system

Abstract

We provide the system. [Solution] Means for obtaining personal physiological information, A means for receiving and analyzing the acquired physiological information, A means of generating a health management plan based on the analyzed results and presenting it to the individual, A means of sharing the generated health management plan and analysis results with a third party, A system that includes this.

Description

Technical Field

[0001] The technology of the present 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 the chatbot's 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] In an aging society, it is an issue to continuously manage an individual's health condition while preventing loneliness and decline in cognitive function. Also, there is a demand to provide comprehensive support for responding promptly in emergencies and making life more comfortable and safe. Due to these issues, a system for enabling the elderly to live an independent life with peace of mind is needed.

Means for Solving the Problems

[0005] This invention supports the management of an individual's health status by providing a device that acquires and analyzes an individual's physiological information to generate a health management plan. Furthermore, it broadly supports an individual's health status by providing a device that allows sharing of the health management plan and information based on the analysis results with third parties. In addition, it aims to solve problems by constructing a system that includes a device that identifies an individual's interests from their conversation history, generates dialogues to reduce feelings of loneliness, and a device that provides games aimed at improving cognitive function. In this way, the invention provides a system that supports elderly people in living independent lives with peace of mind.

[0006] "Personal physiological information" refers to numerical values ​​and data that indicate an individual's physical condition, such as heart rate, blood pressure, and body temperature.

[0007] "Devices for acquisition" refer to hardware such as sensors and wearable devices for collecting physiological information.

[0008] "Analysis equipment" refers to software and hardware used to evaluate health status based on acquired physiological information and perform data analysis.

[0009] A "health management plan generating device" refers to software or a device that creates personalized exercise and medical recommendations based on analysis results.

[0010] A "device for sharing with third parties" refers to a system with communication capabilities for transmitting generated health management plans and analysis results to family members or healthcare professionals.

[0011] "Conversation history" refers to data that records the content of past conversations between an individual and a system.

[0012] A "device for generating and presenting dialogues" is a device that creates personalized dialogue content based on conversation history and presents it in audio or text format.

[0013] "Game content aimed at maintaining and improving cognitive function" refers to interactive programs that provide challenges tailored to individual abilities with the aim of activating the brain and strengthening memory. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that supports health management and aims to maintain and improve cognitive function by acquiring and analyzing an individual's physiological information. By wearing a wearable device, the user continuously acquires physiological information such as heart rate, blood pressure, and body temperature. This data is transmitted from the device to a server, which then analyzes the received physiological information.

[0036] The server generates an optimal health management plan based on the analysis results. This plan is provided to the user via their device, for example, as suggestions to adjust daily exercise levels or as notifications prompting them to visit a medical institution. The server can also share the analysis results with pre-registered family members and medical professionals as needed.

[0037] Furthermore, the device analyzes the user's conversation history and generates conversations on themes based on the user's personal interests and preferences. This reduces feelings of loneliness and allows users to enjoy communication. For example, for a user who enjoys talking about the weather, the device might offer a conversation such as, "It's a nice day today. Are you planning to go for a walk?"

[0038] Furthermore, the server analyzes the user's past gaming history and provides game content aimed at maintaining and improving cognitive function. The terminal periodically presents the user with content such as quizzes and puzzles and records the results. This allows users to activate their cognitive functions while having fun.

[0039] When a user issues a voice command, the device recognizes it and can operate home appliances accordingly. For example, it can recognize a voice command to turn on a light and automatically coordinate with a smart home system to execute the action.

[0040] Thus, this invention provides a comprehensive system that not only supports individual health management but also enhances daily life and enables rapid response in crisis management.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The terminal acquires physiological information from the user's wearable device and stores it in a local database.

[0044] Step 2:

[0045] The terminal periodically connects to the server and transfers stored physiological information. The server receives this data and begins analysis.

[0046] Step 3:

[0047] The server uses an analysis algorithm to assess its health status and identify any abnormalities or areas that need improvement.

[0048] Step 4:

[0049] The server generates an optimal health management plan based on the analysis results and sends that plan to the terminal.

[0050] Step 5:

[0051] The device notifies the user of the generated health management plan and offers suggestions to encourage appropriate exercise and lifestyle improvements.

[0052] Step 6:

[0053] The server periodically shares the user's health data and analysis results with pre-registered third parties (family members or medical professionals).

[0054] Step 7:

[0055] The device references the user's past conversation history to determine a theme for generating a new conversation.

