.

[0145] An "operation command" is a set of instructions that specify the control actions to be performed on multiple devices based on the user's emotional state or selected mode.

[0146] A "server" is a computing device that receives data from the emotion engine and performs analysis using a generative AI model, acting as the central hub responsible for controlling and coordinating the entire system.

[0147] "Devices" are internet-connected devices controlled by a system, and are various types of equipment that create a specific environment based on user commands.

[0148] This invention is an advanced information system designed to enhance the user experience. Users can interact with this system using devices such as smartphones and tablets. The device collects the user's voice, facial expressions, and biometric information and transmits it to a server in the cloud.

[0149] The server analyzes the transmitted information using an emotion engine and identifies the user's emotional state using a generative artificial intelligence model. Then, based on the emotional state and the mode selected by the user, it generates the optimal operation command and distributes the command to each device via the terminal.

[0150] The terminal distributes commands received from the server to various internet-connected devices (e.g., smart home appliances and audio systems) to execute them. This allows for environment settings tailored to the user's emotions.

[0151] For example, if a user selects "Relax Mode," the device sends voice tone and facial expression data to the emotion engine to confirm that the user desires a relaxed state. The server analyzes the data using a generative AI model and instructs the device to adjust the room lighting to a warmer color and play relaxation music.

[0152] An example of a prompt statement is: "What environmental settings would be appropriate if the user wanted to relax? Assuming the user desires relaxation based on their tone of voice and facial expression, please suggest a suitable command." By inputting this prompt statement into the generating AI model, the system can infer and execute the action most appropriate to the user's emotional state.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The user selects the desired mode of operation using a device such as a smartphone or tablet. During this process, voice, facial expressions, and biometric information (e.g., heart rate) are collected via the device's microphone and camera. The user's voice, video, and biometric data are captured by the device as input. As output, this data is prepared for transmission.

[0156] Step 2:

[0157] The device packages the collected voice, facial expressions, and biometric information and sends it to a server in the cloud. The input consists of various data stored within the device. This data is sent to the cloud according to a transmission protocol, and processing begins on the server side. The output is the transmission of data to the server.

[0158] Step 3:

[0159] The server inputs the received data into the emotion engine. Here, various data is analyzed, and a generative AI model is used to infer the user's emotional state. The input is packaged data sent from the terminal. The data is analyzed by the emotion engine, and emotional patterns are identified using a specific algorithm. The output is the analyzed emotional state information.

[0160] Step 4:

[0161] The server uses the emotional state obtained from the emotion engine, analyzes it with a generative AI model, and determines an appropriate action command for the user's state. The input is the inferred emotional state. At this stage, the generative AI model performs analysis based on the prompt text and generates a specific set of commands. The output is the optimized set of commands.

[0162] Step 5:

[0163] The terminal receives a set of commands from the server and distributes them to each IoT device. The input is operation commands from the server. As output, these commands are executed on various devices, and the user's environment is automatically adjusted.

[0164] Step 6:

[0165] The user's surrounding environment changes based on commands. This process might involve, for example, the lighting changing to a different color temperature, or the music being adjusted to a relaxation mode. The output is a physical environment optimized for the user, thereby improving the user experience.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In physical stores, accurately understanding each customer's emotional state and providing appropriate product suggestions and environmental adjustments in real time was difficult. Traditional systems failed to fully understand customer intentions and emotions, resulting in uniform service provision. Therefore, there was a need to improve customer satisfaction and achieve flexible responses tailored to individual needs.

[0169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0170] In this invention, the server includes data collection means for acquiring operation information and biometric information from the user, aggregating and transmitting the information; data analysis means for analyzing the data using an artificial intelligence model that generates the data and inferring the user's emotional state; and command generation means for generating optimal operation commands for multiple functions based on the inference results and transmitting the operation commands. This makes it possible to make appropriate product suggestions and environmental adjustments in real time according to the customer's emotions.

[0171] "Data collection means" refers to a device or system that has the function of acquiring user operation information and biometric information, aggregating this information, and transmitting it to a server.

[0172] "Data analysis means" refers to a device or software equipped with the function of analyzing acquired data using an artificial intelligence model that generates data, and inferring the user's emotional state.

[0173] A "command generation means" is a device or system for generating optimal operation commands for multiple functions based on the results of data analysis and transmitting them to each device.

[0174] "Environmental control means" refers to a device or system that has the function of controlling multiple environmental elements, such as lighting and music, according to the user's operation commands.

[0175] A "generating artificial intelligence model" refers to artificial intelligence technology and its algorithms used to analyze acquired data and infer user emotions and intentions.

[0176] To realize this invention, it is necessary to construct an advanced information system that integrates various devices. The server acquires operation information and biometric information from the user through data collection means. This uses mobile terminals such as smart glasses and smartphones. The devices are equipped with cameras and microphones and collect biometric data such as facial expressions, voice, and heart rate in real time.

[0177] The collected data is processed on servers located in the cloud. These servers analyze the acquired data using artificial intelligence models to infer the user's emotional state. Examples of the artificial intelligence technologies used include Microsoft® Azure® Emotion API and Google® Cloud Vision.

[0178] After the data analysis is complete, the server uses a command generation system to generate optimal operation commands corresponding to the inferred emotions. These operation commands are sent to IoT devices in the store, specifically to lights, speakers, and other devices. Smart home apps such as Samsung SmartThings are used to control these devices.

[0179] For example, when a customer enters a physical store, smart glasses can instantly determine their emotional state and adjust the store's lighting to a warmer color and play soothing music to help them relax. This system can provide an optimal environment tailored to the customer's emotions in real time.

[0180] Example prompt: "Based on the customer's facial expressions and vital data, evaluate the customer's emotional state and generate device commands to provide an optimal relaxation environment. Then, create a protocol to execute those commands."

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The device acquires the user's biometric and operational information. It receives input such as facial expressions and voice data captured by cameras and microphones on smart glasses or smartphones, as well as heart rate measured by vital sensors. This data is temporarily stored and prepared for transmission to a server.

[0184] Step 2:

[0185] The server receives data transmitted from the terminal. The received input data is processed into an appropriate format and analyzed using a generative AI model. Here, the data analysis method infers the user's emotional state, and the inference result is generated as output.

[0186] Step 3:

[0187] The server generates optimal operation commands using a command generation mechanism based on the inference results. The input is analyzed emotion data, and the output is specific operation commands to adjust the environment. It generates prompt statements and prepares them to be sent as commands to various devices.

[0188] Step 4:

[0189] The server sends the generated commands to IoT devices in the store via a terminal. It uses the operation instructions created by the command generation system as input, and the output is used to operate lighting, sound systems, etc. Specifically, it issues commands to change the color temperature of the lighting or the playback content of the sound system.

[0190] Step 5:

[0191] After a user enters the store, the environment is adjusted in real time to provide an optimal environment tailored to the user. For example, if a user wants to relax, the lighting will be set to warm colors and relaxing music will be played. As a result, users can enjoy browsing products in a comfortable environment.

[0192] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0193] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0198] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0199] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0200] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0202] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0203] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0204] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0205] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0206] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0207] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0208] This invention is a system that efficiently manages and coordinates multiple applications and devices that a user operates on a daily basis, through collaboration between the user, terminal, and server. The system aims to improve user convenience by acquiring user operation information and generating and executing appropriate operation commands based on that information.

[0209] Users operate various applications using devices such as smartphones and tablets. These devices transmit user operation data to a server. After receiving the data, the server analyzes it using a generative artificial intelligence model to infer the user's intent. For example, if a user selects "away mode," the server infers a series of actions associated with this mode (e.g., turning off lights, stopping the air conditioner, activating the security system).

[0210] Based on the inference results, the server generates the necessary commands for operation via a command generation device and sends them back to the terminal. The terminal then distributes and controls the commands to each IoT device in order to execute the transmitted commands. This allows the user to automatically adjust all related devices to the desired state with just the selection of "away mode".

[0211] As a concrete example, when a user presses the "Leave" button on their smartphone app to go to work, the server generates a command to turn off all relevant devices in the home and sends it to the device. The device then executes the command sequentially, turning off the lights, the air conditioner, and locking the doors. This allows the user to leave home with peace of mind.

[0212] As described above, the system of the present invention provides a concrete form for conveniently and efficiently managing the user's life by combining command generation based on inference by a generative artificial intelligence model with the control of multiple devices by a terminal.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The user launches a smartphone application and selects a specific mode (e.g., "Away Mode"). This selection allows the application to collect data indicating the user's intent.

[0216] Step 2:

[0217] The terminal initiates communication processing to send data containing user selection information to the server. This data includes the selected mode and related context information.

[0218] Step 3:

[0219] The server receives data sent from the terminal and inputs it into a generative artificial intelligence model. The server uses this model to analyze the user's intent and perform inferences based on that intent. Here, it determines the action associated with each mode.

[0220] Step 4:

[0221] The server generates a series of operation commands using a command generation device based on the inference results. Each command is designed to cause multiple IoT devices to perform a specific action. The generated commands are then sent to the terminals.

[0222] Step 5:

[0223] The terminal receives commands from the server and applies them to each IoT device. Each device then starts performing actions according to the received commands, such as turning off lights or stopping air conditioners.

[0224] Step 6:

[0225] The terminal monitors responses from each device and feeds back the operation results to the server based on those responses. This information is used to verify that the commands were executed correctly.

[0226] Step 7:

[0227] Through a smartphone app, users can verify that all commands have been executed and that each device is functioning as expected. The verification results serve as feedback to help users act with confidence.

