system

Abstract

We provide the system. [Solution] A terminal means for acquiring user vital data and environmental data, A server means that analyzes the acquired data and evaluates the user's state, A server means that generates conversation content based on the evaluated user state and transmits it to the user, A terminal means that converts the generated conversation content into speech and outputs it to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional emotion recognition robots have limited conversation content for users due to the limitations of conversation generation technology. As a result, it has been difficult to relieve loneliness and provide appropriate health management. In addition, comprehensive user state evaluation using smart devices and IoT devices and real-time response based on the evaluation have been insufficient. There is a need to overcome such constraints and realize an AI robot that can closely respond to individual user living environments and health conditions.

Means for Solving the Problems

[0005] This invention includes means for acquiring the user's vital data and environmental data, and a server means for analyzing this data to evaluate the user's state. Based on the evaluated state, the server means generates conversation content optimized for the user, converts it into speech, and transmits it to the user. This system provides interaction tailored to the user's emotional and health state, and further enables the control of IoT devices based on environmental data to optimize the user's living environment. This aims to alleviate feelings of loneliness and improve the user's health management.

[0006] A "user" is someone who uses this system and provides vital data and feedback.

[0007] "Vital data" refers to information that indicates the user's physical health status, such as heart rate, body temperature, and sleep patterns.

[0008] "Environmental data" refers to physical environmental information such as the temperature, humidity, and lighting conditions of the user's living space.

[0009] A "terminal" is a device that acquires vital data and environmental data from the user and transmits them to a server.

[0010] A "server" is a computer system that analyzes data sent from a terminal, evaluates the user's state, and generates conversation content.

[0011] "IoT devices" are devices that can be controlled via the internet and are used to adjust the user's living environment.

[0012] "Conversation content" refers to the dialogue information generated by the server based on the user's state assessment.

[0013] A "generative AI model" is an algorithm that uses artificial intelligence to generate appropriate conversation content in real time based on the user's state.

[0014] "Voice conversion" refers to the process of outputting the generated conversation content in an audible form for the user to listen to.

[0015] "Feedback" means that the user provides their feelings and requests to the system, and this information is used for the system's learning.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered 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.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides an AI system that is tailored to the user's living environment and health condition. This system collects the user's vital data and environmental data, analyzes it on a server to evaluate the user's state, and generates natural and meaningful conversations based on the evaluation results. The aim is to maintain the user's physical and mental health and reduce feelings of loneliness.

[0038] Specifically, users wear wearable devices that collect vital data such as heart rate, body temperature, and sleep patterns through the terminal. Furthermore, environmental data such as room temperature, humidity, and lighting conditions are simultaneously acquired using IoT devices installed in the user's living space. The terminal transmits this information to a server using secure communication.

[0039] The server analyzes the received data in real time to understand the user's health status and emotional state. Based on this assessment, a generative AI model creates conversational content tailored to the user. For example, if signs of sleep deprivation or stress are detected, the server designs a conversation suggesting relaxation advice or light exercise. Furthermore, user feedback is sent to the server and used to train the AI ​​model.

[0040] The terminal uses a speech synthesis engine to output the generated conversation content as voice, interacting with the user. It also issues commands to IoT devices as needed to adjust the user's living environment. For example, if it determines that the user's sleep quality is poor, it will take measures such as changing the bedroom lighting to a warmer color or adjusting the room temperature.

[0041] Through this process, the system of the present invention effectively monitors the user's health status and provides support based on individual needs. This enables the user to enjoy a healthy and comfortable life.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device acquires heart rate, body temperature, and sleep data from wearable devices worn by the user via Bluetooth or Wi-Fi. Additionally, the device uses sensors to acquire environmental data such as room temperature, humidity, and lighting conditions from IoT devices installed in the user's living space.

[0045] Step 2:

[0046] The terminal temporarily stores the collected vital and environmental data and transmits the data to the server at regular intervals using a secure communication protocol (e.g., HTTPS).

[0047] Step 3:

[0048] The server analyzes the received user data to estimate their health and emotional state. This assessment includes comparing it with past user data. The results are stored in an internal database and used in the next step.

[0049] Step 4:

[0050] The server generates personalized conversation content using a generative AI model based on the user's evaluated state. The generation process takes into account the user's past conversation history and current state.

[0051] Step 5:

[0052] The terminal receives conversation content from the server, converts it into speech using a speech synthesis engine, and outputs the voice to the user. Furthermore, it sends commands to IoT devices as needed to adjust the room temperature, change the lighting, and so on.

[0053] Step 6:

[0054] Users can provide feedback through conversation. This feedback is sent to the server by the device.

[0055] Step 7:

[0056] The server collects user feedback and uses it as training data for the generated AI model. This allows the system to continuously evolve and improve the accuracy of future dialogue generation.

[0057] (Example 1)

[0058] 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."

[0059] A lack of systems that cater to users' living environments and health conditions results in a problem where maintaining physical and mental health and reducing feelings of loneliness are not effectively achieved. In particular, the difficulty in real-time analysis of collected data and the generation of appropriate feedback makes it difficult to provide appropriate responses that are tailored to the user's health condition and emotions.

[0060] 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.

[0061] In this invention, the server includes information device means for collecting the user's biometric data and surrounding environment data; processing means for analyzing the collected data and measuring the user's health status; and processing means for generating and providing dialogue content to the user based on the measured user health status. This enables the evaluation of health status based on individual data, the generation of appropriate dialogue, and even the automatic adjustment of the user's living environment.

[0062] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and sleep patterns.

[0063] "Surrounding environment data" refers to information about the environment in the space where the user lives, such as room temperature, humidity, and lighting conditions.

[0064] "Information device means" refers to electronic devices and terminals used for collecting and providing data.

[0065] "Processing device" refers to a device that performs computational processing for data analysis and generation of dialogue content.

[0066] "Health status" refers to an indicator that shows the user's mental and physical condition and emotional state.

[0067] "Dialogue content" refers to the content of conversations and advice exchanged between the user and the system.

[0068] A "generative intelligence model" refers to an artificial intelligence model that generates dialogue based on user input.

[0069] "Various devices" refers to home appliances and IoT devices that can adjust the user's living environment.

[0070] This invention relates to an AI system that effectively monitors a user's health status and provides support based on their individual needs. This system consists primarily of three elements.

[0071] First, the user wears a wearable device to collect biometric data such as heart rate, body temperature, and sleep data. This device is expected to utilize various commercially available health monitoring devices. In addition, IoT sensors are installed in the user's living space to collect ambient environmental data such as room temperature, humidity, and lighting.

[0072] Next, the device transmits this biometric data and surrounding environment data obtained from the user to the server using a secure communication protocol. One example of such a protocol is HTTPS. The device can function as a smartphone or a dedicated home device.

[0073] The server runs a data analysis program to analyze the received data in real time. This analysis can utilize libraries such as Python's pandas and NumPy. Based on the analysis results, the server uses a generative AI model to generate conversation content appropriate to the user's state. An example of such a model is GPT-3®. An example of a prompt statement is: "If the user is tired, how should relaxation be suggested?"

[0074] For example, if the user's data shows signs of stress, the server will generate and provide the user with conversational advice on how to relax. For instance, it might suggest, "Why don't you try taking some deep breaths?"

[0075] In this way, users can enjoy a healthy and comfortable life. The coordinated operation of each step in the system makes it possible to improve the user's health and quality of life.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user collects biometric data using a wearable device. Specifically, it automatically acquires data on heart rate, body temperature, and sleep duration during daily activities. The input to this process is raw sensor data from the user's body, and the output is statistical data indicating the user's health status.

[0079] Step 2:

[0080] The device collects ambient environmental data from IoT sensors installed in the user's living space. Specifically, it acquires information on room temperature, humidity, and lighting at regular intervals. The input data is environmental information from the sensors, and the output is organized ambient environmental data.

[0081] Step 3:

[0082] The device transmits collected biometric data and ambient environmental data to the server using a secure protocol (e.g., HTTPS). During this process, the data is encrypted before transmission. The input is the collected data, and the output is the data received by the server and prepared for analysis.

[0083] Step 4:

[0084] The server analyzes the received data and performs analyses to understand the user's health and emotional state. It utilizes Python libraries to process the data, perform trend analysis, and detect anomalies. The input is data from the terminal, and the output is the result of the health assessment.

[0085] Step 5:

[0086] The server uses a generative AI model to generate prompts based on the analysis results and construct the content of the conversation with the user. Specifically, if there are signs of stress, it will generate a prompt that "suggests ways to relax." The input is the result of the health assessment, and the output is conversation content appropriate for the user.

[0087] Step 6:

[0088] The server collects user feedback and uses it as training data for an AI model to help generate future dialogues. The input is the user's reactions and satisfaction level, and the output is the updated training data.

[0089] Step 7:

[0090] The terminal uses speech synthesis technology to output the generated dialogue content to the user as voice, and controls IoT devices as needed to adjust the user's living environment. Specifically, it can change the color of the lighting or adjust the heating settings. The input consists of the generated dialogue content and control commands, while the output consists of voice messages to the user and changes in the status of IoT devices.

[0091] (Application Example 1)

[0092] 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."

[0093] In modern society, for individual users to lead healthy and comfortable lives, it is crucial to appropriately monitor their health status and living environment and provide information tailored to their individual needs. However, conventional technologies have struggled with real-time analysis of users' biometric and environmental data, and the provision of meaningful information based on these analysis results. This has resulted in insufficient personalized product information and service suggestions, particularly in in-store shopping experiences. Therefore, there is a need to develop systems that appropriately provide users with the information they need on the spot, thereby improving their living environment and shopping experience.

[0094] 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.

[0095] In this invention, the server includes a device means for acquiring the user's biometric and environmental data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating conversation content based on the evaluated user state and communicating it to the user. This makes it possible to appropriately provide the information the user requests and to suggest product information and services that are appropriate to their health condition.

[0096] "Biometric data" refers to various types of information that indicate the user's physical condition, such as heart rate, body temperature, and sleep patterns.

[0097] "Environmental data" refers to information about the environment in which the user is located, such as room temperature, humidity, and illuminance around the user.

[0098] "Device means" refers to hardware devices for acquiring and managing biometric data and environmental data.

[0099] "Information processing means" refers to the process of analyzing acquired data and evaluating the user's state using analysis software and algorithms executed on a server.

[0100] An "information display device" is a digital device used by users to visually perceive information.

[0101] "Product information" refers to data regarding the details, features, and pricing of products sold in physical stores.

[0102] "Service" refers to sales, assistance, experiences, and other services provided to customers, and encompasses various offerings aimed at improving user satisfaction.

[0103] "Control equipment" refers to devices that change the physical state of a living space, such as air conditioning and lighting systems, which are operated based on environmental data.

[0104] The system for carrying out this invention consists of an information display device worn by the user, a device means, an information processing means, and a control device. The server collects the user's biometric data and environmental data through the device means and analyzes this data in the information processing means. This analysis uses data processing and machine learning algorithms built in programming languages ​​such as Python and R. Based on the obtained analysis results, the server utilizes a generative AI model to generate conversation content that is appropriate to the user's situation. For example, OpenAI's GPT-3 is used as a generative AI model.

[0105] The generated conversation is converted into speech data using speech synthesis software (e.g., Amazon Polly) and transmitted to the user via their information display device. User feedback is sent to the server as separate data and used to train the generating AI model. Furthermore, the server operates control devices based on environmental data to adjust the user's living environment. This operation, for example, involves changing the settings of air conditioners and lighting fixtures to provide the user with an optimal living environment.

[0106] As a concrete example, if a user wearing smart glasses in a physical store feels the need to relax, the server will detect that the user's heart rate is higher than normal and will announce via voice, "We'd like to recommend the perfect product for your relaxation time. You can try our aromatherapy candles here." In this case, a possible prompt to confirm the recommended product could be text in the format of, "This is the relaxation item we recommend for you. Would you like to try it?"

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The device collects the user's biometric data (heart rate, body temperature, sleep patterns, etc.) and environmental data (room temperature, humidity, illuminance, etc.) via the device's means. This data is input in real time from sensors. The device formats this data and transmits it to a server via a secure network.