[0056] Step 8:

[0057] The server generates a dialogue script based on the user's interests and preferences and sends it back to the terminal.

[0058] Step 9:

[0059] The device conducts a voice conversation with the user based on a script, receives user responses, and adjusts the flow of the conversation accordingly.

[0060] Step 10:

[0061] The server analyzes the user's game history and designs new game content aimed at improving cognitive function.

[0062] Step 11:

[0063] The device provides users with designed game content and records the results of gameplay.

[0064] Step 12:

[0065] When a user issues a voice command, the device recognizes the command and sends instructions to operate the smart home device.

[0066] Step 13:

[0067] If the device detects an emergency voice keyword, it will communicate with the server to notify registered emergency contacts and call for necessary assistance.

[0068] (Example 1)

[0069] 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."

[0070] There is a growing need for systems that comprehensively support individual health management and quality of life improvement. This requires technologies that integrate continuous acquisition and analysis of physiological data, dialogue based on individual interests, maintenance and improvement of cognitive function, and even automation of the living environment. However, conventional systems cannot provide these functions comprehensively; they only address them as individual functions, resulting in a lack of user convenience and efficiency.

[0071] 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.

[0072] In this invention, the server includes means for acquiring personal physiological information, means for performing statistical analysis, and means for generating and notifying a health management plan. This enables comprehensive support for health management and improvement of the quality of daily life.

[0073] "Personal physiological information" refers to physical data that indicates an individual's health status, such as heart rate, blood pressure, and body temperature.

[0074] "Statistical analysis" refers to the computational processes and methods used to organize data and understand its characteristics.

[0075] A "health management plan" refers to a specific action plan proposed based on an individual's health condition, aimed at improving lifestyle habits such as exercise and diet.

[0076] "Notification" refers to the process of informing a user of information, and this includes methods such as using voice, text, and email.

[0077] "Third party" refers to all individuals or organizations other than the user themselves and the entity providing the system.

[0078] "Dialogue history" refers to informational data that records the content of past conversations, and analysis is performed based on this data.

[0079] "Analyzing interests" refers to the process of identifying the preferences and interests of the individual being analyzed.

[0080] "Entertainment content" refers to interactive content such as games and quizzes that are intended to provide user enjoyment and improve cognitive function.

[0081] "Voice commands" refer to instructions or commands that a user gives to a device via voice.

[0082] "Related equipment" refers to electrical appliances and digital devices that operate in conjunction with the system.

[0083] This invention is a comprehensive system that continuously manages an individual's health status and supports their daily life. The user wears a wearable device to acquire daily physiological information. Specifically, sensors measure heart rate, blood pressure, body temperature, etc. This data is transmitted to a server via a terminal.

[0084] The server stores the received physiological information in a database for statistical analysis and uses analysis tools (e.g., Python's Pandas or R). A health management plan is generated from the analysis results and notified to the user via push notifications or the application. By using a generative AI model, it is possible to dynamically design a plan tailored to the individual's health condition. For example, the user may be advised to do 30 minutes of aerobic exercise three days a week.

[0085] The device also analyzes past conversation history and uses natural language processing technology to explore the user's interests. Based on this, it generates and provides conversations that match the user's interests. For example, it might prompt a user interested in the weather with a conversation like, "It's sunny today, do you have any plans to go anywhere?" Possible prompts could include something like, "Design an AI system that analyzes user interests and generates appropriate conversations."

[0086] Furthermore, the server considers the user's learning and gaming history to select entertainment content that maintains and improves cognitive function. The device periodically presents this content to the user and records the results. For example, it might provide quizzes or puzzles once a week.

[0087] Users can also control devices in their home using voice commands. The device understands the user's instructions using a voice recognition engine (e.g., common voice recognition software) and operates the device in conjunction with the smart home system. A concrete example is a system that automatically turns on the lights in response to a command such as "Turn on the lights."

[0088] This system is designed not only to support individual health management but also to improve overall efficiency and comfort in daily life.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The user begins acquiring physiological information by wearing a wearable device. The device continuously measures data such as heart rate, blood pressure, and body temperature. This physiological information is temporarily stored in the device's internal memory.

[0092] Step 2:

[0093] The device acquires data from wearable devices using Bluetooth. The acquired physiological information is then transmitted to a server via a dedicated application. The transmitted information includes measured heart rate and blood pressure data.