[0228] (Example 1)

[0229] Next, we will describe Example 1. 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."

[0230] In modern times, it is difficult for users to efficiently manage multiple home appliances and electronic devices, and the effort required to operate them individually is increasing. Furthermore, it is currently difficult to accurately understand the user's intentions and control devices accordingly. Therefore, there is a need for systems that simplify user operation and enable more efficient and automated control of multiple devices.

[0231] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0232] In this invention, the server includes data transmission means for acquiring operation information from the user and transmitting it as data, data analysis means for analyzing the received data and inferring the user's intent, and command generation means for generating operation commands based on the inference results. This enables efficient control of the device based on the user's intent.

[0233] A "data transmission means" is a device that has the function of transmitting user operation information to a central device via a terminal.

[0234] A "data analysis means" is a device that analyzes received operation information using a generating artificial intelligence model and has the function of inferring the user's intent.

[0235] A "command generation means" is a device that has the function of generating specific operation commands for multiple pieces of equipment based on the analyzed inference results.

[0236] A "command transmission means" is a device that has the function of transmitting the generated operation command to the target equipment.

[0237] "Equipment control means" refers to a device that has the function of controlling the operation of multiple pieces of equipment in accordance with the user's operation commands.

[0238] A "central device" is the central device that receives operation information from the user, analyzes it, generates commands, and transmits commands.

[0239] A "generative artificial intelligence model" is a model that utilizes artificial intelligence technology to analyze user interaction data and infer intent.

[0240] This invention is a system that enables efficient management and coordination of multiple devices in a user's daily life through the cooperation of a user, a terminal, and a server. The user operates various applications using a terminal such as a smartphone or tablet. The terminal acquires this operation information and transmits it as data to the server.

[0241] The server analyzes the received data using an artificial intelligence model to infer the user's intent. In this process, the server uses the prompt "Please tell me what action should be taken when the user selects 'away mode'," and the model infers the appropriate command.

[0242] Based on the inference results, the server uses a command generation device to generate specific operation commands for multiple devices. These generated commands are then sent to the terminal. The terminal receives these commands, distributes them to each IoT device, and controls each device accordingly.

[0243] For example, if the user selects "away mode," the server generates a series of commands, such as turning off the lights, stopping the air conditioner, and activating the security system, and sends them to the terminal. The terminal then executes these commands, automatically adjusting each specified device to the desired state.

[0244] As a result, users can efficiently manage multiple devices with simple operations, improving the convenience and comfort of their daily lives.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The user selects a specific operating mode by operating an application on their smartphone or tablet. The input is the user's selected mode information, which the device then receives.

[0248] Step 2:

[0249] The terminal sends the acquired operation information as data to the server. The input is user operation information, and the output is accurate data sent to the server. This process is handled by a data communication module.

[0250] Step 3:

[0251] The server uses a generative AI model to analyze the received operation data. The input is operation data sent from the terminal, and the output is the result of inferring the user's intent. In this analysis, the inference is performed using the prompt message "Please tell me what action should be taken when the user selects away mode."

[0252] Step 4:

[0253] Based on the analysis results, the server uses a command generation device to generate specific operation commands for multiple pieces of equipment. The input is the result of inferring the user's intent, and the output is operation commands for multiple pieces of equipment. In this step, it is determined which piece of equipment each command instructs to perform what operation.

[0254] Step 5:

[0255] The server sends the generated commands to the terminal. The input is the operation commands generated by the server, and the output is the commands accurately delivered to the terminal. This communication takes place through a command transmission module.

[0256] Step 6:

[0257] The terminal controls each IoT device based on the commands it receives. The input is operation commands received from the server, and the output is each device starting to act according to those commands. Specific actions include turning off lights, shutting off air conditioners, and activating security systems.

[0258] Through the steps described above, users can automatically manage multiple pieces of equipment within their environment with simple operations.

[0259] (Application Example 1)

[0260] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] Many current smart home systems face the challenge of requiring separate applications for each individual device, making integrated operation difficult. Furthermore, manually operating each security device every time a user leaves the home is cumbersome and inconvenient. Therefore, there is a need for a system that allows users to easily control multiple security devices in an integrated manner, enabling them to leave home with peace of mind.

[0262] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0263] In this invention, the server includes information acquisition means for acquiring operation information from a user and transmitting said operation information as data; analysis means for analyzing said data using a generating artificial intelligence model and inferring the user's intent; and security control means for generating operation commands for a plurality of remotely located security devices based on the inference results and transmitting said operation commands. This makes it possible for a user to easily control a series of security devices through a smart device and manage security efficiently and centrally.

[0264] "Operation information" refers to data generated when a user operates a device, and it reflects the user's intentions.

[0265] "Information acquisition means" refers to a means of acquiring user operation information and transmitting that information as data within the system.

[0266] "Analysis means" refers to a method for analyzing user interaction information using a generating artificial intelligence model and inferring the user's intent.

[0267] The "command generation means" is a means for generating and transmitting operation commands to multiple devices based on the inference results of the analysis means.

[0268] "Control means" refers to means for controlling multiple devices in accordance with operation commands transmitted from command generation means.

[0269] "Security control means" refers to means for generating operation commands for multiple remotely located security devices based on the inference results of the analysis means, and for transmitting and controlling said commands.

[0270] A "generative artificial intelligence model" is an artificial intelligence model used to analyze user interaction information and infer their intentions.

[0271] The system for implementing this invention begins with the user providing operation information through a device such as a smartphone. When the user presses, for example, the "Go Out" button, that operation information is transmitted to the server through the information acquisition means. The server uses the acquired operation information to utilize a generation artificial intelligence model to infer the user's intent. Based on the inferred intent, the command generation means generates appropriate operation commands for multiple security devices and transmits those commands through the control means.

[0272] Specific hardware includes smartphones and IoT devices (e.g., smart locks, security cameras, alarm systems). Software-wise, a program using Python to send HTTP requests functions, for example, by using the requests library to send commands to IoT devices. Furthermore, a generative artificial intelligence model analyzes user intent and optimizes device operation according to the situation.

[0273] For example, when a user presses the "Security Activate" button on a smartphone app, all doors in the house automatically lock, security cameras begin recording, and alarms are activated. This feature allows users to leave their homes with peace of mind.

[0274] An example of a prompt to input into the generating AI model is, "I want to enhance the security of my home while I'm out, so please implement a system that automatically locks the doors, starts camera recording, and activates an alarm with the press of a button on my smartphone."

[0275] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0276] Step 1:

[0277] The user uses the smartphone application to input operation information. This input information includes mode selections such as "going out". This operation information is sent by the terminal to the server via the information acquisition means. The input is the user's selection information, and the output is the data transmitted to the server.

[0278] Step 2:

[0279] The server inputs the received operation information into the generated artificial intelligence model to infer the user's intention. The input here is the operation information transmitted from the terminal, and based on this data, the AI model performs data analysis. The output is the inferred user's intention.

[0280] Step 3:

[0281] Based on the inference result, the server uses the command generation means to generate specific operation commands for multiple security facilities. The input is the inferred user's intention, and in data processing, command translation within the system is performed. The output is the operation command to the security facilities.

[0282] Step 4:

[0283] Upon receiving the operation command sent to the terminal, the control means sends a signal to each IoT device. Specifically, operations such as locking the smart lock, starting recording of the surveillance camera, and activating the alarm are included. The input is the operation command transmitted from the server, and the output is the state change of the IoT device.

[0284] Step 5:

[0285] The user can monitor the operation status of each device in real time through the application. The terminal receives the feedback information of the IoT device and reports the status to the server. The input is the feedback information from each device, and the output is the reflection of the feedback data to the server.

[0286] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0287] The present invention is an advanced information system that incorporates an emotion engine for recognizing the user's emotion and integrates various information acquisition devices, analysis devices, command generation devices, and control devices. For the purpose of significantly improving the user experience, it aims to generate optimal operation commands based on the user's emotion data and efficiently control various devices.

[0288] When the user uses a terminal such as a smartphone, the system extracts the mode selected by the user, voice, expression, biometric information, etc. These data are sent to the emotion engine, and the user's emotion is analyzed. The server integrates the emotion data obtained from the emotion engine and the user's operation information and performs analysis using the generated artificial intelligence model. From the results of the analysis, the operation commands desired or required by the user are inferred.

[0289] The command generation device generates the operation command most suitable for the user's emotional state based on the inference. These commands are distributed to each IoT device through the terminal again. As a result, the state of the device optimal for the user environment can be automatically set.

[0290] As a specific example, assume that the user returns home and wishes to activate the "relaxation mode" to relieve stress. When the user enters this into the terminal, the terminal utilizes the emotion engine to understand the current emotional state from the user's expression and voice tone. The server analyzes the emotion data and this selection, generates commands to change the lighting to a soft brightness and play quiet music, and executes them accordingly. In this way, a comfortable and relaxation - suitable environment is automatically arranged for the user.

[0291] As described above, the system incorporating the emotion engine of the present invention has the capability to realize interactions that take into account the user's emotional state, and to provide a user experience that is both convenient and satisfying.

[0292] The following describes the processing flow.

[0293] Step 1:

[0294] Upon returning home, the user selects "Relax Mode" through a smartphone application. This selection records the user's preferred mode on the device.

[0295] Step 2:

[0296] The device transmits the user's voice, facial expressions, and biometric information to the emotion engine. This prepares the device to analyze the user's current emotional state.