[0110] Step 2:

[0111] The server analyzes the received biometric and environmental data. Here, data cleansing and filtering are performed to remove outliers and noise. Next, statistical methods and machine learning algorithms are applied using Python to evaluate the user's health and emotional state. The output generates an evaluation of the user's state.

[0112] Step 3:

[0113] Based on the analysis results, the server generates conversation content suitable for the user using a generative AI model. Here, the generative AI model utilizes a natural language generation model such as GPT-3, using pre-configured prompt sentences. The evaluation results are combined with the prompts as input, and conversation content is generated as output.

[0114] Step 4:

[0115] The server inputs the generated conversation into speech synthesis software and converts it into audio data. For example, Amazon Polly is used for speech synthesis. The audio data is temporarily sent to the terminal and output as audio through the user's information display device.

[0116] Step 5:

[0117] User feedback is collected on the device and sent to the server. The server receives this feedback and incorporates it as training data for the generative AI model, thereby improving the accuracy of conversation generation in subsequent sessions. The feedback data is used to fine-tune the generative AI model.

[0118] Step 6:

[0119] The server re-evaluates environmental data as needed and operates control devices. For example, it sends instructions to change settings for air conditioning and lighting, making adjustments so that users can enjoy a more comfortable environment. This allows the system to respond to the user's living environment in real time.

[0120] 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.

[0121] This invention provides an AI system that combines an emotion engine that recognizes user emotions. The system aims to provide the user with the most optimal interaction based on an integrated analysis of the user's vital data, environmental data, and emotional state.

[0122] Specifically, users wear wearable devices that collect heart rate, body temperature, and sleep data. This information is collected through the device, and environmental data such as room temperature, humidity, and lighting is also obtained from IoT devices placed in the user's living space. This data is securely transmitted from the device to the server.

[0123] Furthermore, an emotion engine is incorporated that analyzes the user's face and voice, which is used when the user interacts with the system through the display and microphone. The emotion engine analyzes the user's facial expressions and voice tone in real time to estimate their emotional state. This information is comprehensively analyzed on the server along with vital data and environmental data, and reflected in the evaluation of the user's state.

[0124] Based on the analysis results, the server uses a generative AI model to generate conversation content tailored to the user. This conversation includes responses and advice that are appropriate to the user's emotions, based on data obtained from the emotion engine. For example, if the user is feeling stressed, the server will suggest information and relaxation methods that can help alleviate that stress.

[0125] The terminal transmits the conversation content to the user through speech synthesis. It also controls IoT devices based on commands from the server to improve the user's living environment. For example, it might adjust lighting or play music to enhance relaxation. User feedback is collected on the server as further learning data, contributing to system improvement.

[0126] Thus, the system of the present invention can utilize diverse user data to provide more human-like interactions tailored to individual needs. As a result, users can live with a sense of security and comfort.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The device collects heart rate, body temperature, and sleep data via the wearable device worn by the user and stores this data locally. This data is acquired using Bluetooth or Wi-Fi.

[0130] Step 2:

[0131] The terminal uses sensors to measure environmental data such as room temperature, humidity, and lighting through IoT devices installed in the room, and stores this data locally. This data is also collected by the terminal using various communication protocols.

[0132] Step 3:

[0133] A device equipped with an emotion engine captures the user's facial expressions and voice tone using a camera and microphone, and analyzes their emotions in real time. The analysis results are added to the data as the user's emotional state.

[0134] Step 4:

[0135] The device transmits collected vital data, environmental data, and emotional data to the server using a secure communication protocol, ensuring the safe transfer of data.

[0136] Step 5:

[0137] The server comprehensively analyzes the received data to evaluate the user's health and emotional state. Based on this evaluation, a generative AI model develops conversation content optimized for the user.

[0138] Step 6:

[0139] The server sends the generated conversation content to the terminal, which then outputs it to the user as a conversation via a speech synthesis engine. It also sends important commands to IoT devices, such as adjusting the environment by changing the lighting to a relaxing color or playing soothing music.

[0140] Step 7:

[0141] Users provide feedback and comments to their devices through conversation. This feedback is sent from the device to the server and used as training data for the next AI model. The system evolves based on this information, enabling it to provide services that are even more tailored to the user.

[0142] (Example 2)

[0143] 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 as the "terminal".

[0144] In modern society, there is a demand for flexible dialogue interfaces that respond to each user's emotional state and surrounding environment. However, existing systems struggle to analyze a user's biometric and environmental information in real time and provide dialogue tailored to individual needs based on that analysis. Furthermore, current systems lack sufficient mechanisms to continuously reflect user responses in the system to improve the quality of dialogue. To solve these problems, a system is needed that can accurately estimate emotional states, dynamically generate responses, and further optimize the user's environment.

[0145] 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.

[0146] In this invention, the server includes an information terminal means for collecting the user's biometric information and surrounding environment information, an analysis means for estimating the user's emotional state based on the collected information, and a control means for inputting prompts to a generation AI model using the emotional state obtained by the analysis means and generating conversation content. This makes it possible to provide flexible and sophisticated dialogue in real time according to the user's emotional state, and further improve system performance by learning the user's responses.

[0147] "Information terminal means" refers to devices and systems that collect biometric information and surrounding environmental information from users.

[0148] "Analysis means" refers to a device that includes mechanisms and algorithms for estimating the user's emotional state based on collected biological and environmental information.

[0149] "Control means" refers to devices or processes for inputting prompts into a generative AI model and generating conversation content based on the analyzed emotional state.

[0150] "Voice output means" refers to devices or technologies that convert generated conversation content into voice signals and transmit information to the user via voice.

[0151] "Update methods" refer to functions and processes that detect user responses and reflect them in the training dataset of the generated AI model.

[0152] "Management means" refers to devices and systems that manage electronic devices based on information about the surrounding environment and optimize the user's living environment.

[0153] This system aims to accurately recognize the user's emotional state and provide dialogue based on that understanding. The following describes the configuration for implementing this system.

[0154] The terminal collects data through wearable devices worn by the user and various environmental sensors placed in the living space. Specifically, it acquires biometric information such as heart rate, body temperature, and sleep patterns from wearable devices, and environmental information such as room temperature, humidity, and lighting intensity from environmental sensors. This data is aggregated in the terminal via Bluetooth or Wi-Fi.

[0155] The device transmits the collected information to the server using a secure protocol, and the server uses analysis tools to estimate the user's emotional state based on biometric and environmental information. This emotion estimation is supplemented by an emotion engine that analyzes the user's facial expressions and tone of voice in real time.

[0156] The server uses a generative AI model to generate appropriate conversation content based on the estimated emotional state. This generative AI model constructs a natural dialogue with the user based on predefined prompts. For example, it customizes the conversation using prompts such as, "What advice should be offered if the user is feeling stressed?"

[0157] The generated conversation content is transmitted to the user via the device's voice output system. Using a speech synthesis engine, natural-sounding speech is generated to provide the user with a more human-like experience. This output allows the user to receive emotionally responsive interactions.

[0158] Furthermore, user feedback is sent back to the server via the device, and this feedback is used to train the generated AI model, leading to continuous performance improvements. In addition, the server controls electronic devices based on environmental information using management tools to optimize the user's living environment. For example, if a user desires relaxation, the lighting settings are lowered and relaxing music is played to adjust the environment.

[0159] In this way, the system can provide flexible and sophisticated interactions tailored to each individual user, making it possible to create a comfortable living environment.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The terminal acquires biometric information from the wearable device worn by the user. Here, it communicates with the device via Bluetooth and collects data such as heart rate, body temperature, and sleep patterns. The input is raw biometric data, which is then organized as time-series data and stored in a database.

[0163] Step 2:

[0164] The device collects ambient environmental information from environmental sensors placed in the living space. Specifically, it communicates with the sensors using Wi-Fi to acquire data such as room temperature, humidity, and lighting intensity. This input data is organized with a timestamp along with the ID of each sensor and sent to the server at regular intervals.

[0165] Step 3:

[0166] The server receives biometric and environmental information transmitted from terminals via a secure protocol. This data is aggregated and preprocessed. Missing data is imputed, outliers are detected and removed, and noise is filtered to construct a dataset ready for analysis.

[0167] Step 4:

[0168] The server performs analysis using pre-processed data. Here, a machine learning algorithm is used to estimate the user's emotional state. Biometric information, environmental information, and historical emotional data are referenced as input, and the current emotional score is output in real time. The emotional score serves as the basis for the system's next action.

[0169] Step 5:

[0170] The server generates conversation content using a generative AI model based on the analysis results. Specifically, it inputs prompt sentences corresponding to the emotion score into the generative AI model and obtains a response in text format. An example of such a prompt sentence would be, "What advice should be given if the user is feeling stressed?"

[0171] Step 6:

[0172] The device processes the conversation received from the server using a speech synthesis engine and converts it into an audio signal. The generated audio is then transmitted to the user through the speaker. The audio output is adjusted to match the user's emotional state, providing a natural conversational experience.

[0173] Step 7:

[0174] Users input their responses to voice messages via their devices. This feedback should include changes in emotion and specific messages. This feedback data is sent to a server and stored as training data for the generative AI model.

[0175] Step 8:

[0176] The server updates the generated AI model based on the feedback received, aiming to improve the overall system performance. Furthermore, it optimizes the living environment by controlling IoT devices via environmental sensors and managing lighting and music in response to the user's emotional state and feedback.

[0177] (Application Example 2)

[0178] 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".

[0179] In an environment where immediate responses based on user emotions and circumstances are required, traditional methods make it difficult to provide rapid and appropriate services. Furthermore, effectively utilizing feedback necessary to improve the customer experience remains a challenge. Additionally, there is a lack of means to optimize users' living environments according to their specific circumstances.

[0180] 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.

[0181] In this invention, the server includes a user interface means for acquiring the user's physiological data and ambient data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating interaction content and communicating it to the user. This enables a quick and appropriate response in accordance with the user's emotional state.

[0182] "User physiological data" refers to data about the user's physical condition, including heart rate, body temperature, and sleep information.

[0183] "Ambient data" refers to information such as temperature, humidity, and lighting conditions in the user's living space and surrounding environment.

[0184] "User interface means" refers to devices and systems used to acquire and transmit information with the user.

[0185] "Information processing means" refers to a system that analyzes acquired physiological data and ambient data to evaluate and judge the user's state.

[0186] "Interaction content" refers to the content of conversations and responses generated based on the user's state assessment.

[0187] The system designed to realize this application has the capability to provide immediate responses based on the customer's emotional state and integrates smart devices with cloud services. The server acquires the user's physiological data and ambient data, and analyzes this information in the cloud. This analysis utilizes software such as Google Cloud's Vision AI and Speech-to-Text API. As a result, the user's emotional state is identified in real time.

[0188] Based on the analysis results, the server generates appropriate interaction content using a generative AI model. This content is provided to the user through the voice output or display of a smart device. Furthermore, devices such as smart glasses, used as a user interface, contribute to this process by capturing customer facial expressions and voice data.

[0189] As a concrete example, when store staff use smart glasses, the glasses' camera and microphone collect the customer's facial expressions and voice, and send them to a server. If the server's analysis determines that the customer is stressed or fatigued, the staff will be given a suggested customer service approach, such as, "You seem tired. Can I help you?"

[0190] A concrete example of an input prompt for a generative AI model would be: "Based on emotional data obtained from the customer's face and voice, identify signs of relaxation or stress and devise specific countermeasures."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The device uses the camera and microphone of smart glasses to capture the user's facial expressions and voice in real time. This input data includes image data (facial expressions) and audio data (voice tone).

[0194] Step 2:

[0195] The device preprocesses the captured data and sends it to Google Cloud's Vision AI and Speech-to-Text API. This process converts facial expression data into an analyzable format and audio data into text. The output consists of the analyzed facial expression data and the transcribed text data, respectively.

[0196] Step 3:

[0197] The server uses the received, analyzed data to perform an analysis using its emotion engine. The generative AI model takes this data as input, processes it to identify the user's emotional state, and outputs an emotion evaluation. For example, an evaluation result such as "The customer is feeling stressed" might be output.