[0094] Step 3:

[0095] The server stores physiological information received from the terminal in a database. The stored data is analyzed using statistical analysis tools. The main processes of the analysis include detecting anomalies in the biological data and analyzing trends. If an anomaly is detected as a result of the analysis, a warning flag is output.

[0096] Step 4:

[0097] The server generates a health management plan based on the analysis results. Using a generation AI model, it creates specific suggestions tailored to the user's health condition. For example, it might generate a plan recommending "30 minutes of walking per day." The generated plan is then sent from the server to the terminal.

[0098] Step 5:

[0099] The device notifies the user of received health management plans. Notifications are sent via push notifications and in-app messaging. The information is also shared with family members and healthcare professionals as needed.

[0100] Step 6:

[0101] The device records the user's conversation history and analyzes it using natural language processing technology. This analysis identifies the user's interests and preferences. Based on the results, it generates and provides conversation content tailored to the user. For example, for a user who likes talking about the weather, it might provide the conversation, "It's sunny today, do you have any plans to go out?"

[0102] Step 7:

[0103] The server analyzes past game history and selects game content to improve cognitive function. The selected games are periodically presented to the user from the device. The user's actions are sent back to the server and stored as history.

[0104] Step 8:

[0105] Users operate household devices by issuing voice commands. The terminal uses a voice recognition engine to analyze the user's voice instructions. Based on the analysis results, it interacts with smart home devices and performs actions according to the instructions. For example, it can automatically turn on the lights as instructed.

[0106] (Application Example 1)

[0107] 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."

[0108] There is a need for efficient and effective health management and cognitive function maintenance for elderly individuals and those with chronic illnesses. However, many current systems are limited to the collection and analysis of physiological information, and fail to provide appropriate feedback and communication to individuals. Furthermore, the integration of life support functions using voice commands has not progressed. The objective of this invention is to solve these problems and realize comprehensive health management and life support.

[0109] 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.

[0110] In this invention, the server includes means for collecting physiological information of an individual; means for receiving and analyzing the physiological information obtained from the means; means for generating a health management plan based on the analysis results and presenting it to the individual; means for providing cognitive training content to the individual based on the analysis results using a generated AI model; and means for recognizing the individual's voice commands and operating home appliances. This enables the provision of real-time, appropriate health management and cognitive function improvement feedback to elderly individuals and those with chronic illnesses, as well as improved convenience in daily life.

[0111] "Individual physiological information" refers to data related to life-sustaining activities such as heart rate, blood pressure, and body temperature, which are used to evaluate the health status of a specific individual.

[0112] A "health management plan" is a set of guidelines proposed to optimize daily lifestyle habits such as exercise, diet, and rest, based on analyzed physiological information.

[0113] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate personalized feedback and content from collected data.

[0114] "Cognitive training content" refers to content that promotes intellectual activities such as games and puzzles, with the aim of maintaining and improving an individual's cognitive function.

[0115] "Voice commands" are instructions given by an individual using words, which information devices analyze and use to perform actions according to those instructions.

[0116] "Home appliances" are electrical devices used in daily life that are operated by voice commands.

[0117] The system implementing this invention is an inclusive system for efficiently managing the health of elderly individuals and those with chronic illnesses. First, when an individual wears a wearable device, physiological information such as heart rate, blood pressure, and body temperature is acquired in real time. This data is transmitted to a server in the cloud via a terminal such as smart glasses.

[0118] The server utilizes a database and implements a generative AI model to analyze this physiological information. The analysis results generate a health management plan tailored to each individual. This plan provides daily activity guidelines to optimize the individual's health.

[0119] Furthermore, a generative AI model customizes cognitive training content based on the individual's past activity history and interests. The device periodically presents this training content to the individual, maintaining and improving their cognitive function.

[0120] Regarding voice commands, the device can recognize the user's voice and send the command to a cloud server, enabling optimal control of home appliances. For example, by giving a voice command such as "Turn off the lights" at night, all the lights in the house will automatically turn off.

[0121] Examples of specific prompts include, "Please tell me the resident's current health status," and "Based on Mr. / Ms. XX's cognitive game history, suggest the next game." This allows nursing home staff to manage health status in real time and provide appropriate care for each individual.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The user wears a wearable device. This device acquires physiological data such as heart rate, blood pressure, and body temperature in real time. The acquired data is transmitted to a device such as smart glasses via Bluetooth or Wi-Fi.