[0297] Step 3:

[0298] The emotion engine analyzes the user's emotions from the received data and sends that information to the server as emotion data. This data indicates the user's stress level and need for relaxation.

[0299] Step 4:

[0300] The server acquires emotional data from the emotion engine and user mode selection information, and analyzes them using a generative artificial intelligence model. The server then infers the optimal environment settings for the user's state.

[0301] Step 5:

[0302] Based on the inference results, the server generates specific control commands, such as adjusting the lighting intensity or playing music. These commands take into account the user's mental state.

[0303] Step 6:

[0304] The server sends the generated operation instructions to the terminal. The terminal receives these and transmits the instructions to each IoT device.

[0305] Step 7:

[0306] Each IoT device operates according to the instructions from the terminal. For example, the smart lighting is set to soft light, and the music system plays relaxation music.

[0307] Step 8:

[0308] The user receives a notification through the terminal that the setting is complete. Thereby, the user can know that the environment of the whole house has been automatically arranged.

[0309] Through this series of steps, this system realizes the creation of an environment according to the user's emotional state and provides a comfortable living experience.

[0310] (Example 2)

[0311] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] In modern times, realizing automatic and optimal operation of devices according to the user's emotional state is important for improving the user experience. However, in existing systems, there are problems that accurate analysis of emotional data and generation of optimal instructions based on it are not sufficiently carried out, and it is difficult to appropriately reflect the user's intentions.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0314] In this invention, the server includes means for transmitting various biometric information and voice and facial expression data acquired from the user to an emotion engine; means for analyzing the user's emotional state using the emotion engine and inferring the emotional state using an artificial intelligence model; and means for generating optimal operation commands for multiple devices based on the user's emotional state and selected mode using the inference results. This enables automatic and optimal device control based on emotions, accurately reflecting the user's intentions.

[0315] A "user" is an entity that uses a system to select an operating mode and inputs their own emotions and state of mind.

[0316] A "terminal" is a device that receives input from the user, acquires voice, facial expressions, and biometric information, and is a communication device that transmits data to the emotion engine and receives and executes control commands.

[0317] An "emotion engine" is an analytical device with specialized functions to analyze a user's voice, facial expressions, biometric information, etc., in order to identify and infer the user's emotional state.

[0318] A "generative AI model" is an artificial intelligence system that recognizes patterns in emotional states by learning from large amounts of data, and predicts and infers user intentions.

[0319] An "operation command" is a set of instructions that specify the control actions to be performed on multiple devices based on the user's emotional state or selected mode.

[0320] A "server" is a computing device that receives data from the emotion engine and performs analysis using a generative AI model, acting as the central hub responsible for controlling and coordinating the entire system.

[0321] "Devices" are internet-connected devices controlled by a system, and are various types of equipment that create a specific environment based on user commands.

[0322] This invention is an advanced information system designed to enhance the user experience. Users can interact with this system using devices such as smartphones and tablets. The device collects the user's voice, facial expressions, and biometric information and transmits it to a server in the cloud.

[0323] The server analyzes the transmitted information using an emotion engine and identifies the user's emotional state using a generative artificial intelligence model. Then, based on the emotional state and the mode selected by the user, it generates the optimal operation command and distributes the command to each device via the terminal.

[0324] The terminal distributes commands received from the server to various internet-connected devices (e.g., smart home appliances and audio systems) to execute them. This allows for environment settings tailored to the user's emotions.

[0325] For example, if a user selects "Relax Mode," the device sends voice tone and facial expression data to the emotion engine to confirm that the user desires a relaxed state. The server analyzes the data using a generative AI model and instructs the device to adjust the room lighting to a warmer color and play relaxation music.

[0326] An example of a prompt statement is: "What environmental settings would be appropriate if the user wanted to relax? Assuming the user desires relaxation based on their tone of voice and facial expression, please suggest a suitable command." By inputting this prompt statement into the generating AI model, the system can infer and execute the action most appropriate to the user's emotional state.

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] The user selects the desired mode of operation using a device such as a smartphone or tablet. During this process, voice, facial expressions, and biometric information (e.g., heart rate) are collected via the device's microphone and camera. The user's voice, video, and biometric data are captured by the device as input. As output, this data is prepared for transmission.

[0330] Step 2:

[0331] The device packages the collected voice, facial expressions, and biometric information and sends it to a server in the cloud. The input consists of various data stored within the device. This data is sent to the cloud according to a transmission protocol, and processing begins on the server side. The output is the transmission of data to the server.

[0332] Step 3:

[0333] The server inputs the received data into the emotion engine. Here, various data is analyzed, and a generative AI model is used to infer the user's emotional state. The input is packaged data sent from the terminal. The data is analyzed by the emotion engine, and emotional patterns are identified using a specific algorithm. The output is the analyzed emotional state information.

[0334] Step 4:

[0335] The server uses the emotional state obtained from the emotion engine, analyzes it with a generative AI model, and determines an appropriate action command for the user's state. The input is the inferred emotional state. At this stage, the generative AI model performs analysis based on the prompt text and generates a specific set of commands. The output is the optimized set of commands.

[0336] Step 5:

[0337] The terminal receives a set of commands from the server and distributes them to each IoT device. The input is operation commands from the server. As output, these commands are executed on various devices, and the user's environment is automatically adjusted.

[0338] Step 6:

[0339] The user's surrounding environment changes based on commands. This process might involve, for example, the lighting changing to a different color temperature, or the music being adjusted to a relaxation mode. The output is a physical environment optimized for the user, thereby improving the user experience.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0342] In physical stores, accurately understanding each customer's emotional state and providing appropriate product suggestions and environmental adjustments in real time was difficult. Traditional systems failed to fully understand customer intentions and emotions, resulting in uniform service provision. Therefore, there was a need to improve customer satisfaction and achieve flexible responses tailored to individual needs.

[0343] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0344] In this invention, the server includes data collection means for acquiring operation information and biometric information from the user, aggregating and transmitting the information; data analysis means for analyzing the data using an artificial intelligence model that generates the data and inferring the user's emotional state; and command generation means for generating optimal operation commands for multiple functions based on the inference results and transmitting the operation commands. This makes it possible to make appropriate product suggestions and environmental adjustments in real time according to the customer's emotions.

[0345] "Data collection means" refers to a device or system that has the function of acquiring user operation information and biometric information, aggregating this information, and transmitting it to a server.

[0346] "Data analysis means" refers to a device or software equipped with the function of analyzing acquired data using an artificial intelligence model that generates data, and inferring the user's emotional state.

[0347] A "command generation means" is a device or system for generating optimal operation commands for multiple functions based on the results of data analysis and transmitting them to each device.

[0348] "Environmental control means" refers to a device or system that has the function of controlling multiple environmental elements, such as lighting and music, according to the user's operation commands.

[0349] A "generating artificial intelligence model" refers to artificial intelligence technology and its algorithms used to analyze acquired data and infer user emotions and intentions.

[0350] To realize this invention, it is necessary to construct an advanced information system that integrates various devices. The server acquires operation information and biometric information from the user through data collection means. This uses mobile terminals such as smart glasses and smartphones. The devices are equipped with cameras and microphones and collect biometric data such as facial expressions, voice, and heart rate in real time.

[0351] The collected data is processed on servers located in the cloud. These servers analyze the acquired data using artificial intelligence models to infer the user's emotional state. Examples of AI technologies used include Microsoft Azure Emotion API and Google Cloud Vision.

[0352] After the data analysis is complete, the server uses a command generation system to generate optimal operation commands corresponding to the inferred emotions. These operation commands are sent to IoT devices in the store, specifically to lights, speakers, and other devices. Smart home apps such as Samsung SmartThings are used to control these devices.

[0353] For example, when a customer enters a physical store, smart glasses can instantly determine their emotional state and adjust the store's lighting to a warmer color and play soothing music to help them relax. This system can provide an optimal environment tailored to the customer's emotions in real time.

[0354] Example prompt: "Based on the customer's facial expressions and vital data, evaluate the customer's emotional state and generate device commands to provide an optimal relaxation environment. Then, create a protocol to execute those commands."

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Step 1:

[0357] The device acquires the user's biometric and operational information. It receives input such as facial expressions and voice data captured by cameras and microphones on smart glasses or smartphones, as well as heart rate measured by vital sensors. This data is temporarily stored and prepared for transmission to a server.

[0358] Step 2:

[0359] The server receives data transmitted from the terminal. The received input data is processed into an appropriate format and analyzed using a generative AI model. Here, the data analysis method infers the user's emotional state, and the inference result is generated as output.

[0360] Step 3:

[0361] The server generates optimal operation commands using a command generation mechanism based on the inference results. The input is analyzed emotion data, and the output is specific operation commands to adjust the environment. It generates prompt statements and prepares them to be sent as commands to various devices.

[0362] Step 4:

[0363] The server sends the generated commands to IoT devices in the store via a terminal. It uses the operation instructions created by the command generation system as input, and the output is used to operate lighting, sound systems, etc. Specifically, it issues commands to change the color temperature of the lighting or the playback content of the sound system.

[0364] Step 5:

[0365] After a user enters the store, the environment is adjusted in real time to provide an optimal environment tailored to the user. For example, if a user wants to relax, the lighting will be set to warm colors and relaxing music will be played. As a result, users can enjoy browsing products in a comfortable environment.