[0198] Step 4:

[0199] The server generates conversation content based on sentiment evaluation and prompts. This generation process constructs user-appropriate interaction content based on the evaluation results and outputs it as response data in voice or text format.

[0200] Step 5:

[0201] The terminal receives the generated response data and transmits it to the user using speech synthesis. The user receives the generated conversation content through their eyes and ears and can confirm it visually or aurally.

[0202] Step 6:

[0203] The device then retrieves user feedback and nonverbal responses and sends them back to the server. The server uses this feedback to further train the generative AI model and improve the entire system.

[0204] 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.

[0205] 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 those described above. 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 shown 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention provides an AI system that is tailored to the user's living environment and health condition. This system collects the user's vital data and environmental data, analyzes it on a server to evaluate the user's state, and generates natural and meaningful conversations based on the evaluation results. The aim is to maintain the user's physical and mental health and reduce feelings of loneliness.

[0221] Specifically, users wear wearable devices that collect vital data such as heart rate, body temperature, and sleep patterns through the terminal. Furthermore, environmental data such as room temperature, humidity, and lighting conditions are simultaneously acquired using IoT devices installed in the user's living space. The terminal transmits this information to a server using secure communication.

[0222] The server analyzes the received data in real time to understand the user's health status and emotional state. Based on this assessment, a generative AI model creates conversational content tailored to the user. For example, if signs of sleep deprivation or stress are detected, the server designs a conversation suggesting relaxation advice or light exercise. Furthermore, user feedback is sent to the server and used to train the AI ​​model.

[0223] The terminal uses a speech synthesis engine to output the generated conversation content as voice, interacting with the user. It also issues commands to IoT devices as needed to adjust the user's living environment. For example, if it determines that the user's sleep quality is poor, it will take measures such as changing the bedroom lighting to a warmer color or adjusting the room temperature.

[0224] Through this process, the system of the present invention effectively monitors the user's health status and provides support based on individual needs. This enables the user to enjoy a healthy and comfortable life.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The device acquires heart rate, body temperature, and sleep data from wearable devices worn by the user via Bluetooth or Wi-Fi. Additionally, the device uses sensors to acquire environmental data such as room temperature, humidity, and lighting conditions from IoT devices installed in the user's living space.

[0228] Step 2:

[0229] The terminal temporarily stores the collected vital and environmental data and transmits the data to the server at regular intervals using a secure communication protocol (e.g., HTTPS).

[0230] Step 3:

[0231] The server analyzes the received user data to estimate their health and emotional state. This assessment includes comparing it with past user data. The results are stored in an internal database and used in the next step.

[0232] Step 4:

[0233] The server generates personalized conversation content using a generative AI model based on the user's evaluated state. The generation process takes into account the user's past conversation history and current state.

[0234] Step 5:

[0235] The terminal receives conversation content from the server, converts it into speech using a speech synthesis engine, and outputs the voice to the user. Furthermore, it sends commands to IoT devices as needed to adjust the room temperature, change the lighting, and so on.

[0236] Step 6:

[0237] Users can provide feedback through conversation. This feedback is sent to the server by the device.

[0238] Step 7:

[0239] The server collects user feedback and uses it as training data for the generated AI model. This allows the system to continuously evolve and improve the accuracy of future dialogue generation.

[0240] (Example 1)

[0241] 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."

[0242] A lack of systems that cater to users' living environments and health conditions results in a problem where maintaining physical and mental health and reducing feelings of loneliness are not effectively achieved. In particular, the difficulty in real-time analysis of collected data and the generation of appropriate feedback makes it difficult to provide appropriate responses that are tailored to the user's health condition and emotions.

[0243] 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.

[0244] In this invention, the server includes information device means for collecting the user's biometric data and surrounding environment data; processing means for analyzing the collected data and measuring the user's health status; and processing means for generating and providing dialogue content to the user based on the measured user health status. This enables the evaluation of health status based on individual data, the generation of appropriate dialogue, and even the automatic adjustment of the user's living environment.

[0245] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and sleep patterns.

[0246] "Surrounding environment data" refers to information about the environment in the space where the user lives, such as room temperature, humidity, and lighting conditions.

[0247] "Information device means" refers to electronic devices and terminals used for collecting and providing data.

[0248] "Processing device" refers to a device that performs computational processing for data analysis and generation of dialogue content.

[0249] "Health status" refers to an indicator that shows the user's mental and physical condition and emotional state.

[0250] "Dialogue content" refers to the content of conversations and advice exchanged between the user and the system.

[0251] A "generative intelligence model" refers to an artificial intelligence model that generates dialogue based on user input.

[0252] "Various devices" refers to home appliances and IoT devices that can adjust the user's living environment.

[0253] This invention relates to an AI system that effectively monitors a user's health status and provides support based on their individual needs. This system consists primarily of three elements.

[0254] First, the user wears a wearable device to collect biometric data such as heart rate, body temperature, and sleep data. This device is expected to utilize various commercially available health monitoring devices. In addition, IoT sensors are installed in the user's living space to collect ambient environmental data such as room temperature, humidity, and lighting.

[0255] Next, the device transmits this biometric data and surrounding environment data obtained from the user to the server using a secure communication protocol. One example of such a protocol is HTTPS. The device can function as a smartphone or a dedicated home device.

[0256] The server runs a data analysis program to analyze the received data in real time. This analysis can utilize libraries such as Python's pandas and NumPy. Based on the analysis results, the server uses a generative AI model to generate conversation content appropriate to the user's state. One example of such a model is GPT-3. An example of a prompt might be: "If the user is tired, how should we suggest relaxation?"

[0257] For example, if the user's data shows signs of stress, the server will generate and provide the user with conversational advice on how to relax. For instance, it might suggest, "Why don't you try taking some deep breaths?"

[0258] In this way, users can enjoy a healthy and comfortable life. The coordinated operation of each step in the system makes it possible to improve the user's health and quality of life.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The user collects biometric data using a wearable device. Specifically, it automatically acquires data on heart rate, body temperature, and sleep duration during daily activities. The input to this process is raw sensor data from the user's body, and the output is statistical data indicating the user's health status.

[0262] Step 2:

[0263] The device collects ambient environmental data from IoT sensors installed in the user's living space. Specifically, it acquires information on room temperature, humidity, and lighting at regular intervals. The input data is environmental information from the sensors, and the output is organized ambient environmental data.

[0264] Step 3:

[0265] The device transmits collected biometric data and ambient environmental data to the server using a secure protocol (e.g., HTTPS). During this process, the data is encrypted before transmission. The input is the collected data, and the output is the data received by the server and prepared for analysis.

[0266] Step 4:

[0267] The server analyzes the received data and performs analyses to understand the user's health and emotional state. It utilizes Python libraries to process the data, perform trend analysis, and detect anomalies. The input is data from the terminal, and the output is the result of the health assessment.

[0268] Step 5:

[0269] The server uses a generative AI model to generate prompts based on the analysis results and construct the content of the conversation with the user. Specifically, if there are signs of stress, it will generate a prompt that "suggests ways to relax." The input is the result of the health assessment, and the output is conversation content appropriate for the user.

[0270] Step 6:

[0271] The server collects user feedback and uses it as training data for an AI model to help generate future dialogues. The input is the user's reactions and satisfaction level, and the output is the updated training data.

[0272] Step 7:

[0273] The terminal uses speech synthesis technology to output the generated dialogue content to the user as voice, and controls IoT devices as needed to adjust the user's living environment. Specifically, it can change the color of the lighting or adjust the heating settings. The input consists of the generated dialogue content and control commands, while the output consists of voice messages to the user and changes in the status of IoT devices.

[0274] (Application Example 1)

[0275] 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."

[0276] In modern society, for individual users to lead healthy and comfortable lives, it is crucial to appropriately monitor their health status and living environment and provide information tailored to their individual needs. However, conventional technologies have struggled with real-time analysis of users' biometric and environmental data, and the provision of meaningful information based on these analysis results. This has resulted in insufficient personalized product information and service suggestions, particularly in in-store shopping experiences. Therefore, there is a need to develop systems that appropriately provide users with the information they need on the spot, thereby improving their living environment and shopping experience.

[0277] 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.

[0278] In this invention, the server includes a device means for acquiring the user's biometric and environmental data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating conversation content based on the evaluated user state and communicating it to the user. This makes it possible to appropriately provide the information the user requests and to suggest product information and services that are appropriate to their health condition.

[0279] "Biometric data" refers to various types of information that indicate the user's physical condition, such as heart rate, body temperature, and sleep patterns.

[0280] "Environmental data" refers to information about the environment in which the user is located, such as room temperature, humidity, and illuminance around the user.

[0281] "Device means" refers to hardware devices for acquiring and managing biometric data and environmental data.

[0282] "Information processing means" refers to the process of analyzing acquired data and evaluating the user's state using analysis software and algorithms executed on a server.

[0283] An "information presentation device" is a digital device used by a user to visually perceive information.

[0284] "Product information" refers to data related to the details, features, price settings, etc. of products sold in physical stores.

[0285] "Service" means sales, assistance, experiences, etc. provided to customers, and is various provisions for improving user satisfaction.

[0286] A "control device" is a device for changing the physical state of a living space, such as an air conditioner or lighting device, which is operated based on environmental data.

[0287] The system for implementing this invention is composed of an information presentation device worn by the user, device means, information processing means, and a control device. The server collects the user's biological data and environmental data through the device means, and analyzes these data in the information processing means. For this analysis, data processing and machine learning algorithms built with programming languages such as Python and R are used. The server utilizes a generation AI model to generate conversation content suitable for the user's situation based on the obtained analysis results. As the generation AI model, for example, OpenAI's GPT-3 is used.

[0288] The generated conversation content is converted into audio data using speech synthesis software (such as Amazon Polly, etc.), and is transmitted to the user via the user's information presentation device. The user's feedback is sent to the server as separate data and is used for the learning of the generation AI model. Furthermore, the server operates the control device based on the environmental data to adjust the user's living environment. This operation provides the user with an optimal living environment by, for example, changing the settings of the air conditioner and lighting equipment.

[0289] As a concrete example, if a user wearing smart glasses in a physical store feels the need to relax, the server will detect that the user's heart rate is higher than normal and will announce via voice, "We'd like to recommend the perfect product for your relaxation time. You can try our aromatherapy candles here." In this case, a possible prompt to confirm the recommended product could be text in the format of, "This is the relaxation item we recommend for you. Would you like to try it?"

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The device collects the user's biometric data (heart rate, body temperature, sleep patterns, etc.) and environmental data (room temperature, humidity, illuminance, etc.) via the device's means. This data is input in real time from sensors. The device formats this data and transmits it to a server via a secure network.

[0293] Step 2:

[0294] The server analyzes the received biometric and environmental data. Here, data cleansing and filtering are performed to remove outliers and noise. Next, statistical methods and machine learning algorithms are applied using Python to evaluate the user's health and emotional state. The output generates an evaluation of the user's state.

[0295] Step 3:

[0296] Based on the analysis results, the server generates conversation content suitable for the user using a generative AI model. Here, the generative AI model utilizes a natural language generation model such as GPT-3, using pre-configured prompt sentences. The evaluation results are combined with the prompts as input, and conversation content is generated as output.

[0297] Step 4:

[0298] The server inputs the generated conversation into speech synthesis software and converts it into audio data. For example, Amazon Polly is used for speech synthesis. The audio data is temporarily sent to the terminal and output as audio through the user's information display device.

[0299] Step 5:

[0300] User feedback is collected on the device and sent to the server. The server receives this feedback and incorporates it as training data for the generative AI model, thereby improving the accuracy of conversation generation in subsequent sessions. The feedback data is used to fine-tune the generative AI model.

[0301] Step 6:

[0302] The server re-evaluates environmental data as needed and operates control devices. For example, it sends instructions to change settings for air conditioning and lighting, making adjustments so that users can enjoy a more comfortable environment. This allows the system to respond to the user's living environment in real time.

[0303] 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.