[0125] Input: User's physiological information

[0126] Output: Transmission of real-time physiological data

[0127] Step 2:

[0128] The device sends the received physiological data to a cloud server. On the cloud server, the data is stored in a database and prepared for analysis.

[0129] Input: Physiological data received from a wearable device.

[0130] Output: Data storage in the cloud

[0131] Step 3:

[0132] The server analyzes the received physiological data. A generative AI model is used for the analysis to detect data trends and anomalies.

[0133] Input: Physiological data stored on a cloud server

[0134] Output: Analysis results (e.g., current health risks and abnormalities)

[0135] Step 4:

[0136] Based on the analysis results, the server automatically generates a health management plan tailored to each user. The generated plan is then presented to the user via their terminal.

[0137] Input: Analysis results of physiological data

[0138] Output: User health management plan

[0139] Step 5:

[0140] The server customizes cognitive training content based on the user's past activity history and interests. It utilizes a generative AI model to suggest the most suitable games and puzzles for the user.

[0141] Input: User history data and proposed AI model

[0142] Output: Proposal of cognitive training content

[0143] Step 6:

[0144] The terminal recognizes the user's voice commands and sends them to the server. The server generates signals to control the home appliances and makes them perform actions according to the voice commands.

[0145] Input: User's voice command

[0146] Output: Home appliance control signals based on voice commands

[0147] Step 7:

[0148] The server is configured to allow the generated health management plan and cognitive training results to be shared with third parties such as family members and healthcare professionals.

[0149] Input: User consent and sharing settings

[0150] Output: Data sharing with third parties

[0151] 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.

[0152] This invention is a system that provides more personalized health management support by combining a user's physiological information and emotions. The user continuously acquires physiological information using a wearable device. This data from the device is transmitted to a server via a terminal. The server analyzes the received physiological information and generates a health management plan based on it.

[0153] Furthermore, this invention incorporates user voice and facial expression data and uses an emotion engine to understand the user's emotional state. This allows emotional factors to be incorporated into the health management plan, providing advice tailored to the user's mental state. For example, if a high level of stress is detected, the health management plan will include recommendations for relaxation techniques and light exercise.

[0154] The user's emotions, recognized by the emotion engine, are reflected in the dialogue. The device responds with themes and tones appropriate to the user's emotions, customizing the conversation individually. This allows the user to enjoy a more comfortable dialogue. For example, if the user appears depressed, the device might respond with something like, "Is there something bothering you? Shall we talk?"

[0155] Furthermore, the game content is individually adjusted to improve cognitive function by utilizing information from the emotion engine. When the user is relaxed, slightly more challenging game content is provided, while when concentration is required, simpler content that can be progressed smoothly is offered.

[0156] A device that recognizes a user's voice commands can not only operate smart home devices, but also take into account the user's emotional state and make suggestions such as, "You seem tired today. Shall we dim the lights a little?"

[0157] This invention provides users with support that comprehensively considers their physiological and emotional needs, creating an environment that allows them to live a safe and comfortable life.

[0158] The following describes the processing flow.

[0159] Step 1:

[0160] The device retrieves daily physiological information (heart rate, blood pressure, body temperature, etc.) from the user's wearable device and stores it in a database.

[0161] Step 2:

[0162] The device acquires physiological information along with data such as the user's voice and facial expressions. The device uses an emotion engine to analyze this data and recognize the user's emotional state.

[0163] Step 3:

[0164] The device sends acquired physiological information and recognized emotional data to the server. The server analyzes this data and executes an algorithm to comprehensively evaluate the user's health and emotions.

[0165] Step 4:

[0166] The server generates a personalized health management plan based on the analyzed health status and emotions. This plan includes suggestions for exercise and relaxation methods that take into account not only physical health but also mental health.

[0167] Step 5:

[0168] The device presents the generated health management plan to the user and notifies them via voice and screen display to ensure they follow it properly.

[0169] Step 6:

[0170] The device analyzes the user's past conversation history and prepares appropriate conversation topics based on the user's emotional state.

[0171] Step 7:

[0172] The server generates appropriate dialogue content and sends it to the terminal, which then initiates a conversation with the user based on this content. During the conversation, the terminal dynamically adjusts the dialogue content in response to changes in the user's emotions.

[0173] Step 8:

[0174] The server designs game content based on the user's emotions and learning history, and delivers it to the user through the device. The device sends new data obtained from the user playing the game to the server, which is then used to inform the next learning cycle.