[0366] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0367] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0369] [Third Embodiment]

[0370] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0371] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0372] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0374] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0376] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0377] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0378] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0379] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0381] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0382] This invention is a system that efficiently manages and coordinates multiple applications and devices that a user operates on a daily basis, through collaboration between the user, terminal, and server. The system aims to improve user convenience by acquiring user operation information and generating and executing appropriate operation commands based on that information.

[0383] Users operate various applications using devices such as smartphones and tablets. These devices transmit user operation data to a server. After receiving the data, the server analyzes it using a generative artificial intelligence model to infer the user's intent. For example, if a user selects "away mode," the server infers a series of actions associated with this mode (e.g., turning off lights, stopping the air conditioner, activating the security system).

[0384] Based on the inference results, the server generates the necessary commands for operation via a command generation device and sends them back to the terminal. The terminal then distributes and controls the commands to each IoT device in order to execute the transmitted commands. This allows the user to automatically adjust all related devices to the desired state with just the selection of "away mode".

[0385] As a concrete example, when a user presses the "Leave" button on their smartphone app to go to work, the server generates a command to turn off all relevant devices in the home and sends it to the device. The device then executes the command sequentially, turning off the lights, the air conditioner, and locking the doors. This allows the user to leave home with peace of mind.

[0386] As described above, the system of the present invention provides a concrete form for conveniently and efficiently managing the user's life by combining command generation based on inference by a generative artificial intelligence model with the control of multiple devices by a terminal.

[0387] The following describes the processing flow.

[0388] Step 1:

[0389] The user launches a smartphone application and selects a specific mode (e.g., "Away Mode"). This selection allows the application to collect data indicating the user's intent.

[0390] Step 2:

[0391] The terminal initiates communication processing to send data containing user selection information to the server. This data includes the selected mode and related context information.

[0392] Step 3:

[0393] The server receives data sent from the terminal and inputs it into a generative artificial intelligence model. The server uses this model to analyze the user's intent and perform inferences based on that intent. Here, it determines the action associated with each mode.

[0394] Step 4:

[0395] The server generates a series of operation commands using a command generation device based on the inference results. Each command is designed to cause multiple IoT devices to perform a specific action. The generated commands are then sent to the terminals.

[0396] Step 5:

[0397] The terminal receives commands from the server and applies them to each IoT device. Each device then starts performing actions according to the received commands, such as turning off lights or stopping air conditioners.

[0398] Step 6:

[0399] The terminal monitors responses from each device and feeds back the operation results to the server based on those responses. This information is used to verify that the commands were executed correctly.

[0400] Step 7:

[0401] Through a smartphone app, users can verify that all commands have been executed and that each device is functioning as expected. The verification results serve as feedback to help users act with confidence.

[0402] (Example 1)

[0403] Next, we will describe Example 1. 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."

[0404] In modern times, it is difficult for users to efficiently manage multiple home appliances and electronic devices, and the effort required to operate them individually is increasing. Furthermore, it is currently difficult to accurately understand the user's intentions and control devices accordingly. Therefore, there is a need for systems that simplify user operation and enable more efficient and automated control of multiple devices.

[0405] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0406] In this invention, the server includes data transmission means for acquiring operation information from the user and transmitting it as data, data analysis means for analyzing the received data and inferring the user's intent, and command generation means for generating operation commands based on the inference results. This enables efficient control of the device based on the user's intent.

[0407] A "data transmission means" is a device that has the function of transmitting user operation information to a central device via a terminal.

[0408] A "data analysis means" is a device that analyzes received operation information using a generating artificial intelligence model and has the function of inferring the user's intent.

[0409] A "command generation means" is a device that has the function of generating specific operation commands for multiple pieces of equipment based on the analyzed inference results.

[0410] A "command transmission means" is a device that has the function of transmitting the generated operation command to the target equipment.

[0411] "Equipment control means" refers to a device that has the function of controlling the operation of multiple pieces of equipment in accordance with the user's operation commands.

[0412] A "central device" is the central device that receives operation information from the user, analyzes it, generates commands, and transmits commands.

[0413] A "generative artificial intelligence model" is a model that utilizes artificial intelligence technology to analyze user interaction data and infer intent.

[0414] This invention is a system that enables efficient management and coordination of multiple devices in a user's daily life through the cooperation of a user, a terminal, and a server. The user operates various applications using a terminal such as a smartphone or tablet. The terminal acquires this operation information and transmits it as data to the server.

[0415] The server analyzes the received data using an artificial intelligence model to infer the user's intent. In this process, the server uses the prompt "Please tell me what action should be taken when the user selects 'away mode'," and the model infers the appropriate command.

[0416] Based on the inference results, the server uses a command generation device to generate specific operation commands for multiple devices. These generated commands are then sent to the terminal. The terminal receives these commands, distributes them to each IoT device, and controls each device accordingly.

[0417] For example, if the user selects "away mode," the server generates a series of commands, such as turning off the lights, stopping the air conditioner, and activating the security system, and sends them to the terminal. The terminal then executes these commands, automatically adjusting each specified device to the desired state.

[0418] As a result, users can efficiently manage multiple devices with simple operations, improving the convenience and comfort of their daily lives.

[0419] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0420] Step 1:

[0421] The user selects a specific operating mode by operating an application on their smartphone or tablet. The input is the user's selected mode information, which the device then receives.

[0422] Step 2:

[0423] The terminal sends the acquired operation information as data to the server. The input is user operation information, and the output is accurate data sent to the server. This process is handled by a data communication module.

[0424] Step 3:

[0425] The server uses a generative AI model to analyze the received operation data. The input is operation data sent from the terminal, and the output is the result of inferring the user's intent. In this analysis, the inference is performed using the prompt message "Please tell me what action should be taken when the user selects away mode."

[0426] Step 4:

[0427] Based on the analysis results, the server uses a command generation device to generate specific operation commands for multiple pieces of equipment. The input is the result of inferring the user's intent, and the output is operation commands for multiple pieces of equipment. In this step, it is determined which piece of equipment each command instructs to perform what operation.

[0428] Step 5:

[0429] The server sends the generated commands to the terminal. The input is the operation commands generated by the server, and the output is the commands accurately delivered to the terminal. This communication takes place through a command transmission module.

[0430] Step 6:

[0431] The terminal controls each IoT device based on the commands it receives. The input is operation commands received from the server, and the output is each device starting to act according to those commands. Specific actions include turning off lights, shutting off air conditioners, and activating security systems.

[0432] Through the steps described above, users can automatically manage multiple pieces of equipment within their environment with simple operations.

[0433] (Application Example 1)

[0434] Next, we will explain Application Example 1. In the following explanation, 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."

[0435] Many current smart home systems face the challenge of requiring separate applications for each individual device, making integrated operation difficult. Furthermore, manually operating each security device every time a user leaves the home is cumbersome and inconvenient. Therefore, there is a need for a system that allows users to easily control multiple security devices in an integrated manner, enabling them to leave home with peace of mind.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0437] In this invention, the server includes information acquisition means for acquiring operation information from a user and transmitting said operation information as data; analysis means for analyzing said data using a generating artificial intelligence model and inferring the user's intent; and security control means for generating operation commands for a plurality of remotely located security devices based on the inference results and transmitting said operation commands. This makes it possible for a user to easily control a series of security devices through a smart device and manage security efficiently and centrally.

[0438] "Operation information" refers to data generated when a user operates a device, and it reflects the user's intentions.

[0439] "Information acquisition means" refers to a means of acquiring user operation information and transmitting that information as data within the system.

[0440] "Analysis means" refers to a method for analyzing user interaction information using a generating artificial intelligence model and inferring the user's intent.

[0441] The "command generation means" is a means for generating and transmitting operation commands to multiple devices based on the inference results of the analysis means.

[0442] "Control means" refers to means for controlling multiple devices in accordance with operation commands transmitted from command generation means.

[0443] "Security control means" refers to means for generating operation commands for multiple remotely located security devices based on the inference results of the analysis means, and for transmitting and controlling said commands.

[0444] A "generative artificial intelligence model" is an artificial intelligence model used to analyze user interaction information and infer their intentions.

[0445] The system for implementing this invention begins with the user providing operation information through a device such as a smartphone. When the user presses, for example, the "Go Out" button, that operation information is transmitted to the server through the information acquisition means. The server uses the acquired operation information to utilize a generation artificial intelligence model to infer the user's intent. Based on the inferred intent, the command generation means generates appropriate operation commands for multiple security devices and transmits those commands through the control means.

[0446] Specific hardware includes smartphones and IoT devices (e.g., smart locks, security cameras, alarm systems). Software-wise, a program using Python to send HTTP requests functions, for example, by using the requests library to send commands to IoT devices. Furthermore, a generative artificial intelligence model analyzes user intent and optimizes device operation according to the situation.

[0447] For example, when a user presses the "Security Activate" button on a smartphone app, all doors in the house automatically lock, security cameras begin recording, and alarms are activated. This feature allows users to leave their homes with peace of mind.

[0448] An example of a prompt to input into the generating AI model is, "I want to enhance the security of my home while I'm out, so please implement a system that automatically locks the doors, starts camera recording, and activates an alarm with the press of a button on my smartphone."

[0449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0450] Step 1:

[0451] The user inputs operational information using a smartphone application. This input information includes mode selections such as "Going Out." This operational information is transmitted from the terminal to the server via an information acquisition mechanism. The input is the user's selection information, and the output is the data transmitted to the server.

[0452] Step 2:

[0453] The server inputs the received operation information into a generating artificial intelligence model to infer the user's intent. The input here is operation information sent from the terminal, and the AI ​​model performs data analysis based on this data. The output is the inferred user intent.