[0304] This invention provides an AI system that combines an emotion engine that recognizes user emotions. The system aims to provide the user with the most optimal interaction based on an integrated analysis of the user's vital data, environmental data, and emotional state.

[0305] Specifically, users wear wearable devices that collect heart rate, body temperature, and sleep data. This information is collected through the device, and environmental data such as room temperature, humidity, and lighting is also obtained from IoT devices placed in the user's living space. This data is securely transmitted from the device to the server.

[0306] Furthermore, an emotion engine that analyzes the user's face and voice is incorporated and utilized when the user interacts with the system through the display and microphone. The emotion engine analyzes the user's facial expressions and voice tones in real time to estimate the emotional state. This information is comprehensively analyzed by the server together with vital data and environmental data and reflected in the evaluation of the user's state.

[0307] Based on the analysis results, the server uses the generative AI model to generate conversation content customized for the user. This conversation includes responses and advice tailored to the user's emotions based on the data obtained from the emotion engine. For example, when the user is feeling stressed, the server proposes information and relaxation methods that are helpful for relaxation.

[0308] The terminal transmits this conversation content to the user through speech synthesis. Also, in order to improve the user's living environment, it controls IoT devices based on commands from the server. For example, it dims the lights and plays music to enhance the relaxation effect. The user's feedback is aggregated to the server as additional learning data, contributing to the improvement of the system.

[0309] In this way, the system of the present invention can utilize various data of the user and provide interactions tailored to individual needs in a more human-like manner. As a result, the user can live while feeling a sense of security and comfort.

[0310] The processing flow will be described below.

[0311] Step 1:

[0312] The terminal collects the heart rate, body temperature, and sleep data via the wearable device worn by the user and stores this locally. At this time, data is acquired using Bluetooth or Wi-Fi.

[0313] Step 2:

[0314] The terminal uses sensors to measure environmental data such as room temperature, humidity, and lighting through IoT devices installed in the room, and stores this data locally. This data is also collected by the terminal using various communication protocols.

[0315] Step 3:

[0316] A device equipped with an emotion engine captures the user's facial expressions and voice tone using a camera and microphone, and analyzes their emotions in real time. The analysis results are added to the data as the user's emotional state.

[0317] Step 4:

[0318] The device transmits collected vital data, environmental data, and emotional data to the server using a secure communication protocol, ensuring the safe transfer of data.

[0319] Step 5:

[0320] The server comprehensively analyzes the received data to evaluate the user's health and emotional state. Based on this evaluation, a generative AI model develops conversation content optimized for the user.

[0321] Step 6:

[0322] The server sends the generated conversation content to the terminal, which then outputs it to the user as a conversation via a speech synthesis engine. It also sends important commands to IoT devices, such as adjusting the environment by changing the lighting to a relaxing color or playing soothing music.

[0323] Step 7:

[0324] Users provide feedback and comments to their devices through conversation. This feedback is sent from the device to the server and used as training data for the next AI model. The system evolves based on this information, enabling it to provide services that are even more tailored to the user.

[0325] (Example 2)

[0326] 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 glasses 214 will be referred to as the "terminal".

[0327] In modern society, there is a demand for flexible dialogue interfaces that respond to each user's emotional state and surrounding environment. However, existing systems struggle to analyze a user's biometric and environmental information in real time and provide dialogue tailored to individual needs based on that analysis. Furthermore, current systems lack sufficient mechanisms to continuously reflect user responses in the system to improve the quality of dialogue. To solve these problems, a system is needed that can accurately estimate emotional states, dynamically generate responses, and further optimize the user's environment.

[0328] 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.

[0329] In this invention, the server includes an information terminal means for collecting the user's biometric information and surrounding environment information, an analysis means for estimating the user's emotional state based on the collected information, and a control means for inputting prompts to a generation AI model using the emotional state obtained by the analysis means and generating conversation content. This makes it possible to provide flexible and sophisticated dialogue in real time according to the user's emotional state, and further improve system performance by learning the user's responses.

[0330] "Information terminal means" refers to devices and systems that collect biometric information and surrounding environmental information from users.

[0331] "Analysis means" refers to a device that includes mechanisms and algorithms for estimating the user's emotional state based on collected biological and environmental information.

[0332] "Control means" refers to devices or processes for inputting prompts into a generative AI model and generating conversation content based on the analyzed emotional state.

[0333] "Voice output means" refers to devices or technologies that convert generated conversation content into voice signals and transmit information to the user via voice.

[0334] "Update methods" refer to functions and processes that detect user responses and reflect them in the training dataset of the generated AI model.

[0335] "Management means" refers to devices and systems that manage electronic devices based on information about the surrounding environment and optimize the user's living environment.

[0336] This system aims to accurately recognize the user's emotional state and provide dialogue based on that understanding. The following describes the configuration for implementing this system.

[0337] The terminal collects data through wearable devices worn by the user and various environmental sensors placed in the living space. Specifically, it acquires biometric information such as heart rate, body temperature, and sleep patterns from wearable devices, and environmental information such as room temperature, humidity, and lighting intensity from environmental sensors. This data is aggregated in the terminal via Bluetooth or Wi-Fi.

[0338] The device transmits the collected information to the server using a secure protocol, and the server uses analysis tools to estimate the user's emotional state based on biometric and environmental information. This emotion estimation is supplemented by an emotion engine that analyzes the user's facial expressions and tone of voice in real time.

[0339] The server uses a generative AI model to generate appropriate conversation content based on the estimated emotional state. This generative AI model constructs a natural dialogue with the user based on predefined prompts. For example, it customizes the conversation using prompts such as, "What advice should be offered if the user is feeling stressed?"

[0340] The generated conversation content is transmitted to the user via the device's voice output system. Using a speech synthesis engine, natural-sounding speech is generated to provide the user with a more human-like experience. This output allows the user to receive emotionally responsive interactions.

[0341] Furthermore, user feedback is sent back to the server via the device, and this feedback is used to train the generated AI model, leading to continuous performance improvements. In addition, the server controls electronic devices based on environmental information using management tools to optimize the user's living environment. For example, if a user desires relaxation, the lighting settings are lowered and relaxing music is played to adjust the environment.

[0342] In this way, the system can provide flexible and sophisticated interactions tailored to each individual user, making it possible to create a comfortable living environment.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] The terminal acquires biometric information from the wearable device worn by the user. Here, it communicates with the device via Bluetooth and collects data such as heart rate, body temperature, and sleep patterns. The input is raw biometric data, which is then organized as time-series data and stored in a database.

[0346] Step 2:

[0347] The device collects ambient environmental information from environmental sensors placed in the living space. Specifically, it communicates with the sensors using Wi-Fi to acquire data such as room temperature, humidity, and lighting intensity. This input data is organized with a timestamp along with the ID of each sensor and sent to the server at regular intervals.

[0348] Step 3:

[0349] The server receives biometric and environmental information transmitted from terminals via a secure protocol. This data is aggregated and preprocessed. Missing data is imputed, outliers are detected and removed, and noise is filtered to construct a dataset ready for analysis.

[0350] Step 4:

[0351] The server performs analysis using pre-processed data. Here, a machine learning algorithm is used to estimate the user's emotional state. Biometric information, environmental information, and historical emotional data are referenced as input, and the current emotional score is output in real time. The emotional score serves as the basis for the system's next action.

[0352] Step 5:

[0353] The server generates conversation content using a generative AI model based on the analysis results. Specifically, it inputs prompt sentences corresponding to the emotion score into the generative AI model and obtains a response in text format. An example of such a prompt sentence would be, "What advice should be given if the user is feeling stressed?"

[0354] Step 6:

[0355] The device processes the conversation received from the server using a speech synthesis engine and converts it into an audio signal. The generated audio is then transmitted to the user through the speaker. The audio output is adjusted to match the user's emotional state, providing a natural conversational experience.

[0356] Step 7:

[0357] Users input their responses to voice messages via their devices. This feedback should include changes in emotion and specific messages. This feedback data is sent to a server and stored as training data for the generative AI model.

[0358] Step 8:

[0359] The server updates the generated AI model based on the feedback received, aiming to improve the overall system performance. Furthermore, it optimizes the living environment by controlling IoT devices via environmental sensors and managing lighting and music in response to the user's emotional state and feedback.

[0360] (Application Example 2)

[0361] 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."

[0362] In an environment where immediate responses based on user emotions and circumstances are required, traditional methods make it difficult to provide rapid and appropriate services. Furthermore, effectively utilizing feedback necessary to improve the customer experience remains a challenge. Additionally, there is a lack of means to optimize users' living environments according to their specific circumstances.

[0363] 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.

[0364] In this invention, the server includes a user interface means for acquiring the user's physiological data and ambient data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating interaction content and communicating it to the user. This enables a quick and appropriate response in accordance with the user's emotional state.

[0365] "User physiological data" refers to data about the user's physical condition, including heart rate, body temperature, and sleep information.

[0366] "Ambient data" refers to information such as temperature, humidity, and lighting conditions in the user's living space and surrounding environment.

[0367] "User interface means" refers to devices and systems used to acquire and transmit information with the user.

[0368] "Information processing means" refers to a system that analyzes acquired physiological data and ambient data to evaluate and judge the user's state.

[0369] "Interaction content" refers to the content of conversations and responses generated based on the user's state assessment.

[0370] The system designed to realize this application has the capability to provide immediate responses based on the customer's emotional state and integrates smart devices with cloud services. The server acquires the user's physiological data and ambient data, and analyzes this information in the cloud. This analysis utilizes software such as Google Cloud's Vision AI and Speech-to-Text API. As a result, the user's emotional state is identified in real time.

[0371] Based on the analysis results, the server generates appropriate interaction content using a generative AI model. This content is provided to the user through the voice output or display of a smart device. Furthermore, devices such as smart glasses, used as a user interface, contribute to this process by capturing customer facial expressions and voice data.

[0372] As a concrete example, when store staff use smart glasses, the glasses' camera and microphone collect the customer's facial expressions and voice, and send them to a server. If the server's analysis determines that the customer is stressed or fatigued, the staff will be given a suggested customer service approach, such as, "You seem tired. Can I help you?"

[0373] A concrete example of an input prompt for a generative AI model would be: "Based on emotional data obtained from the customer's face and voice, identify signs of relaxation or stress and devise specific countermeasures."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The device uses the camera and microphone of smart glasses to capture the user's facial expressions and voice in real time. This input data includes image data (facial expressions) and audio data (voice tone).

[0377] Step 2:

[0378] The device preprocesses the captured data and sends it to Google Cloud's Vision AI and Speech-to-Text API. This process converts facial expression data into an analyzable format and audio data into text. The output consists of the analyzed facial expression data and the transcribed text data, respectively.

[0379] Step 3:

[0380] The server uses the received, analyzed data to perform an analysis using its emotion engine. The generative AI model takes this data as input, processes it to identify the user's emotional state, and outputs an emotion evaluation. For example, an evaluation result such as "The customer is feeling stressed" might be output.

[0381] Step 4:

[0382] The server generates conversation content based on sentiment evaluation and prompts. This generation process constructs user-appropriate interaction content based on the evaluation results and outputs it as response data in voice or text format.

[0383] Step 5:

[0384] The terminal receives the generated response data and transmits it to the user using speech synthesis. The user receives the generated conversation content through their eyes and ears and can confirm it visually or aurally.

[0385] Step 6:

[0386] The device then retrieves user feedback and nonverbal responses and sends them back to the server. The server uses this feedback to further train the generative AI model and improve the entire system.

[0387] 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.

[0388] 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 those described above. 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 shown 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention provides an AI system that is tailored to the user's living environment and health condition. This system collects the user's vital data and environmental data, analyzes it on a server to evaluate the user's state, and generates natural and meaningful conversations based on the evaluation results. The aim is to maintain the user's physical and mental health and reduce feelings of loneliness.

[0404] Specifically, users wear wearable devices that collect vital data such as heart rate, body temperature, and sleep patterns through the terminal. Furthermore, environmental data such as room temperature, humidity, and lighting conditions are simultaneously acquired using IoT devices installed in the user's living space. The terminal transmits this information to a server using secure communication.