[0175] Step 9:

[0176] When a user uses voice commands, the device recognizes them and operates smart home devices. It also makes suggestions based on the user's emotional state, such as, "I'll play music that's perfect for your current mood."

[0177] Thus, the present invention comprehensively utilizes the user's physiological data and emotional information to provide individually optimized health management and lifestyle support.

[0178] (Example 2)

[0179] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] In health management, it is necessary to consider not only physiological data but also emotional factors and provide individualized support. However, conventional systems do not integrate physiological information analysis and emotional analysis, making it difficult to provide comprehensive support for individual needs. Furthermore, there is insufficient provision of measures to improve cognitive function using this information. Against this backdrop, there is a need for a system that comprehensively analyzes physiological and emotional information and provides support optimized for the individual.

[0181] 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.

[0182] In this invention, the server includes equipment for collecting physiological data of an individual, means for constructing and presenting a health management plan tailored to the individual based on the data analysis results, and means for providing individualized support based on emotional state and the health management plan. This enables health support that comprehensively considers the physiological and emotional needs of the individual.

[0183] "Physiological data" refers to information that indicates an individual's physical functions and condition, including heart rate, body temperature, and activity level.

[0184] "Equipment" refers to a physical or electronic device used to collect, transmit, or receive data.

[0185] "Data processing" refers to activities that involve technical operations to analyze received information and derive appropriate conclusions or judgments.

[0186] A "health management plan" refers to specific guidelines and recommendations formulated to maintain or improve an individual's health.

[0187] "Emotional state" refers to an individual's emotional response and psychological state, and is inferred from information such as voice and facial expressions.

[0188] "Digital content" refers to content provided electronically, such as games and educational materials, that aims to improve knowledge and skills.

[0189] This invention aims to achieve individually optimized health management by comprehensively analyzing an individual's physiological data and emotional state. Users acquire physiological data such as heart rate and body temperature in real time using wearable devices. Common smartwatches and fitness trackers are used as these devices.

[0190] The terminal receives physiological data acquired from wearable devices and transmits it to a server using its communication function. The server processes the data and analyzes it using a generative AI model. For example, the AI ​​model can detect abnormal heart rates early and reflect this in a health management plan. The terminal also incorporates the user's voice and facial expression information into an emotion engine to determine the individual's emotional state. This emotion engine is implemented using general speech recognition and image processing technologies.

[0191] Based on the analysis results, the server generates a personalized health management plan for the user and presents it to the user via the terminal. The terminal then incorporates individualized support into the health management plan according to the user's emotional state. For example, if the user is feeling stressed, relaxation techniques will be recommended. Conversely, if the user is in a good emotional state, more challenging cognitive function improvement content will be presented.

[0192] A possible example of a specific prompt might be: "Please suggest some relaxation methods that would be recommended when the user's heart rate is higher than normal. Also, please provide example responses for when the user is feeling depressed." In this way, users can receive individually optimized support based on their current health and emotional state.

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] The user wears a wearable device. The wearable device records physiological data such as the user's heart rate, body temperature, and activity level in real time. The input physiological data is detected by sensors within the device and transmitted to the terminal in digital format. During this process, data is collected at regular intervals and stored in the terminal.

[0196] Step 2:

[0197] The terminal receives physiological data transmitted from the wearable device. The received data is standardized within the terminal and then sent to the server via the network. Here, the data is sent to the server via Wi-Fi or a mobile data network. The output is the organized physiological data that arrives at the server.

[0198] Step 3:

[0199] The server receives physiological data transmitted from the terminal. Based on this input data, an analysis is performed using an AI algorithm. The analysis detects anomalies and patterns in the data and evaluates the health status. As output, a health evaluation result is generated.

[0200] Step 4:

[0201] Based on the analysis results, the server uses a generated AI model to create a personalized health management plan for the user. This plan includes suggestions for lifestyle improvements, exercise, and dietary advice. After the program generates the plan, it is output and sent to the terminal.

[0202] Step 5:

[0203] The device receives a health management plan transmitted from the server. Simultaneously, the device uses an emotion engine to analyze the user's voice and facial expression data to determine their emotional state. Speech recognition and image analysis technologies are used in the emotion analysis, and the user's emotional state is estimated as output.

[0204] Step 6:

[0205] The device integrates the received health management plan and analyzed emotional state to provide individually optimized advice to the user. Based on the emotional state, recommended activities and conversations are selected and presented to the user. The output provides customized instructions and advice.