[0454] Step 3:

[0455] Based on the inference results, the server uses a command generation mechanism to generate specific operating commands for multiple security devices. The input is the inferred user intent, and data processing involves command translation within the system. The output is the operating commands for the security devices.

[0456] Step 4:

[0457] Upon receiving operation commands sent to the terminal, the control system sends signals to each IoT device. Specifically, these include actions such as locking a smart lock, starting recording on a security camera, and activating an alarm. The input is the operation command sent from the server, and the output is the state change of the IoT device.

[0458] Step 5:

[0459] Users can monitor the operating status of each device in real time through the application. The terminal receives feedback information from IoT devices and reports its status to the server. The input is the feedback information from each device, and the output is the reflection of the feedback data to the server.

[0460] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0461] This invention is an advanced information system that incorporates an emotion engine that recognizes user emotions and integrates various information acquisition devices, analysis devices, command generation devices, and control devices. Its purpose is to significantly improve the user experience by generating optimal operation commands based on user emotion data and efficiently controlling various devices.

[0462] When a user uses a device such as a smartphone, this system extracts the user's selected mode, voice, facial expressions, and biometric information. This data is sent to an emotion engine, where the user's emotions are analyzed. The server integrates the emotion data obtained from the emotion engine with the user's operation information and performs analysis using a generative artificial intelligence model. From the results of the analysis, the system infers the operation commands that the user desires or needs.

[0463] The command generation device generates operation commands that are best suited to the user's emotional state based on inference. These commands are then distributed to each IoT device via the terminal. As a result, the device state can be automatically set to the optimal state for the user's environment.

[0464] As a concrete example, suppose a user returns home and desires a "relaxation mode" to reduce stress. When the user inputs this into the device, the device uses an emotion engine to understand the user's current emotional state from their facial expressions and tone of voice. The server analyzes the emotional data and this selection, generates commands to change the lighting to softer light and play calming music, and then executes them. In this way, a comfortable and relaxing environment is automatically created for the user.

[0465] As described above, the system incorporating the emotion engine of the present invention has the capability to realize interactions that take into account the user's emotional state, and to provide a user experience that is both convenient and satisfying.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] Upon returning home, the user selects "Relax Mode" through a smartphone application. This selection records the user's preferred mode on the device.

[0469] Step 2:

[0470] The device transmits the user's voice, facial expressions, and biometric information to the emotion engine. This prepares the device to analyze the user's current emotional state.

[0471] Step 3:

[0472] The emotion engine analyzes the user's emotions from the received data and sends that information to the server as emotion data. This data indicates the user's stress level and need for relaxation.

[0473] Step 4:

[0474] The server acquires emotional data from the emotion engine and user mode selection information, and analyzes them using a generative artificial intelligence model. The server then infers the optimal environment settings for the user's state.

[0475] Step 5:

[0476] Based on the inference results, the server generates specific control commands, such as adjusting the lighting intensity or playing music. These commands take into account the user's mental state.

[0477] Step 6:

[0478] The server sends the generated operation commands to the terminal. The terminal receives these commands and transmits them to each IoT device.

[0479] Step 7:

[0480] Each IoT device operates according to commands from the terminal. For example, smart lighting might be set to soft light, or a music system might play relaxation music.

[0481] Step 8:

[0482] The user receives a notification via their device when the setup is complete. This lets the user know that the entire home environment has been automatically configured.

[0483] Through this series of steps, the system creates an environment that matches the user's emotional state, providing a comfortable living experience.

[0484] (Example 2)

[0485] Next, we will describe Example 2. 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."

[0486] In today's world, enabling automatic and optimal device operation that responds to a user's emotional state is crucial for improving the user experience. However, existing systems often fail to accurately analyze emotional data and generate optimal commands based on that analysis, making it difficult to properly reflect the user's intentions.

[0487] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0488] In this invention, the server includes means for transmitting various biometric information and voice and facial expression data acquired from the user to an emotion engine; means for analyzing the user's emotional state using the emotion engine and inferring the emotional state using an artificial intelligence model; and means for generating optimal operation commands for multiple devices based on the user's emotional state and selected mode using the inference results. This enables automatic and optimal device control based on emotions, accurately reflecting the user's intentions.

[0489] A "user" is an entity that uses a system to select an operating mode and inputs their own emotions and state of mind.

[0490] A "terminal" is a device that receives input from the user, acquires voice, facial expressions, and biometric information, and is a communication device that transmits data to the emotion engine and receives and executes control commands.

[0491] An "emotion engine" is an analytical device with specialized functions to analyze a user's voice, facial expressions, biometric information, etc., in order to identify and infer the user's emotional state.

[0492] A "generative AI model" is an artificial intelligence system that recognizes patterns in emotional states by learning from large amounts of data, and predicts and infers user intentions.

[0493] An "operation command" is a set of instructions that specify the control actions to be performed on multiple devices based on the user's emotional state or selected mode.

[0494] A "server" is a computing device that receives data from the emotion engine and performs analysis using a generative AI model, acting as the central hub responsible for controlling and coordinating the entire system.

[0495] "Devices" are internet-connected devices controlled by a system, and are various types of equipment that create a specific environment based on user commands.

[0496] This invention is an advanced information system designed to enhance the user experience. Users can interact with this system using devices such as smartphones and tablets. The device collects the user's voice, facial expressions, and biometric information and transmits it to a server in the cloud.

[0497] The server analyzes the transmitted information using an emotion engine and identifies the user's emotional state using a generative artificial intelligence model. Then, based on the emotional state and the mode selected by the user, it generates the optimal operation command and distributes the command to each device via the terminal.

[0498] The terminal distributes commands received from the server to various internet-connected devices (e.g., smart home appliances and audio systems) to execute them. This allows for environment settings tailored to the user's emotions.

[0499] For example, if a user selects "Relax Mode," the device sends voice tone and facial expression data to the emotion engine to confirm that the user desires a relaxed state. The server analyzes the data using a generative AI model and instructs the device to adjust the room lighting to a warmer color and play relaxation music.

[0500] An example of a prompt statement is: "What environmental settings would be appropriate if the user wanted to relax? Assuming the user desires relaxation based on their tone of voice and facial expression, please suggest a suitable command." By inputting this prompt statement into the generating AI model, the system can infer and execute the action most appropriate to the user's emotional state.

[0501] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0502] Step 1:

[0503] The user selects the desired mode of operation using a device such as a smartphone or tablet. During this process, voice, facial expressions, and biometric information (e.g., heart rate) are collected via the device's microphone and camera. The user's voice, video, and biometric data are captured by the device as input. As output, this data is prepared for transmission.

[0504] Step 2:

[0505] The device packages the collected voice, facial expressions, and biometric information and sends it to a server in the cloud. The input consists of various data stored within the device. This data is sent to the cloud according to a transmission protocol, and processing begins on the server side. The output is the transmission of data to the server.

[0506] Step 3:

[0507] The server inputs the received data into the emotion engine. Here, various data is analyzed, and a generative AI model is used to infer the user's emotional state. The input is packaged data sent from the terminal. The data is analyzed by the emotion engine, and emotional patterns are identified using a specific algorithm. The output is the analyzed emotional state information.

[0508] Step 4:

[0509] The server uses the emotional state obtained from the emotion engine, analyzes it with a generative AI model, and determines an appropriate action command for the user's state. The input is the inferred emotional state. At this stage, the generative AI model performs analysis based on the prompt text and generates a specific set of commands. The output is the optimized set of commands.

[0510] Step 5:

[0511] The terminal receives a set of commands from the server and distributes them to each IoT device. The input is operation commands from the server. As output, these commands are executed on various devices, and the user's environment is automatically adjusted.

[0512] Step 6:

[0513] The user's surrounding environment changes based on commands. This process might involve, for example, the lighting changing to a different color temperature, or the music being adjusted to a relaxation mode. The output is a physical environment optimized for the user, thereby improving the user experience.

[0514] (Application Example 2)

[0515] Next, we will explain Application Example 2. In the following explanation, 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."

[0516] In physical stores, accurately understanding each customer's emotional state and providing appropriate product suggestions and environmental adjustments in real time was difficult. Traditional systems failed to fully understand customer intentions and emotions, resulting in uniform service provision. Therefore, there was a need to improve customer satisfaction and achieve flexible responses tailored to individual needs.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0518] In this invention, the server includes data collection means for acquiring operation information and biometric information from the user, aggregating and transmitting the information; data analysis means for analyzing the data using an artificial intelligence model that generates the data and inferring the user's emotional state; and command generation means for generating optimal operation commands for multiple functions based on the inference results and transmitting the operation commands. This makes it possible to make appropriate product suggestions and environmental adjustments in real time according to the customer's emotions.

[0519] "Data collection means" refers to a device or system that has the function of acquiring user operation information and biometric information, aggregating this information, and transmitting it to a server.

[0520] "Data analysis means" refers to a device or software equipped with the function of analyzing acquired data using an artificial intelligence model that generates data, and inferring the user's emotional state.

[0521] A "command generation means" is a device or system for generating optimal operation commands for multiple functions based on the results of data analysis and transmitting them to each device.

[0522] "Environmental control means" refers to a device or system that has the function of controlling multiple environmental elements, such as lighting and music, according to the user's operation commands.

[0523] A "generating artificial intelligence model" refers to artificial intelligence technology and its algorithms used to analyze acquired data and infer user emotions and intentions.