[0405] The server analyzes the received data in real time to understand the user's health status and emotional state. Based on this assessment, a generative AI model creates conversational content tailored to the user. For example, if signs of sleep deprivation or stress are detected, the server designs a conversation suggesting relaxation advice or light exercise. Furthermore, user feedback is sent to the server and used to train the AI ​​model.

[0406] The terminal uses a speech synthesis engine to output the generated conversation content as voice, interacting with the user. It also issues commands to IoT devices as needed to adjust the user's living environment. For example, if it determines that the user's sleep quality is poor, it will take measures such as changing the bedroom lighting to a warmer color or adjusting the room temperature.

[0407] Through this process, the system of the present invention effectively monitors the user's health status and provides support based on individual needs. This enables the user to enjoy a healthy and comfortable life.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The device acquires heart rate, body temperature, and sleep data from wearable devices worn by the user via Bluetooth or Wi-Fi. Additionally, the device uses sensors to acquire environmental data such as room temperature, humidity, and lighting conditions from IoT devices installed in the user's living space.

[0411] Step 2:

[0412] The terminal temporarily stores the collected vital and environmental data and transmits the data to the server at regular intervals using a secure communication protocol (e.g., HTTPS).

[0413] Step 3:

[0414] The server analyzes the received user data to estimate their health and emotional state. This assessment includes comparing it with past user data. The results are stored in an internal database and used in the next step.

[0415] Step 4:

[0416] The server generates personalized conversation content using a generative AI model based on the user's evaluated state. The generation process takes into account the user's past conversation history and current state.

[0417] Step 5:

[0418] The terminal receives conversation content from the server, converts it into speech using a speech synthesis engine, and outputs the voice to the user. Furthermore, it sends commands to IoT devices as needed to adjust the room temperature, change the lighting, and so on.

[0419] Step 6:

[0420] Users can provide feedback through conversation. This feedback is sent to the server by the device.

[0421] Step 7:

[0422] The server collects user feedback and uses it as training data for the generated AI model. This allows the system to continuously evolve and improve the accuracy of future dialogue generation.

[0423] (Example 1)

[0424] 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."

[0425] A lack of systems that cater to users' living environments and health conditions results in a problem where maintaining physical and mental health and reducing feelings of loneliness are not effectively achieved. In particular, the difficulty in real-time analysis of collected data and the generation of appropriate feedback makes it difficult to provide appropriate responses that are tailored to the user's health condition and emotions.

[0426] 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.

[0427] In this invention, the server includes information device means for collecting the user's biometric data and surrounding environment data; processing means for analyzing the collected data and measuring the user's health status; and processing means for generating and providing dialogue content to the user based on the measured user health status. This enables the evaluation of health status based on individual data, the generation of appropriate dialogue, and even the automatic adjustment of the user's living environment.

[0428] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and sleep patterns.

[0429] "Surrounding environment data" refers to information about the environment in the space where the user lives, such as room temperature, humidity, and lighting conditions.

[0430] "Information device means" refers to electronic devices and terminals used for collecting and providing data.

[0431] "Processing device" refers to a device that performs computational processing for data analysis and generation of dialogue content.

[0432] "Health status" refers to an indicator that shows the user's mental and physical condition and emotional state.

[0433] "Dialogue content" refers to the content of conversations and advice exchanged between the user and the system.

[0434] A "generative intelligence model" refers to an artificial intelligence model that generates dialogue based on user input.

[0435] "Various devices" refers to home appliances and IoT devices that can adjust the user's living environment.

[0436] This invention relates to an AI system that effectively monitors a user's health status and provides support based on their individual needs. This system consists primarily of three elements.

[0437] First, the user wears a wearable device to collect biometric data such as heart rate, body temperature, and sleep data. This device is expected to utilize various commercially available health monitoring devices. In addition, IoT sensors are installed in the user's living space to collect ambient environmental data such as room temperature, humidity, and lighting.

[0438] Next, the device transmits this biometric data and surrounding environment data obtained from the user to the server using a secure communication protocol. One example of such a protocol is HTTPS. The device can function as a smartphone or a dedicated home device.

[0439] The server runs a data analysis program to analyze the received data in real time. This analysis can utilize libraries such as Python's pandas and NumPy. Based on the analysis results, the server uses a generative AI model to generate conversation content appropriate to the user's state. One example of such a model is GPT-3. An example of a prompt might be: "If the user is tired, how should we suggest relaxation?"

[0440] For example, if the user's data shows signs of stress, the server will generate and provide the user with conversational advice on how to relax. For instance, it might suggest, "Why don't you try taking some deep breaths?"

[0441] In this way, users can enjoy a healthy and comfortable life. The coordinated operation of each step in the system makes it possible to improve the user's health and quality of life.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user collects biometric data using a wearable device. Specifically, it automatically acquires data on heart rate, body temperature, and sleep duration during daily activities. The input to this process is raw sensor data from the user's body, and the output is statistical data indicating the user's health status.

[0445] Step 2:

[0446] The device collects ambient environmental data from IoT sensors installed in the user's living space. Specifically, it acquires information on room temperature, humidity, and lighting at regular intervals. The input data is environmental information from the sensors, and the output is organized ambient environmental data.

[0447] Step 3:

[0448] The device transmits collected biometric data and ambient environmental data to the server using a secure protocol (e.g., HTTPS). During this process, the data is encrypted before transmission. The input is the collected data, and the output is the data received by the server and prepared for analysis.

[0449] Step 4:

[0450] The server analyzes the received data and performs analyses to understand the user's health and emotional state. It utilizes Python libraries to process the data, perform trend analysis, and detect anomalies. The input is data from the terminal, and the output is the result of the health assessment.

[0451] Step 5:

[0452] The server uses a generative AI model to generate prompts based on the analysis results and construct the content of the conversation with the user. Specifically, if there are signs of stress, it will generate a prompt that "suggests ways to relax." The input is the result of the health assessment, and the output is conversation content appropriate for the user.

[0453] Step 6:

[0454] The server collects user feedback and uses it as training data for an AI model to help generate future dialogues. The input is the user's reactions and satisfaction level, and the output is the updated training data.

[0455] Step 7:

[0456] The terminal uses speech synthesis technology to output the generated dialogue content to the user as voice, and controls IoT devices as needed to adjust the user's living environment. Specifically, it can change the color of the lighting or adjust the heating settings. The input consists of the generated dialogue content and control commands, while the output consists of voice messages to the user and changes in the status of IoT devices.

[0457] (Application Example 1)

[0458] 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."

[0459] In modern society, for individual users to lead healthy and comfortable lives, it is crucial to appropriately monitor their health status and living environment and provide information tailored to their individual needs. However, conventional technologies have struggled with real-time analysis of users' biometric and environmental data, and the provision of meaningful information based on these analysis results. This has resulted in insufficient personalized product information and service suggestions, particularly in in-store shopping experiences. Therefore, there is a need to develop systems that appropriately provide users with the information they need on the spot, thereby improving their living environment and shopping experience.

[0460] 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.

[0461] In this invention, the server includes a device means for acquiring the user's biometric and environmental data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating conversation content based on the evaluated user state and communicating it to the user. This makes it possible to appropriately provide the information the user requests and to suggest product information and services that are appropriate to their health condition.

[0462] "Biometric data" refers to various types of information that indicate the user's physical condition, such as heart rate, body temperature, and sleep patterns.

[0463] "Environmental data" refers to information about the environment in which the user is located, such as room temperature, humidity, and illuminance around the user.

[0464] "Device means" refers to hardware devices for acquiring and managing biometric data and environmental data.

[0465] "Information processing means" refers to the process of analyzing acquired data and evaluating the user's state using analysis software and algorithms executed on a server.

[0466] An "information display device" is a digital device used by users to visually perceive information.

[0467] "Product information" refers to data regarding the details, features, and pricing of products sold in physical stores.

[0468] "Service" refers to sales, assistance, experiences, and other services provided to customers, and encompasses various offerings aimed at improving user satisfaction.

[0469] "Control equipment" refers to devices that change the physical state of a living space, such as air conditioning and lighting systems, which are operated based on environmental data.

[0470] The system for carrying out this invention consists of an information display device worn by the user, a device means, an information processing means, and a control device. The server collects the user's biometric data and environmental data through the device means and analyzes this data in the information processing means. This analysis uses data processing and machine learning algorithms built in programming languages ​​such as Python and R. Based on the obtained analysis results, the server utilizes a generative AI model to generate conversation content that is appropriate to the user's situation. For example, OpenAI's GPT-3 is used as a generative AI model.

[0471] The generated conversation is converted into speech data using speech synthesis software (e.g., Amazon Polly) and transmitted to the user via their information display device. User feedback is sent to the server as separate data and used to train the generating AI model. Furthermore, the server operates control devices based on environmental data to adjust the user's living environment. This operation, for example, involves changing the settings of air conditioners and lighting fixtures to provide the user with an optimal living environment.

[0472] As a concrete example, if a user wearing smart glasses in a physical store feels the need to relax, the server will detect that the user's heart rate is higher than normal and will announce via voice, "We'd like to recommend the perfect product for your relaxation time. You can try our aromatherapy candles here." In this case, a possible prompt to confirm the recommended product could be text in the format of, "This is the relaxation item we recommend for you. Would you like to try it?"

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The device collects the user's biometric data (heart rate, body temperature, sleep patterns, etc.) and environmental data (room temperature, humidity, illuminance, etc.) via the device's means. This data is input in real time from sensors. The device formats this data and transmits it to a server via a secure network.

[0476] Step 2:

[0477] The server analyzes the received biometric and environmental data. Here, data cleansing and filtering are performed to remove outliers and noise. Next, statistical methods and machine learning algorithms are applied using Python to evaluate the user's health and emotional state. The output generates an evaluation of the user's state.

[0478] Step 3:

[0479] Based on the analysis results, the server generates conversation content suitable for the user using a generative AI model. Here, the generative AI model utilizes a natural language generation model such as GPT-3, using pre-configured prompt sentences. The evaluation results are combined with the prompts as input, and conversation content is generated as output.

[0480] Step 4:

[0481] The server inputs the generated conversation into speech synthesis software and converts it into audio data. For example, Amazon Polly is used for speech synthesis. The audio data is temporarily sent to the terminal and output as audio through the user's information display device.

[0482] Step 5:

[0483] User feedback is collected on the device and sent to the server. The server receives this feedback and incorporates it as training data for the generative AI model, thereby improving the accuracy of conversation generation in subsequent sessions. The feedback data is used to fine-tune the generative AI model.

[0484] Step 6:

[0485] The server re-evaluates environmental data as needed and operates control devices. For example, it sends instructions to change settings for air conditioning and lighting, making adjustments so that users can enjoy a more comfortable environment. This allows the system to respond to the user's living environment in real time.

[0486] 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.

[0487] This invention provides an AI system that combines an emotion engine that recognizes user emotions. The system aims to provide the user with the most optimal interaction based on an integrated analysis of the user's vital data, environmental data, and emotional state.

[0488] Specifically, users wear wearable devices that collect heart rate, body temperature, and sleep data. This information is collected through the device, and environmental data such as room temperature, humidity, and lighting is also obtained from IoT devices placed in the user's living space. This data is securely transmitted from the device to the server.

[0489] Furthermore, an emotion engine is incorporated that analyzes the user's face and voice, which is used when the user interacts with the system through the display and microphone. The emotion engine analyzes the user's facial expressions and voice tone in real time to estimate their emotional state. This information is comprehensively analyzed on the server along with vital data and environmental data, and reflected in the evaluation of the user's state.

[0490] Based on the analysis results, the server uses a generative AI model to generate conversation content tailored to the user. This conversation includes responses and advice that are appropriate to the user's emotions, based on data obtained from the emotion engine. For example, if the user is feeling stressed, the server will suggest information and relaxation methods that can help alleviate that stress.

[0491] The terminal transmits the conversation content to the user through speech synthesis. It also controls IoT devices based on commands from the server to improve the user's living environment. For example, it might adjust lighting or play music to enhance relaxation. User feedback is collected on the server as further learning data, contributing to system improvement.