[0206] Step 7:

[0207] The user takes action based on advice from the device. For example, they might perform recommended exercises or review their diet. The device also collects feedback from the user regarding their progress and sends this data back to the server to be incorporated into the next health management plan. The output is new data for the next plan.

[0208] (Application Example 2)

[0209] 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 device 14 will be referred to as the "terminal."

[0210] In recent years, in an aging society, comprehensive support that takes into account not only an individual's physiological state but also their emotional state is needed to improve health management and quality of life. However, while conventional health management systems primarily focused on collecting and analyzing physiological information, systems that grasp emotional states in real time and reflect them in health management have not been adequately realized. As a result, there are challenges in providing optimal advice and dialogue tailored to each individual, as well as appropriate content to improve cognitive function.

[0211] 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.

[0212] In this invention, the server includes means for acquiring an individual's physiological information, means for generating a health management plan based on the analyzed physiological information, means for providing a health management plan based on the individual's emotional state using emotion analysis technology, and means for sharing the generated health management plan and analysis results with a third party. This enables more personalized health management by comprehensively considering an individual's physiological and emotional needs.

[0213] "Physiological information" refers to data that indicates an individual's physical condition, including vital signs such as heart rate, body temperature, and blood pressure, as well as activity levels.

[0214] "Emotional analysis technology" is a technology that identifies an individual's emotional state at a given time by analyzing data such as their voice, facial expressions, and behavior.

[0215] A "health management plan" is a specific plan that provides guidelines for maintaining and promoting an individual's health, based on analyzed physiological information and emotional state.

[0216] A "third party" refers to anyone other than the individual receiving health management services, such as medical professionals, caregivers, or family members, who are authorized to share information.

[0217] "Emotional state" refers to an individual's mental and psychological condition, which is expressed by emotional categories such as joy, anger, sadness, and pleasure.

[0218] The system for realizing this invention collects and analyzes an individual's physiological information and emotional state to provide a health management plan. The server continuously acquires physiological information from a wearable device. The acquired data is transmitted to the server via wireless communication. The server analyzes the received physiological information, generates a health management plan, and presents it to the terminal.

[0219] The device uses speech recognition APIs and facial recognition technology to analyze the user's voice and facial information using emotion analysis technology. The emotional state obtained from the analysis is sent to the server. Based on the analysis results, the server generates a health management plan and dialogue content that takes the emotional state into account, and presents them to the user individually.

[0220] For example, if a user says, "I'm tired today," the device will recognize the voice and offer advice such as, "Let's take a short break. I recommend 10 minutes of meditation." If the device detects that the user is feeling anxious based on their facial expression, it will notify them with a message like, "Have you been stressed lately? Please let us know if there's anything we can do to help."

[0221] The hardware used includes wearable devices (Fitbit and common smartwatches), smartphones, and smart glasses. For software, Google® Speech-to-Text API is used for speech recognition, Microsoft® Azure® Emotion Recognition API for sentiment analysis, and OpenCV for facial expression recognition.

[0222] An example of a prompt using a generative AI model is, "What activities are suitable for elderly people when they are relaxed?" This allows the system to provide information appropriate to the user's state.

[0223] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0224] Step 1:

[0225] The server receives physiological data from wearable devices. This data is transmitted in the form of heart rate, activity level, etc. The server analyzes this data and monitors the individual's health status in real time. The input is physiological information from the wearable device, and the output is analyzed health indicator data.

[0226] Step 2:

[0227] The device collects the user's voice using a microphone and converts it into text data using a speech recognition API. The device then inputs this text data into an emotion analysis engine to determine the user's emotional state. The input is audio data, and the output is the estimated emotional state.

[0228] Step 3:

[0229] The device uses a camera to capture the user's face and facial recognition software to collect the user's facial expression data. This data is sent to an emotion analysis engine to supplement the emotion. The input is facial image data, and the output is the supplemented emotional state.

[0230] Step 4:

[0231] The server integrates health analysis results based on physiological information with emotional analysis results to generate a customized health management plan. The inputs are health indicator data and emotional state data, and the output is a personalized health management plan.

[0232] Step 5:

[0233] The user receives a health management plan provided via their device. They can also receive suggestions in the form of dialogue, such as, "You seem tired today. Shall we take a short break?" The input is the user's health status and emotional state, and the output is the health management plan and dialogue content.