[0524] To realize this invention, it is necessary to construct an advanced information system that integrates various devices. The server acquires operation information and biometric information from the user through data collection means. This uses mobile terminals such as smart glasses and smartphones. The devices are equipped with cameras and microphones and collect biometric data such as facial expressions, voice, and heart rate in real time.

[0525] The collected data is processed on servers located in the cloud. These servers analyze the acquired data using artificial intelligence models to infer the user's emotional state. Examples of AI technologies used include Microsoft Azure Emotion API and Google Cloud Vision.

[0526] After the data analysis is complete, the server uses a command generation system to generate optimal operation commands corresponding to the inferred emotions. These operation commands are sent to IoT devices in the store, specifically to lights, speakers, and other devices. Smart home apps such as Samsung SmartThings are used to control these devices.

[0527] For example, when a customer enters a physical store, smart glasses can instantly determine their emotional state and adjust the store's lighting to a warmer color and play soothing music to help them relax. This system can provide an optimal environment tailored to the customer's emotions in real time.

[0528] Example prompt: "Based on the customer's facial expressions and vital data, evaluate the customer's emotional state and generate device commands to provide an optimal relaxation environment. Then, create a protocol to execute those commands."

[0529] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0530] Step 1:

[0531] The device acquires the user's biometric and operational information. It receives input such as facial expressions and voice data captured by cameras and microphones on smart glasses or smartphones, as well as heart rate measured by vital sensors. This data is temporarily stored and prepared for transmission to a server.

[0532] Step 2:

[0533] The server receives data transmitted from the terminal. The received input data is processed into an appropriate format and analyzed using a generative AI model. Here, the data analysis method infers the user's emotional state, and the inference result is generated as output.

[0534] Step 3:

[0535] The server generates optimal operation commands using a command generation mechanism based on the inference results. The input is analyzed emotion data, and the output is specific operation commands to adjust the environment. It generates prompt statements and prepares them to be sent as commands to various devices.

[0536] Step 4:

[0537] The server sends the generated commands to IoT devices in the store via a terminal. It uses the operation instructions created by the command generation system as input, and the output is used to operate lighting, sound systems, etc. Specifically, it issues commands to change the color temperature of the lighting or the playback content of the sound system.

[0538] Step 5:

[0539] After a user enters the store, the environment is adjusted in real time to provide an optimal environment tailored to the user. For example, if a user wants to relax, the lighting will be set to warm colors and relaxing music will be played. As a result, users can enjoy browsing products in a comfortable environment.

[0540] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0541] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0542] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0543] [Fourth Embodiment]

[0544] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0545] As shown in Figure 7, the 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.

[0546] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0547] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0548] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0549] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0550] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0551] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0552] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0553] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0554] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0555] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0556] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0557] This invention is a system that efficiently manages and coordinates multiple applications and devices that a user operates on a daily basis, through collaboration between the user, terminal, and server. The system aims to improve user convenience by acquiring user operation information and generating and executing appropriate operation commands based on that information.

[0558] Users operate various applications using devices such as smartphones and tablets. These devices transmit user operation data to a server. After receiving the data, the server analyzes it using a generative artificial intelligence model to infer the user's intent. For example, if a user selects "away mode," the server infers a series of actions associated with this mode (e.g., turning off lights, stopping the air conditioner, activating the security system).

[0559] Based on the inference results, the server generates the necessary commands for operation via a command generation device and sends them back to the terminal. The terminal then distributes and controls the commands to each IoT device in order to execute the transmitted commands. This allows the user to automatically adjust all related devices to the desired state with just the selection of "away mode".

[0560] As a concrete example, when a user presses the "Leave" button on their smartphone app to go to work, the server generates a command to turn off all relevant devices in the home and sends it to the device. The device then executes the command sequentially, turning off the lights, the air conditioner, and locking the doors. This allows the user to leave home with peace of mind.

[0561] As described above, the system of the present invention provides a concrete form for conveniently and efficiently managing the user's life by combining command generation based on inference by a generative artificial intelligence model with the control of multiple devices by a terminal.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] The user launches a smartphone application and selects a specific mode (e.g., "Away Mode"). This selection allows the application to collect data indicating the user's intent.

[0565] Step 2:

[0566] The terminal initiates communication processing to send data containing user selection information to the server. This data includes the selected mode and related context information.

[0567] Step 3:

[0568] The server receives data sent from the terminal and inputs it into a generative artificial intelligence model. The server uses this model to analyze the user's intent and perform inferences based on that intent. Here, it determines the action associated with each mode.

[0569] Step 4:

[0570] The server generates a series of operation commands using a command generation device based on the inference results. Each command is designed to cause multiple IoT devices to perform a specific action. The generated commands are then sent to the terminals.

[0571] Step 5:

[0572] The terminal receives commands from the server and applies them to each IoT device. Each device then starts performing actions according to the received commands, such as turning off lights or stopping air conditioners.

[0573] Step 6:

[0574] The terminal monitors responses from each device and feeds back the operation results to the server based on those responses. This information is used to verify that the commands were executed correctly.

[0575] Step 7:

[0576] Through a smartphone app, users can verify that all commands have been executed and that each device is functioning as expected. The verification results serve as feedback to help users act with confidence.

[0577] (Example 1)

[0578] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0579] In modern times, it is difficult for users to efficiently manage multiple home appliances and electronic devices, and the effort required to operate them individually is increasing. Furthermore, it is currently difficult to accurately understand the user's intentions and control devices accordingly. Therefore, there is a need for systems that simplify user operation and enable more efficient and automated control of multiple devices.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0581] In this invention, the server includes data transmission means for acquiring operation information from the user and transmitting it as data, data analysis means for analyzing the received data and inferring the user's intent, and command generation means for generating operation commands based on the inference results. This enables efficient control of the device based on the user's intent.

[0582] A "data transmission means" is a device that has the function of transmitting user operation information to a central device via a terminal.

[0583] A "data analysis means" is a device that analyzes received operation information using a generating artificial intelligence model and has the function of inferring the user's intent.

[0584] A "command generation means" is a device that has the function of generating specific operation commands for multiple pieces of equipment based on the analyzed inference results.

[0585] A "command transmission means" is a device that has the function of transmitting the generated operation command to the target equipment.

[0586] "Equipment control means" refers to a device that has the function of controlling the operation of multiple pieces of equipment in accordance with the user's operation commands.

[0587] A "central device" is the central device that receives operation information from the user, analyzes it, generates commands, and transmits commands.

[0588] A "generative artificial intelligence model" is a model that utilizes artificial intelligence technology to analyze user interaction data and infer intent.

[0589] This invention is a system that enables efficient management and coordination of multiple devices in a user's daily life through the cooperation of a user, a terminal, and a server. The user operates various applications using a terminal such as a smartphone or tablet. The terminal acquires this operation information and transmits it as data to the server.

[0590] The server analyzes the received data using an artificial intelligence model to infer the user's intent. In this process, the server uses the prompt "Please tell me what action should be taken when the user selects 'away mode'," and the model infers the appropriate command.

[0591] Based on the inference results, the server uses a command generation device to generate specific operation commands for multiple devices. These generated commands are then sent to the terminal. The terminal receives these commands, distributes them to each IoT device, and controls each device accordingly.

[0592] For example, if the user selects "away mode," the server generates a series of commands, such as turning off the lights, stopping the air conditioner, and activating the security system, and sends them to the terminal. The terminal then executes these commands, automatically adjusting each specified device to the desired state.

[0593] As a result, users can efficiently manage multiple devices with simple operations, improving the convenience and comfort of their daily lives.

[0594] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0595] Step 1:

[0596] The user selects a specific operating mode by operating an application on their smartphone or tablet. The input is the user's selected mode information, which the device then receives.

[0597] Step 2:

[0598] The terminal sends the acquired operation information as data to the server. The input is user operation information, and the output is accurate data sent to the server. This process is handled by a data communication module.

[0599] Step 3:

[0600] The server uses a generative AI model to analyze the received operation data. The input is operation data sent from the terminal, and the output is the result of inferring the user's intent. In this analysis, the inference is performed using the prompt message "Please tell me what action should be taken when the user selects away mode."

[0601] Step 4:

[0602] Based on the analysis results, the server uses a command generation device to generate specific operation commands for multiple pieces of equipment. The input is the result of inferring the user's intent, and the output is operation commands for multiple pieces of equipment. In this step, it is determined which piece of equipment each command instructs to perform what operation.

[0603] Step 5:

[0604] The server sends the generated commands to the terminal. The input is the operation commands generated by the server, and the output is the commands accurately delivered to the terminal. This communication takes place through a command transmission module.

[0605] Step 6:

[0606] The terminal controls each IoT device based on the commands it receives. The input is operation commands received from the server, and the output is each device starting to act according to those commands. Specific actions include turning off lights, shutting off air conditioners, and activating security systems.

[0607] Through the steps described above, users can automatically manage multiple pieces of equipment within their environment with simple operations.

[0608] (Application Example 1)

[0609] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0610] Many current smart home systems face the challenge of requiring separate applications for each individual device, making integrated operation difficult. Furthermore, manually operating each security device every time a user leaves the home is cumbersome and inconvenient. Therefore, there is a need for a system that allows users to easily control multiple security devices in an integrated manner, enabling them to leave home with peace of mind.