[0492] Thus, the system of the present invention can utilize diverse user data to provide more human-like interactions tailored to individual needs. As a result, users can live with a sense of security and comfort.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The device collects heart rate, body temperature, and sleep data via the wearable device worn by the user and stores this data locally. This data is acquired using Bluetooth or Wi-Fi.

[0496] Step 2:

[0497] The terminal uses sensors to measure environmental data such as room temperature, humidity, and lighting through IoT devices installed in the room, and stores this data locally. This data is also collected by the terminal using various communication protocols.

[0498] Step 3:

[0499] A device equipped with an emotion engine captures the user's facial expressions and voice tone using a camera and microphone, and analyzes their emotions in real time. The analysis results are added to the data as the user's emotional state.

[0500] Step 4:

[0501] The device transmits collected vital data, environmental data, and emotional data to the server using a secure communication protocol, ensuring the safe transfer of data.

[0502] Step 5:

[0503] The server comprehensively analyzes the received data to evaluate the user's health and emotional state. Based on this evaluation, a generative AI model develops conversation content optimized for the user.

[0504] Step 6:

[0505] The server sends the generated conversation content to the terminal, which then outputs it to the user as a conversation via a speech synthesis engine. It also sends important commands to IoT devices, such as adjusting the environment by changing the lighting to a relaxing color or playing soothing music.

[0506] Step 7:

[0507] Users provide feedback and comments to their devices through conversation. This feedback is sent from the device to the server and used as training data for the next AI model. The system evolves based on this information, enabling it to provide services that are even more tailored to the user.

[0508] (Example 2)

[0509] 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."

[0510] In modern society, there is a demand for flexible dialogue interfaces that respond to each user's emotional state and surrounding environment. However, existing systems struggle to analyze a user's biometric and environmental information in real time and provide dialogue tailored to individual needs based on that analysis. Furthermore, current systems lack sufficient mechanisms to continuously reflect user responses in the system to improve the quality of dialogue. To solve these problems, a system is needed that can accurately estimate emotional states, dynamically generate responses, and further optimize the user's environment.

[0511] 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.

[0512] In this invention, the server includes an information terminal means for collecting the user's biometric information and surrounding environment information, an analysis means for estimating the user's emotional state based on the collected information, and a control means for inputting prompts to a generation AI model using the emotional state obtained by the analysis means and generating conversation content. This makes it possible to provide flexible and sophisticated dialogue in real time according to the user's emotional state, and further improve system performance by learning the user's responses.

[0513] "Information terminal means" refers to devices and systems that collect biometric information and surrounding environmental information from users.

[0514] "Analysis means" refers to a device that includes mechanisms and algorithms for estimating the user's emotional state based on collected biological and environmental information.

[0515] "Control means" refers to devices or processes for inputting prompts into a generative AI model and generating conversation content based on the analyzed emotional state.

[0516] "Voice output means" refers to devices or technologies that convert generated conversation content into voice signals and transmit information to the user via voice.

[0517] "Update methods" refer to functions and processes that detect user responses and reflect them in the training dataset of the generated AI model.

[0518] "Management means" refers to devices and systems that manage electronic devices based on information about the surrounding environment and optimize the user's living environment.

[0519] This system aims to accurately recognize the user's emotional state and provide dialogue based on that understanding. The following describes the configuration for implementing this system.

[0520] The terminal collects data through wearable devices worn by the user and various environmental sensors placed in the living space. Specifically, it acquires biometric information such as heart rate, body temperature, and sleep patterns from wearable devices, and environmental information such as room temperature, humidity, and lighting intensity from environmental sensors. This data is aggregated in the terminal via Bluetooth or Wi-Fi.

[0521] The device transmits the collected information to the server using a secure protocol, and the server uses analysis tools to estimate the user's emotional state based on biometric and environmental information. This emotion estimation is supplemented by an emotion engine that analyzes the user's facial expressions and tone of voice in real time.

[0522] The server uses a generative AI model to generate appropriate conversation content based on the estimated emotional state. This generative AI model constructs a natural dialogue with the user based on predefined prompts. For example, it customizes the conversation using prompts such as, "What advice should be offered if the user is feeling stressed?"

[0523] The generated conversation content is transmitted to the user via the device's voice output system. Using a speech synthesis engine, natural-sounding speech is generated to provide the user with a more human-like experience. This output allows the user to receive emotionally responsive interactions.

[0524] Furthermore, user feedback is sent back to the server via the device, and this feedback is used to train the generated AI model, leading to continuous performance improvements. In addition, the server controls electronic devices based on environmental information using management tools to optimize the user's living environment. For example, if a user desires relaxation, the lighting settings are lowered and relaxing music is played to adjust the environment.

[0525] In this way, the system can provide flexible and sophisticated interactions tailored to each individual user, making it possible to create a comfortable living environment.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] The terminal acquires biometric information from the wearable device worn by the user. Here, it communicates with the device via Bluetooth and collects data such as heart rate, body temperature, and sleep patterns. The input is raw biometric data, which is then organized as time-series data and stored in a database.

[0529] Step 2:

[0530] The device collects ambient environmental information from environmental sensors placed in the living space. Specifically, it communicates with the sensors using Wi-Fi to acquire data such as room temperature, humidity, and lighting intensity. This input data is organized with a timestamp along with the ID of each sensor and sent to the server at regular intervals.

[0531] Step 3:

[0532] The server receives biometric and environmental information transmitted from terminals via a secure protocol. This data is aggregated and preprocessed. Missing data is imputed, outliers are detected and removed, and noise is filtered to construct a dataset ready for analysis.

[0533] Step 4:

[0534] The server performs analysis using pre-processed data. Here, a machine learning algorithm is used to estimate the user's emotional state. Biometric information, environmental information, and historical emotional data are referenced as input, and the current emotional score is output in real time. The emotional score serves as the basis for the system's next action.

[0535] Step 5:

[0536] The server generates conversation content using a generative AI model based on the analysis results. Specifically, it inputs prompt sentences corresponding to the emotion score into the generative AI model and obtains a response in text format. An example of such a prompt sentence would be, "What advice should be given if the user is feeling stressed?"

[0537] Step 6:

[0538] The device processes the conversation received from the server using a speech synthesis engine and converts it into an audio signal. The generated audio is then transmitted to the user through the speaker. The audio output is adjusted to match the user's emotional state, providing a natural conversational experience.

[0539] Step 7:

[0540] Users input their responses to voice messages via their devices. This feedback should include changes in emotion and specific messages. This feedback data is sent to a server and stored as training data for the generative AI model.

[0541] Step 8:

[0542] The server updates the generated AI model based on the feedback received, aiming to improve the overall system performance. Furthermore, it optimizes the living environment by controlling IoT devices via environmental sensors and managing lighting and music in response to the user's emotional state and feedback.

[0543] (Application Example 2)

[0544] 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."

[0545] In an environment where immediate responses based on user emotions and circumstances are required, traditional methods make it difficult to provide rapid and appropriate services. Furthermore, effectively utilizing feedback necessary to improve the customer experience remains a challenge. Additionally, there is a lack of means to optimize users' living environments according to their specific circumstances.

[0546] 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.

[0547] In this invention, the server includes a user interface means for acquiring the user's physiological data and ambient data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating interaction content and communicating it to the user. This enables a quick and appropriate response in accordance with the user's emotional state.

[0548] "User physiological data" refers to data about the user's physical condition, including heart rate, body temperature, and sleep information.

[0549] "Ambient data" refers to information such as temperature, humidity, and lighting conditions in the user's living space and surrounding environment.

[0550] "User interface means" refers to devices and systems used to acquire and transmit information with the user.

[0551] "Information processing means" refers to a system that analyzes acquired physiological data and ambient data to evaluate and judge the user's state.

[0552] "Interaction content" refers to the content of conversations and responses generated based on the user's state assessment.

[0553] The system designed to realize this application has the capability to provide immediate responses based on the customer's emotional state and integrates smart devices with cloud services. The server acquires the user's physiological data and ambient data, and analyzes this information in the cloud. This analysis utilizes software such as Google Cloud's Vision AI and Speech-to-Text API. As a result, the user's emotional state is identified in real time.

[0554] Based on the analysis results, the server generates appropriate interaction content using a generative AI model. This content is provided to the user through the voice output or display of a smart device. Furthermore, devices such as smart glasses, used as a user interface, contribute to this process by capturing customer facial expressions and voice data.

[0555] As a concrete example, when store staff use smart glasses, the glasses' camera and microphone collect the customer's facial expressions and voice, and send them to a server. If the server's analysis determines that the customer is stressed or fatigued, the staff will be given a suggested customer service approach, such as, "You seem tired. Can I help you?"

[0556] A concrete example of an input prompt for a generative AI model would be: "Based on emotional data obtained from the customer's face and voice, identify signs of relaxation or stress and devise specific countermeasures."

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The device uses the camera and microphone of smart glasses to capture the user's facial expressions and voice in real time. This input data includes image data (facial expressions) and audio data (voice tone).

[0560] Step 2:

[0561] The device preprocesses the captured data and sends it to Google Cloud's Vision AI and Speech-to-Text API. This process converts facial expression data into an analyzable format and audio data into text. The output consists of the analyzed facial expression data and the transcribed text data, respectively.

[0562] Step 3:

[0563] The server uses the received, analyzed data to perform an analysis using its emotion engine. The generative AI model takes this data as input, processes it to identify the user's emotional state, and outputs an emotion evaluation. For example, an evaluation result such as "The customer is feeling stressed" might be output.

[0564] Step 4:

[0565] The server generates conversation content based on sentiment evaluation and prompts. This generation process constructs user-appropriate interaction content based on the evaluation results and outputs it as response data in voice or text format.

[0566] Step 5:

[0567] The terminal receives the generated response data and transmits it to the user using speech synthesis. The user receives the generated conversation content through their eyes and ears and can confirm it visually or aurally.

[0568] Step 6:

[0569] The device then retrieves user feedback and nonverbal responses and sends them back to the server. The server uses this feedback to further train the generative AI model and improve the entire system.

[0570] 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.

[0571] 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 those described above. 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 shown 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.

[0572] 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.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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".

[0587] This invention provides an AI system that is tailored to the user's living environment and health condition. This system collects the user's vital data and environmental data, analyzes it on a server to evaluate the user's state, and generates natural and meaningful conversations based on the evaluation results. The aim is to maintain the user's physical and mental health and reduce feelings of loneliness.

[0588] Specifically, users wear wearable devices that collect vital data such as heart rate, body temperature, and sleep patterns through the terminal. Furthermore, environmental data such as room temperature, humidity, and lighting conditions are simultaneously acquired using IoT devices installed in the user's living space. The terminal transmits this information to a server using secure communication.

[0589] The server analyzes the received data in real time to understand the user's health status and emotional state. Based on this assessment, a generative AI model creates conversational content tailored to the user. For example, if signs of sleep deprivation or stress are detected, the server designs a conversation suggesting relaxation advice or light exercise. Furthermore, user feedback is sent to the server and used to train the AI ​​model.

[0590] The terminal uses a speech synthesis engine to output the generated conversation content as voice, interacting with the user. It also issues commands to IoT devices as needed to adjust the user's living environment. For example, if it determines that the user's sleep quality is poor, it will take measures such as changing the bedroom lighting to a warmer color or adjusting the room temperature.

[0591] Through this process, the system of the present invention effectively monitors the user's health status and provides support based on individual needs. This enables the user to enjoy a healthy and comfortable life.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device acquires heart rate, body temperature, and sleep data from wearable devices worn by the user via Bluetooth or Wi-Fi. Additionally, the device uses sensors to acquire environmental data such as room temperature, humidity, and lighting conditions from IoT devices installed in the user's living space.

[0595] Step 2:

[0596] The terminal temporarily stores the collected vital and environmental data and transmits the data to the server at regular intervals using a secure communication protocol (e.g., HTTPS).

[0597] Step 3:

[0598] The server analyzes the received user data to estimate their health and emotional state. This assessment includes comparing it with past user data. The results are stored in an internal database and used in the next step.

[0599] Step 4:

[0600] The server generates personalized conversation content using a generative AI model based on the user's evaluated state. The generation process takes into account the user's past conversation history and current state.