[0611] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0612] In this invention, the server includes information acquisition means for acquiring operation information from a user and transmitting said operation information as data; analysis means for analyzing said data using a generating artificial intelligence model and inferring the user's intent; and security control means for generating operation commands for a plurality of remotely located security devices based on the inference results and transmitting said operation commands. This makes it possible for a user to easily control a series of security devices through a smart device and manage security efficiently and centrally.

[0613] "Operation information" refers to data generated when a user operates a device, and it reflects the user's intentions.

[0614] "Information acquisition means" refers to a means of acquiring user operation information and transmitting that information as data within the system.

[0615] "Analysis means" refers to a method for analyzing user interaction information using a generating artificial intelligence model and inferring the user's intent.

[0616] The "command generation means" is a means for generating and transmitting operation commands to multiple devices based on the inference results of the analysis means.

[0617] "Control means" refers to means for controlling multiple devices in accordance with operation commands transmitted from command generation means.

[0618] "Security control means" refers to means for generating operation commands for multiple remotely located security devices based on the inference results of the analysis means, and for transmitting and controlling said commands.

[0619] A "generative artificial intelligence model" is an artificial intelligence model used to analyze user interaction information and infer their intentions.

[0620] The system for implementing this invention begins with the user providing operation information through a device such as a smartphone. When the user presses, for example, the "Go Out" button, that operation information is transmitted to the server through the information acquisition means. The server uses the acquired operation information to utilize a generation artificial intelligence model to infer the user's intent. Based on the inferred intent, the command generation means generates appropriate operation commands for multiple security devices and transmits those commands through the control means.

[0621] Specific hardware includes smartphones and IoT devices (e.g., smart locks, security cameras, alarm systems). Software-wise, a program using Python to send HTTP requests functions, for example, by using the requests library to send commands to IoT devices. Furthermore, a generative artificial intelligence model analyzes user intent and optimizes device operation according to the situation.

[0622] For example, when a user presses the "Security Activate" button on a smartphone app, all doors in the house automatically lock, security cameras begin recording, and alarms are activated. This feature allows users to leave their homes with peace of mind.

[0623] An example of a prompt to input into the generating AI model is, "I want to enhance the security of my home while I'm out, so please implement a system that automatically locks the doors, starts camera recording, and activates an alarm with the press of a button on my smartphone."

[0624] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0625] Step 1:

[0626] The user inputs operational information using a smartphone application. This input information includes mode selections such as "Going Out." This operational information is transmitted from the terminal to the server via an information acquisition mechanism. The input is the user's selection information, and the output is the data transmitted to the server.

[0627] Step 2:

[0628] The server inputs the received operation information into a generating artificial intelligence model to infer the user's intent. The input here is operation information sent from the terminal, and the AI ​​model performs data analysis based on this data. The output is the inferred user intent.

[0629] Step 3:

[0630] Based on the inference results, the server uses a command generation mechanism to generate specific operating commands for multiple security devices. The input is the inferred user intent, and data processing involves command translation within the system. The output is the operating commands for the security devices.

[0631] Step 4:

[0632] Upon receiving operation commands sent to the terminal, the control system sends signals to each IoT device. Specifically, these include actions such as locking a smart lock, starting recording on a security camera, and activating an alarm. The input is the operation command sent from the server, and the output is the state change of the IoT device.

[0633] Step 5:

[0634] Users can monitor the operating status of each device in real time through the application. The terminal receives feedback information from IoT devices and reports its status to the server. The input is the feedback information from each device, and the output is the reflection of the feedback data to the server.

[0635] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0636] This invention is an advanced information system that incorporates an emotion engine that recognizes user emotions and integrates various information acquisition devices, analysis devices, command generation devices, and control devices. Its purpose is to significantly improve the user experience by generating optimal operation commands based on user emotion data and efficiently controlling various devices.

[0637] When a user uses a device such as a smartphone, this system extracts the user's selected mode, voice, facial expressions, and biometric information. This data is sent to an emotion engine, where the user's emotions are analyzed. The server integrates the emotion data obtained from the emotion engine with the user's operation information and performs analysis using a generative artificial intelligence model. From the results of the analysis, the system infers the operation commands that the user desires or needs.

[0638] The command generation device generates operation commands that are best suited to the user's emotional state based on inference. These commands are then distributed to each IoT device via the terminal. As a result, the device state can be automatically set to the optimal state for the user's environment.

[0639] As a concrete example, suppose a user returns home and desires a "relaxation mode" to reduce stress. When the user inputs this into the device, the device uses an emotion engine to understand the user's current emotional state from their facial expressions and tone of voice. The server analyzes the emotional data and this selection, generates commands to change the lighting to softer light and play calming music, and then executes them. In this way, a comfortable and relaxing environment is automatically created for the user.

[0640] As described above, the system incorporating the emotion engine of the present invention has the capability to realize interactions that take into account the user's emotional state, and to provide a user experience that is both convenient and satisfying.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] Upon returning home, the user selects "Relax Mode" through a smartphone application. This selection records the user's preferred mode on the device.

[0644] Step 2:

[0645] The device transmits the user's voice, facial expressions, and biometric information to the emotion engine. This prepares the device to analyze the user's current emotional state.

[0646] Step 3:

[0647] The emotion engine analyzes the user's emotions from the received data and sends that information to the server as emotion data. This data indicates the user's stress level and need for relaxation.

[0648] Step 4:

[0649] The server acquires emotional data from the emotion engine and user mode selection information, and analyzes them using a generative artificial intelligence model. The server then infers the optimal environment settings for the user's state.

[0650] Step 5:

[0651] Based on the inference results, the server generates specific control commands, such as adjusting the lighting intensity or playing music. These commands take into account the user's mental state.

[0652] Step 6:

[0653] The server sends the generated operation commands to the terminal. The terminal receives these commands and transmits them to each IoT device.

[0654] Step 7:

[0655] Each IoT device operates according to commands from the terminal. For example, smart lighting might be set to soft light, or a music system might play relaxation music.

[0656] Step 8:

[0657] The user receives a notification via their device when the setup is complete. This lets the user know that the entire home environment has been automatically configured.

[0658] Through this series of steps, the system creates an environment that matches the user's emotional state, providing a comfortable living experience.

[0659] (Example 2)

[0660] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In today's world, enabling automatic and optimal device operation that responds to a user's emotional state is crucial for improving the user experience. However, existing systems often fail to accurately analyze emotional data and generate optimal commands based on that analysis, making it difficult to properly reflect the user's intentions.

[0662] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0663] In this invention, the server includes means for transmitting various biometric information and voice and facial expression data acquired from the user to an emotion engine; means for analyzing the user's emotional state using the emotion engine and inferring the emotional state using an artificial intelligence model; and means for generating optimal operation commands for multiple devices based on the user's emotional state and selected mode using the inference results. This enables automatic and optimal device control based on emotions, accurately reflecting the user's intentions.

[0664] A "user" is an entity that uses a system to select an operating mode and inputs their own emotions and state of mind.

[0665] A "terminal" is a device that receives input from the user, acquires voice, facial expressions, and biometric information, and is a communication device that transmits data to the emotion engine and receives and executes control commands.

[0666] An "emotion engine" is an analytical device with specialized functions to analyze a user's voice, facial expressions, biometric information, etc., in order to identify and infer the user's emotional state.

[0667] A "generative AI model" is an artificial intelligence system that recognizes patterns in emotional states by learning from large amounts of data, and predicts and infers user intentions.

[0668] An "operation command" is a set of instructions that specify the control actions to be performed on multiple devices based on the user's emotional state or selected mode.

[0669] A "server" is a computing device that receives data from the emotion engine and performs analysis using a generative AI model, acting as the central hub responsible for controlling and coordinating the entire system.

[0670] "Devices" are internet-connected devices controlled by a system, and are various types of equipment that create a specific environment based on user commands.

[0671] This invention is an advanced information system designed to enhance the user experience. Users can interact with this system using devices such as smartphones and tablets. The device collects the user's voice, facial expressions, and biometric information and transmits it to a server in the cloud.

[0672] The server analyzes the transmitted information using an emotion engine and identifies the user's emotional state using a generative artificial intelligence model. Then, based on the emotional state and the mode selected by the user, it generates the optimal operation command and distributes the command to each device via the terminal.

[0673] The terminal distributes commands received from the server to various internet-connected devices (e.g., smart home appliances and audio systems) to execute them. This allows for environment settings tailored to the user's emotions.

[0674] For example, if a user selects "Relax Mode," the device sends voice tone and facial expression data to the emotion engine to confirm that the user desires a relaxed state. The server analyzes the data using a generative AI model and instructs the device to adjust the room lighting to a warmer color and play relaxation music.

[0675] An example of a prompt statement is: "What environmental settings would be appropriate if the user wanted to relax? Assuming the user desires relaxation based on their tone of voice and facial expression, please suggest a suitable command." By inputting this prompt statement into the generating AI model, the system can infer and execute the action most appropriate to the user's emotional state.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] The user selects the desired mode of operation using a device such as a smartphone or tablet. During this process, voice, facial expressions, and biometric information (e.g., heart rate) are collected via the device's microphone and camera. The user's voice, video, and biometric data are captured by the device as input. As output, this data is prepared for transmission.

[0679] Step 2:

[0680] The device packages the collected voice, facial expressions, and biometric information and sends it to a server in the cloud. The input consists of various data stored within the device. This data is sent to the cloud according to a transmission protocol, and processing begins on the server side. The output is the transmission of data to the server.

[0681] Step 3:

[0682] The server inputs the received data into the emotion engine. Here, various data is analyzed, and a generative AI model is used to infer the user's emotional state. The input is packaged data sent from the terminal. The data is analyzed by the emotion engine, and emotional patterns are identified using a specific algorithm. The output is the analyzed emotional state information.

[0683] Step 4:

[0684] The server uses the emotional state obtained from the emotion engine, analyzes it with a generative AI model, and determines an appropriate action command for the user's state. The input is the inferred emotional state. At this stage, the generative AI model performs analysis based on the prompt text and generates a specific set of commands. The output is the optimized set of commands.

[0685] Step 5:

[0686] The terminal receives a set of commands from the server and distributes them to each IoT device. The input is operation commands from the server. As output, these commands are executed on various devices, and the user's environment is automatically adjusted.

[0687] Step 6:

[0688] The user's surrounding environment changes based on commands. This process might involve, for example, the lighting changing to a different color temperature, or the music being adjusted to a relaxation mode. The output is a physical environment optimized for the user, thereby improving the user experience.

[0689] (Application Example 2)

[0690] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] In physical stores, accurately understanding each customer's emotional state and providing appropriate product suggestions and environmental adjustments in real time was difficult. Traditional systems failed to fully understand customer intentions and emotions, resulting in uniform service provision. Therefore, there was a need to improve customer satisfaction and achieve flexible responses tailored to individual needs.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0693] In this invention, the server includes data collection means for acquiring operation information and biometric information from the user, aggregating and transmitting the information; data analysis means for analyzing the data using an artificial intelligence model that generates the data and inferring the user's emotional state; and command generation means for generating optimal operation commands for multiple functions based on the inference results and transmitting the operation commands. This makes it possible to make appropriate product suggestions and environmental adjustments in real time according to the customer's emotions.

[0694] "Data collection means" refers to a device or system that has the function of acquiring user operation information and biometric information, aggregating this information, and transmitting it to a server.

[0695] "Data analysis means" refers to a device or software equipped with the function of analyzing acquired data using an artificial intelligence model that generates data, and inferring the user's emotional state.

[0696] A "command generation means" is a device or system for generating optimal operation commands for multiple functions based on the results of data analysis and transmitting them to each device.

[0697] "Environmental control means" refers to a device or system that has the function of controlling multiple environmental elements, such as lighting and music, according to the user's operation commands.

[0698] A "generating artificial intelligence model" refers to artificial intelligence technology and its algorithms used to analyze acquired data and infer user emotions and intentions.

[0699] To realize this invention, it is necessary to construct an advanced information system that integrates various devices. The server acquires operation information and biometric information from the user through data collection means. This uses mobile terminals such as smart glasses and smartphones. The devices are equipped with cameras and microphones and collect biometric data such as facial expressions, voice, and heart rate in real time.

[0700] The collected data is processed on servers located in the cloud. These servers analyze the acquired data using artificial intelligence models to infer the user's emotional state. Examples of AI technologies used include Microsoft Azure Emotion API and Google Cloud Vision.

[0701] After the data analysis is complete, the server uses a command generation system to generate optimal operation commands corresponding to the inferred emotions. These operation commands are sent to IoT devices in the store, specifically to lights, speakers, and other devices. Smart home apps such as Samsung SmartThings are used to control these devices.

[0702] For example, when a customer enters a physical store, smart glasses can instantly determine their emotional state and adjust the store's lighting to a warmer color and play soothing music to help them relax. This system can provide an optimal environment tailored to the customer's emotions in real time.

[0703] Example prompt: "Based on the customer's facial expressions and vital data, evaluate the customer's emotional state and generate device commands to provide an optimal relaxation environment. Then, create a protocol to execute those commands."

[0704] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0705] Step 1:

[0706] The device acquires the user's biometric and operational information. It receives input such as facial expressions and voice data captured by cameras and microphones on smart glasses or smartphones, as well as heart rate measured by vital sensors. This data is temporarily stored and prepared for transmission to a server.

[0707] Step 2:

[0708] The server receives data transmitted from the terminal. The received input data is processed into an appropriate format and analyzed using a generative AI model. Here, the data analysis method infers the user's emotional state, and the inference result is generated as output.

[0709] Step 3:

[0710] The server generates optimal operation commands using a command generation mechanism based on the inference results. The input is analyzed emotion data, and the output is specific operation commands to adjust the environment. It generates prompt statements and prepares them to be sent as commands to various devices.

[0711] Step 4:

[0712] The server sends the generated commands to IoT devices in the store via a terminal. It uses the operation instructions created by the command generation system as input, and the output is used to operate lighting, sound systems, etc. Specifically, it issues commands to change the color temperature of the lighting or the playback content of the sound system.

[0713] Step 5:

[0714] After a user enters the store, the environment is adjusted in real time to provide an optimal environment tailored to the user. For example, if a user wants to relax, the lighting will be set to warm colors and relaxing music will be played. As a result, users can enjoy browsing products in a comfortable environment.

[0715] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0716] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0717] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0718] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0719] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0720] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0721] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0722] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0723] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0724] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0725] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0726] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0727] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0728] 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.

[0729] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0730] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0731] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0732] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0733] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0734] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0735] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0736] The following is further disclosed regarding the embodiments described above.

[0737] (Claim 1)

[0738] An information acquisition device that acquires operation information from the user and transmits said operation information as data,

[0739] An analysis device that analyzes the aforementioned data using an artificial intelligence model and infers the user's intent,

[0740] A command generation device that generates operation commands for multiple devices based on the aforementioned inference results and transmits said operation commands,

[0741] A control device that controls multiple devices in accordance with the aforementioned operation command,

[0742] A system that includes this.

[0743] (Claim 2)

[0744] The system according to claim 1, wherein the information acquisition device includes a method for transmitting user data from a terminal to a server.

[0745] (Claim 3)

[0746] The system according to claim 1, wherein the control device includes means for monitoring the operating status of multiple devices and feeding back the operating status to a server.

[0747] "Example 1"

[0748] (Claim 1)

[0749] A data transmission means that acquires operation information from the user and transmits said operation information as data,

[0750] A data analysis means that analyzes the aforementioned data using an artificial intelligence model to infer the user's intent,

[0751] A command generation means that generates operation commands for multiple pieces of equipment based on the inference results,

[0752] Command transmission means for transmitting the generated operation command to the target device,

[0753] Equipment control means for controlling multiple pieces of equipment in accordance with the aforementioned operation command,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, wherein the data transmission means has a function of transmitting user operation information to a central device via a terminal.

[0757] (Claim 3)

[0758] The system according to claim 1, wherein the equipment control means includes a mechanism for monitoring the operating status of multiple pieces of equipment and feeding back the operating status to a central device.

[0759] "Application Example 1"

[0760] (Claim 1)

[0761] An information acquisition means that acquires operation information from the user and transmits said operation information as data,

[0762] An analysis means that analyzes the aforementioned data using an artificial intelligence model to infer the user's intent,

[0763] Based on the aforementioned inference results, a command generation means generates operation commands for multiple devices and transmits the operation commands,

[0764] A control means for controlling multiple devices in accordance with the aforementioned operation command,

[0765] Based on the aforementioned inference results, a security control means generates operation commands for multiple remotely located security devices and transmits the operation commands,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, wherein the information acquisition means includes a method for transmitting user data from a terminal to a server.

[0769] (Claim 3)

[0770] The system according to claim 1, wherein the control means includes means for monitoring the operating status of a plurality of devices and for feeding back the operating status to a server.

[0771] "Example 2 of combining an emotion engine"

[0772] (Claim 1)

[0773] A means for transmitting diverse biometric information, voice, and facial expression data acquired from the user to an emotion engine,

[0774] The means for analyzing the user's emotional state using the aforementioned emotion engine and for inferring the aforementioned emotional state using a generating artificial intelligence model,

[0775] A means for generating optimal operation commands for multiple devices based on the user's emotional state and selection mode using inference results,

[0776] Means for distributing the aforementioned operation commands to each device via a terminal and controlling the operation of said device,

[0777] ...

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising a method for the terminal to transmit data based on emotional state and selection mode to a server and receive analysis results.

[0781] (Claim 3)

[0782] The system according to claim 1, further comprising means for monitoring the operating state of equipment based on inferred operation commands and for forming a system feedback loop using the operating state.

[0783] "Application example 2 when combining with an emotional engine"

[0784] (Claim 1)

[0785] A data collection means that acquires user operation information and biometric information, aggregates the information, and transmits it.

[0786] A data analysis means that analyzes the aforementioned data using an artificial intelligence model and infers the user's emotional state,

[0787] A command generation means generates optimal operation commands for multiple functions based on the aforementioned inference results and transmits the operation commands,

[0788] An environmental control means for controlling multiple environmental elements in accordance with the aforementioned operation command,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the data collection means includes a method for transmitting information corresponding to the user's situation from a terminal to an information management device.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the environmental control means includes means for monitoring the operating state of a plurality of environmental elements and for feeding back the operating state to an information management device. [Explanation of symbols]

[0794] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information acquisition device that acquires operation information from the user and transmits said operation information as data, An analysis device that analyzes the aforementioned data using an artificial intelligence model and infers the user's intent, A command generation device that generates operation commands for multiple devices based on the aforementioned inference results and transmits said operation commands, A control device that controls multiple devices in accordance with the aforementioned operation command, A system that includes this.

2. The system according to claim 1, wherein the information acquisition device includes a method for transmitting user data from a terminal to a server.

3. The system according to claim 1, wherein the control device includes means for monitoring the operating status of multiple devices and feeding back the operating status to a server.

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