[0601] Step 5:

[0602] The terminal receives conversation content from the server, converts it into speech using a speech synthesis engine, and outputs the voice to the user. Furthermore, it sends commands to IoT devices as needed to adjust the room temperature, change the lighting, and so on.

[0603] Step 6:

[0604] Users can provide feedback through conversation. This feedback is sent to the server by the device.

[0605] Step 7:

[0606] The server collects user feedback and uses it as training data for the generated AI model. This allows the system to continuously evolve and improve the accuracy of future dialogue generation.

[0607] (Example 1)

[0608] 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".

[0609] A lack of systems that cater to users' living environments and health conditions results in a problem where maintaining physical and mental health and reducing feelings of loneliness are not effectively achieved. In particular, the difficulty in real-time analysis of collected data and the generation of appropriate feedback makes it difficult to provide appropriate responses that are tailored to the user's health condition and emotions.

[0610] 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.

[0611] In this invention, the server includes information device means for collecting the user's biometric data and surrounding environment data; processing means for analyzing the collected data and measuring the user's health status; and processing means for generating and providing dialogue content to the user based on the measured user health status. This enables the evaluation of health status based on individual data, the generation of appropriate dialogue, and even the automatic adjustment of the user's living environment.

[0612] "Biometric data" refers to information about the user's body, such as heart rate, body temperature, and sleep patterns.

[0613] "Surrounding environment data" refers to information about the environment in the space where the user lives, such as room temperature, humidity, and lighting conditions.

[0614] "Information device means" refers to electronic devices and terminals used for collecting and providing data.

[0615] "Processing device" refers to a device that performs computational processing for data analysis and generation of dialogue content.

[0616] "Health status" refers to an indicator that shows the user's mental and physical condition and emotional state.

[0617] "Dialogue content" refers to the content of conversations and advice exchanged between the user and the system.

[0618] A "generative intelligence model" refers to an artificial intelligence model that generates dialogue based on user input.

[0619] "Various devices" refers to home appliances and IoT devices that can adjust the user's living environment.

[0620] This invention relates to an AI system that effectively monitors a user's health status and provides support based on their individual needs. This system consists primarily of three elements.

[0621] First, the user wears a wearable device to collect biometric data such as heart rate, body temperature, and sleep data. This device is expected to utilize various commercially available health monitoring devices. In addition, IoT sensors are installed in the user's living space to collect ambient environmental data such as room temperature, humidity, and lighting.

[0622] Next, the device transmits this biometric data and surrounding environment data obtained from the user to the server using a secure communication protocol. One example of such a protocol is HTTPS. The device can function as a smartphone or a dedicated home device.

[0623] The server runs a data analysis program to analyze the received data in real time. This analysis can utilize libraries such as Python's pandas and NumPy. Based on the analysis results, the server uses a generative AI model to generate conversation content appropriate to the user's state. One example of such a model is GPT-3. An example of a prompt might be: "If the user is tired, how should we suggest relaxation?"

[0624] For example, if the user's data shows signs of stress, the server will generate and provide the user with conversational advice on how to relax. For instance, it might suggest, "Why don't you try taking some deep breaths?"

[0625] In this way, users can enjoy a healthy and comfortable life. The coordinated operation of each step in the system makes it possible to improve the user's health and quality of life.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The user collects biometric data using a wearable device. Specifically, it automatically acquires data on heart rate, body temperature, and sleep duration during daily activities. The input to this process is raw sensor data from the user's body, and the output is statistical data indicating the user's health status.

[0629] Step 2:

[0630] The device collects ambient environmental data from IoT sensors installed in the user's living space. Specifically, it acquires information on room temperature, humidity, and lighting at regular intervals. The input data is environmental information from the sensors, and the output is organized ambient environmental data.

[0631] Step 3:

[0632] The device transmits collected biometric data and ambient environmental data to the server using a secure protocol (e.g., HTTPS). During this process, the data is encrypted before transmission. The input is the collected data, and the output is the data received by the server and prepared for analysis.

[0633] Step 4:

[0634] The server analyzes the received data and performs analyses to understand the user's health and emotional state. It utilizes Python libraries to process the data, perform trend analysis, and detect anomalies. The input is data from the terminal, and the output is the result of the health assessment.

[0635] Step 5:

[0636] The server uses a generative AI model to generate prompts based on the analysis results and construct the content of the conversation with the user. Specifically, if there are signs of stress, it will generate a prompt that "suggests ways to relax." The input is the result of the health assessment, and the output is conversation content appropriate for the user.

[0637] Step 6:

[0638] The server collects user feedback and uses it as training data for an AI model to help generate future dialogues. The input is the user's reactions and satisfaction level, and the output is the updated training data.

[0639] Step 7:

[0640] The terminal uses speech synthesis technology to output the generated dialogue content to the user as voice, and controls IoT devices as needed to adjust the user's living environment. Specifically, it can change the color of the lighting or adjust the heating settings. The input consists of the generated dialogue content and control commands, while the output consists of voice messages to the user and changes in the status of IoT devices.

[0641] (Application Example 1)

[0642] 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".

[0643] In modern society, for individual users to lead healthy and comfortable lives, it is crucial to appropriately monitor their health status and living environment and provide information tailored to their individual needs. However, conventional technologies have struggled with real-time analysis of users' biometric and environmental data, and the provision of meaningful information based on these analysis results. This has resulted in insufficient personalized product information and service suggestions, particularly in in-store shopping experiences. Therefore, there is a need to develop systems that appropriately provide users with the information they need on the spot, thereby improving their living environment and shopping experience.

[0644] 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.

[0645] In this invention, the server includes a device means for acquiring the user's biometric and environmental data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating conversation content based on the evaluated user state and communicating it to the user. This makes it possible to appropriately provide the information the user requests and to suggest product information and services that are appropriate to their health condition.

[0646] "Biometric data" refers to various types of information that indicate the user's physical condition, such as heart rate, body temperature, and sleep patterns.

[0647] "Environmental data" refers to information about the environment in which the user is located, such as room temperature, humidity, and illuminance around the user.

[0648] "Device means" refers to hardware devices for acquiring and managing biometric data and environmental data.

[0649] "Information processing means" refers to the process of analyzing acquired data and evaluating the user's state using analysis software and algorithms executed on a server.

[0650] An "information display device" is a digital device used by users to visually perceive information.

[0651] "Product information" refers to data regarding the details, features, and pricing of products sold in physical stores.

[0652] "Service" refers to sales, assistance, experiences, and other services provided to customers, and encompasses various offerings aimed at improving user satisfaction.

[0653] "Control equipment" refers to devices that change the physical state of a living space, such as air conditioning and lighting systems, which are operated based on environmental data.

[0654] The system for carrying out this invention consists of an information display device worn by the user, a device means, an information processing means, and a control device. The server collects the user's biometric data and environmental data through the device means and analyzes this data in the information processing means. This analysis uses data processing and machine learning algorithms built in programming languages ​​such as Python and R. Based on the obtained analysis results, the server utilizes a generative AI model to generate conversation content that is appropriate to the user's situation. For example, OpenAI's GPT-3 is used as a generative AI model.

[0655] The generated conversation is converted into speech data using speech synthesis software (e.g., Amazon Polly) and transmitted to the user via their information display device. User feedback is sent to the server as separate data and used to train the generating AI model. Furthermore, the server operates control devices based on environmental data to adjust the user's living environment. This operation, for example, involves changing the settings of air conditioners and lighting fixtures to provide the user with an optimal living environment.

[0656] As a concrete example, if a user wearing smart glasses in a physical store feels the need to relax, the server will detect that the user's heart rate is higher than normal and will announce via voice, "We'd like to recommend the perfect product for your relaxation time. You can try our aromatherapy candles here." In this case, a possible prompt to confirm the recommended product could be text in the format of, "This is the relaxation item we recommend for you. Would you like to try it?"

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The device collects the user's biometric data (heart rate, body temperature, sleep patterns, etc.) and environmental data (room temperature, humidity, illuminance, etc.) via the device's means. This data is input in real time from sensors. The device formats this data and transmits it to a server via a secure network.

[0660] Step 2:

[0661] The server analyzes the received biometric and environmental data. Here, data cleansing and filtering are performed to remove outliers and noise. Next, statistical methods and machine learning algorithms are applied using Python to evaluate the user's health and emotional state. The output generates an evaluation of the user's state.

[0662] Step 3:

[0663] Based on the analysis results, the server generates conversation content suitable for the user using a generative AI model. Here, the generative AI model utilizes a natural language generation model such as GPT-3, using pre-configured prompt sentences. The evaluation results are combined with the prompts as input, and conversation content is generated as output.

[0664] Step 4:

[0665] The server inputs the generated conversation into speech synthesis software and converts it into audio data. For example, Amazon Polly is used for speech synthesis. The audio data is temporarily sent to the terminal and output as audio through the user's information display device.

[0666] Step 5:

[0667] User feedback is collected on the device and sent to the server. The server receives this feedback and incorporates it as training data for the generative AI model, thereby improving the accuracy of conversation generation in subsequent sessions. The feedback data is used to fine-tune the generative AI model.

[0668] Step 6:

[0669] The server re-evaluates environmental data as needed and operates control devices. For example, it sends instructions to change settings for air conditioning and lighting, making adjustments so that users can enjoy a more comfortable environment. This allows the system to respond to the user's living environment in real time.

[0670] 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.

[0671] This invention provides an AI system that combines an emotion engine that recognizes user emotions. The system aims to provide the user with the most optimal interaction based on an integrated analysis of the user's vital data, environmental data, and emotional state.

[0672] Specifically, users wear wearable devices that collect heart rate, body temperature, and sleep data. This information is collected through the device, and environmental data such as room temperature, humidity, and lighting is also obtained from IoT devices placed in the user's living space. This data is securely transmitted from the device to the server.

[0673] Furthermore, an emotion engine is incorporated that analyzes the user's face and voice, which is used when the user interacts with the system through the display and microphone. The emotion engine analyzes the user's facial expressions and voice tone in real time to estimate their emotional state. This information is comprehensively analyzed on the server along with vital data and environmental data, and reflected in the evaluation of the user's state.

[0674] Based on the analysis results, the server uses a generative AI model to generate conversation content tailored to the user. This conversation includes responses and advice that are appropriate to the user's emotions, based on data obtained from the emotion engine. For example, if the user is feeling stressed, the server will suggest information and relaxation methods that can help alleviate that stress.

[0675] The terminal transmits the conversation content to the user through speech synthesis. It also controls IoT devices based on commands from the server to improve the user's living environment. For example, it might adjust lighting or play music to enhance relaxation. User feedback is collected on the server as further learning data, contributing to system improvement.

[0676] Thus, the system of the present invention can utilize diverse user data to provide more human-like interactions tailored to individual needs. As a result, users can live with a sense of security and comfort.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The device collects heart rate, body temperature, and sleep data via the wearable device worn by the user and stores this data locally. This data is acquired using Bluetooth or Wi-Fi.

[0680] Step 2:

[0681] The terminal uses sensors to measure environmental data such as room temperature, humidity, and lighting through IoT devices installed in the room, and stores this data locally. This data is also collected by the terminal using various communication protocols.

[0682] Step 3:

[0683] A device equipped with an emotion engine captures the user's facial expressions and voice tone using a camera and microphone, and analyzes their emotions in real time. The analysis results are added to the data as the user's emotional state.

[0684] Step 4:

[0685] The device transmits collected vital data, environmental data, and emotional data to the server using a secure communication protocol, ensuring the safe transfer of data.

[0686] Step 5:

[0687] The server comprehensively analyzes the received data to evaluate the user's health and emotional state. Based on this evaluation, a generative AI model develops conversation content optimized for the user.

[0688] Step 6:

[0689] The server sends the generated conversation content to the terminal, which then outputs it to the user as a conversation via a speech synthesis engine. It also sends important commands to IoT devices, such as adjusting the environment by changing the lighting to a relaxing color or playing soothing music.

[0690] Step 7:

[0691] Users provide feedback and comments to their devices through conversation. This feedback is sent from the device to the server and used as training data for the next AI model. The system evolves based on this information, enabling it to provide services that are even more tailored to the user.

[0692] (Example 2)

[0693] 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".

[0694] In modern society, there is a demand for flexible dialogue interfaces that respond to each user's emotional state and surrounding environment. However, existing systems struggle to analyze a user's biometric and environmental information in real time and provide dialogue tailored to individual needs based on that analysis. Furthermore, current systems lack sufficient mechanisms to continuously reflect user responses in the system to improve the quality of dialogue. To solve these problems, a system is needed that can accurately estimate emotional states, dynamically generate responses, and further optimize the user's environment.

[0695] 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.

[0696] In this invention, the server includes an information terminal means for collecting the user's biometric information and surrounding environment information, an analysis means for estimating the user's emotional state based on the collected information, and a control means for inputting prompts to a generation AI model using the emotional state obtained by the analysis means and generating conversation content. This makes it possible to provide flexible and sophisticated dialogue in real time according to the user's emotional state, and further improve system performance by learning the user's responses.

[0697] "Information terminal means" refers to devices and systems that collect biometric information and surrounding environmental information from users.

[0698] "Analysis means" refers to a device that includes mechanisms and algorithms for estimating the user's emotional state based on collected biological and environmental information.

[0699] "Control means" refers to devices or processes for inputting prompts into a generative AI model and generating conversation content based on the analyzed emotional state.

[0700] "Voice output means" refers to devices or technologies that convert generated conversation content into voice signals and transmit information to the user via voice.

[0701] "Update methods" refer to functions and processes that detect user responses and reflect them in the training dataset of the generated AI model.

[0702] "Management means" refers to devices and systems that manage electronic devices based on information about the surrounding environment and optimize the user's living environment.

[0703] This system aims to accurately recognize the user's emotional state and provide dialogue based on that understanding. The following describes the configuration for implementing this system.

[0704] The terminal collects data through wearable devices worn by the user and various environmental sensors placed in the living space. Specifically, it acquires biometric information such as heart rate, body temperature, and sleep patterns from wearable devices, and environmental information such as room temperature, humidity, and lighting intensity from environmental sensors. This data is aggregated in the terminal via Bluetooth or Wi-Fi.

[0705] The device transmits the collected information to the server using a secure protocol, and the server uses analysis tools to estimate the user's emotional state based on biometric and environmental information. This emotion estimation is supplemented by an emotion engine that analyzes the user's facial expressions and tone of voice in real time.

[0706] The server uses a generative AI model to generate appropriate conversation content based on the estimated emotional state. This generative AI model constructs a natural dialogue with the user based on predefined prompts. For example, it customizes the conversation using prompts such as, "What advice should be offered if the user is feeling stressed?"

[0707] The generated conversation content is transmitted to the user via the device's voice output system. Using a speech synthesis engine, natural-sounding speech is generated to provide the user with a more human-like experience. This output allows the user to receive emotionally responsive interactions.

[0708] Furthermore, user feedback is sent back to the server via the device, and this feedback is used to train the generated AI model, leading to continuous performance improvements. In addition, the server controls electronic devices based on environmental information using management tools to optimize the user's living environment. For example, if a user desires relaxation, the lighting settings are lowered and relaxing music is played to adjust the environment.

[0709] In this way, the system can provide flexible and sophisticated interactions tailored to each individual user, making it possible to create a comfortable living environment.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] The terminal acquires biometric information from the wearable device worn by the user. Here, it communicates with the device via Bluetooth and collects data such as heart rate, body temperature, and sleep patterns. The input is raw biometric data, which is then organized as time-series data and stored in a database.

[0713] Step 2:

[0714] The device collects ambient environmental information from environmental sensors placed in the living space. Specifically, it communicates with the sensors using Wi-Fi to acquire data such as room temperature, humidity, and lighting intensity. This input data is organized with a timestamp along with the ID of each sensor and sent to the server at regular intervals.

[0715] Step 3:

[0716] The server receives biometric and environmental information transmitted from terminals via a secure protocol. This data is aggregated and preprocessed. Missing data is imputed, outliers are detected and removed, and noise is filtered to construct a dataset ready for analysis.

[0717] Step 4:

[0718] The server performs analysis using pre-processed data. Here, a machine learning algorithm is used to estimate the user's emotional state. Biometric information, environmental information, and historical emotional data are referenced as input, and the current emotional score is output in real time. The emotional score serves as the basis for the system's next action.

[0719] Step 5:

[0720] The server generates conversation content using a generative AI model based on the analysis results. Specifically, it inputs prompt sentences corresponding to the emotion score into the generative AI model and obtains a response in text format. An example of such a prompt sentence would be, "What advice should be given if the user is feeling stressed?"

[0721] Step 6:

[0722] The device processes the conversation received from the server using a speech synthesis engine and converts it into an audio signal. The generated audio is then transmitted to the user through the speaker. The audio output is adjusted to match the user's emotional state, providing a natural conversational experience.

[0723] Step 7:

[0724] Users input their responses to voice messages via their devices. This feedback should include changes in emotion and specific messages. This feedback data is sent to a server and stored as training data for the generative AI model.

[0725] Step 8:

[0726] The server updates the generated AI model based on the feedback received, aiming to improve the overall system performance. Furthermore, it optimizes the living environment by controlling IoT devices via environmental sensors and managing lighting and music in response to the user's emotional state and feedback.

[0727] (Application Example 2)

[0728] 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".

[0729] In an environment where immediate responses based on user emotions and circumstances are required, traditional methods make it difficult to provide rapid and appropriate services. Furthermore, effectively utilizing feedback necessary to improve the customer experience remains a challenge. Additionally, there is a lack of means to optimize users' living environments according to their specific circumstances.

[0730] 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.

[0731] In this invention, the server includes a user interface means for acquiring the user's physiological data and ambient data, an information processing means for analyzing the acquired data and evaluating the user's state, and an information processing means for generating interaction content and communicating it to the user. This enables a quick and appropriate response in accordance with the user's emotional state.

[0732] "User physiological data" refers to data about the user's physical condition, including heart rate, body temperature, and sleep information.

[0733] "Ambient data" refers to information such as temperature, humidity, and lighting conditions in the user's living space and surrounding environment.

[0734] "User interface means" refers to devices and systems used to acquire and transmit information with the user.

[0735] "Information processing means" refers to a system that analyzes acquired physiological data and ambient data to evaluate and judge the user's state.

[0736] "Interaction content" refers to the content of conversations and responses generated based on the user's state assessment.

[0737] The system designed to realize this application has the capability to provide immediate responses based on the customer's emotional state and integrates smart devices with cloud services. The server acquires the user's physiological data and ambient data, and analyzes this information in the cloud. This analysis utilizes software such as Google Cloud's Vision AI and Speech-to-Text API. As a result, the user's emotional state is identified in real time.

[0738] Based on the analysis results, the server generates appropriate interaction content using a generative AI model. This content is provided to the user through the voice output or display of a smart device. Furthermore, devices such as smart glasses, used as a user interface, contribute to this process by capturing customer facial expressions and voice data.

[0739] As a concrete example, when store staff use smart glasses, the glasses' camera and microphone collect the customer's facial expressions and voice, and send them to a server. If the server's analysis determines that the customer is stressed or fatigued, the staff will be given a suggested customer service approach, such as, "You seem tired. Can I help you?"

[0740] A concrete example of an input prompt for a generative AI model would be: "Based on emotional data obtained from the customer's face and voice, identify signs of relaxation or stress and devise specific countermeasures."

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The device uses the camera and microphone of smart glasses to capture the user's facial expressions and voice in real time. This input data includes image data (facial expressions) and audio data (voice tone).

[0744] Step 2:

[0745] The device preprocesses the captured data and sends it to Google Cloud's Vision AI and Speech-to-Text API. This process converts facial expression data into an analyzable format and audio data into text. The output consists of the analyzed facial expression data and the transcribed text data, respectively.

[0746] Step 3:

[0747] The server uses the received, analyzed data to perform an analysis using its emotion engine. The generative AI model takes this data as input, processes it to identify the user's emotional state, and outputs an emotion evaluation. For example, an evaluation result such as "The customer is feeling stressed" might be output.

[0748] Step 4:

[0749] The server generates conversation content based on sentiment evaluation and prompts. This generation process constructs user-appropriate interaction content based on the evaluation results and outputs it as response data in voice or text format.

[0750] Step 5:

[0751] The terminal receives the generated response data and transmits it to the user using speech synthesis. The user receives the generated conversation content through their eyes and ears and can confirm it visually or aurally.

[0752] Step 6:

[0753] The device then retrieves user feedback and nonverbal responses and sends them back to the server. The server uses this feedback to further train the generative AI model and improve the entire system.

[0754] 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.

[0755] 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 those described above. 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 shown 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] A terminal means for acquiring user vital data and environmental data,

[0778] A server means that analyzes the acquired data and evaluates the user's state,

[0779] A server means that generates conversation content based on the evaluated user state and transmits it to the user,

[0780] A terminal means that converts the generated conversation content into speech and outputs it to the user,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, comprising means for obtaining user feedback and reflecting it in the learning of the generated AI model.

[0784] (Claim 3)

[0785] The system according to claim 1, comprising means for controlling IoT devices based on the aforementioned environmental data and adjusting the user's living environment.

[0786] "Example 1"

[0787] (Claim 1)

[0788] Information device means for collecting user biometric data and surrounding environment data,

[0789] A processing device that analyzes the collected data and measures the user's health status,

[0790] A processing device that generates and provides dialogue content to the user based on the measured user health status,

[0791] Information device means that converts the generated dialogue content into speech and presents it to the user,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, comprising means for acquiring user responses and utilizing them for training the generative intelligence model.

[0795] (Claim 3)

[0796] The system according to claim 1, comprising means for adjusting various devices based on the aforementioned ambient environment data to improve the user's living environment.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] A device means for acquiring user biometric data and environmental data,

[0800] Information processing means for analyzing the acquired data and evaluating the user's state,

[0801] Information processing means for generating conversation content based on the evaluated user state and communicating it to the user,

[0802] A device means that converts the generated conversation content into speech and outputs it to the user,

[0803] A means of providing information through the user's vision using information display devices worn by the user,

[0804] A means of proposing product information and services that are suitable for the user,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, comprising means for acquiring user response information and reflecting it in the learning of the generated AI model.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising means for operating control equipment based on the aforementioned environmental data to adjust the user's living environment.

[0810] "Example 2 of combining an emotion engine"

[0811] (Claim 1)

[0812] Information terminal means for collecting user biometric information and surrounding environment information,

[0813] An analysis means for estimating the user's emotional state based on the collected information,

[0814] A control means that uses the emotional state obtained by the analysis means to input prompts to a generating AI model and generates conversation content,

[0815] The generated conversation content is converted into an audio signal using speech synthesis technology and output to the user by an audio output means,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising an update means for detecting user responses and reflecting them in the training dataset of the generated AI model.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising a management means for managing electronic devices based on the surrounding environment information and optimizing the user's living environment.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A user interface means for acquiring user physiological data and ambient data,

[0824] Information processing means for analyzing the acquired data and evaluating the user's state,

[0825] Information processing means for generating interaction content based on the evaluated user state and communicating it to the user,

[0826] A user interface means that converts the generated interaction content into audio and outputs it to the user,

[0827] A user interface means that identifies the customer's emotional state and adjusts the services provided in real time,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising means for receiving user feedback and reflecting it in the learning of the generative knowledge model.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising means for operating communication control equipment based on the aforementioned ambient data to optimize the user's living environment. [Explanation of Symbols]

[0833] 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. A terminal means for acquiring user vital data and environmental data, A server means that analyzes the acquired data and evaluates the user's state, A server means that generates conversation content based on the evaluated user state and transmits it to the user, A terminal means that converts the generated conversation content into speech and outputs it to the user, A system that includes this.

2. The system according to claim 1, comprising means for obtaining user feedback and reflecting it in the learning of the generated AI model.

3. The system according to claim 1, comprising means for controlling IoT devices based on the aforementioned environmental data and adjusting the user's living environment.

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