[0234] Step 6:

[0235] The server shares the generated health management plan and related information with third parties in a secure cloud environment. This allows caregivers and medical professionals to share necessary information. The input is health management plan data, and the output is the shared information.

[0236] 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.

[0237] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0238] 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.

[0239] [Second Embodiment]

[0240] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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".

[0252] This invention is a system that supports health management and aims to maintain and improve cognitive function by acquiring and analyzing an individual's physiological information. By wearing a wearable device, the user continuously acquires physiological information such as heart rate, blood pressure, and body temperature. This data is transmitted from the device to a server, which then analyzes the received physiological information.

[0253] The server generates an optimal health management plan based on the analysis results. This plan is provided to the user via their device, for example, as suggestions to adjust daily exercise levels or as notifications prompting them to visit a medical institution. The server can also share the analysis results with pre-registered family members and medical professionals as needed.

[0254] Furthermore, the device analyzes the user's conversation history and generates conversations on themes based on the user's personal interests and preferences. This reduces feelings of loneliness and allows users to enjoy communication. For example, for a user who enjoys talking about the weather, the device might offer a conversation such as, "It's a nice day today. Are you planning to go for a walk?"

[0255] Furthermore, the server analyzes the user's past gaming history and provides game content aimed at maintaining and improving cognitive function. The terminal periodically presents the user with content such as quizzes and puzzles and records the results. This allows users to activate their cognitive functions while having fun.

[0256] When a user issues a voice command, the device recognizes it and can operate home appliances accordingly. For example, it can recognize a voice command to turn on a light and automatically coordinate with a smart home system to execute the action.

[0257] Thus, this invention provides a comprehensive system that not only supports individual health management but also enhances daily life and enables rapid response in crisis management.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The terminal acquires physiological information from the user's wearable device and stores it in a local database.

[0261] Step 2:

[0262] The terminal periodically connects to the server and transfers stored physiological information. The server receives this data and begins analysis.

[0263] Step 3:

[0264] The server uses an analysis algorithm to assess its health status and identify any abnormalities or areas that need improvement.

[0265] Step 4:

[0266] The server generates an optimal health management plan based on the analysis results and sends that plan to the terminal.

[0267] Step 5:

[0268] The device notifies the user of the generated health management plan and offers suggestions to encourage appropriate exercise and lifestyle improvements.

[0269] Step 6:

[0270] The server periodically shares the user's health data and analysis results with pre-registered third parties (family members or medical professionals).

[0271] Step 7:

[0272] The device references the user's past conversation history to determine a theme for generating a new conversation.

[0273] Step 8:

[0274] The server generates a dialogue script based on the user's interests and preferences and sends it back to the terminal.

[0275] Step 9:

[0276] The device conducts a voice conversation with the user based on a script, receives user responses, and adjusts the flow of the conversation accordingly.

[0277] Step 10:

[0278] The server analyzes the game history played by the user and designs new game content for the purpose of improving cognitive functions.

[0279] Step 11:

[0280] The terminal provides the designed game content to the user and records the results of the play.

[0281] Step 12:

[0282] When the user issues a voice command, the terminal recognizes the command and sends an instruction to operate the smart home device.

[0283] Step 13:

[0284] When the terminal detects an emergency voice keyword, it cooperates with the server to notify the registered emergency contacts and call for necessary assistance.

[0285] (Example 1)

[0286] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] The need for a system that comprehensively supports personal health management and improvement of the quality of life is increasing. Among these, technologies that integrally handle continuous acquisition and analysis of physiological information, conversation based on personal interests, maintenance and improvement of cognitive functions, and further automation of the living environment are required. However, conventional systems cannot provide these functions comprehensively and can only handle them as individual functions, so there is a problem of lacking user convenience and efficiency.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0289] In this invention, the server includes means for acquiring personal physiological information, means for performing statistical analysis, and means for generating and notifying a health management plan. This enables comprehensive support for health management and improvement of the quality of daily life.

[0290] "Personal physiological information" refers to physical data that indicates an individual's health status, such as heart rate, blood pressure, and body temperature.

[0291] "Statistical analysis" refers to the computational processes and methods used to organize data and understand its characteristics.

[0292] A "health management plan" refers to a specific action plan proposed based on an individual's health condition, aimed at improving lifestyle habits such as exercise and diet.

[0293] "Notification" refers to the process of informing a user of information, and this includes methods such as using voice, text, and email.

[0294] "Third party" refers to all individuals or organizations other than the user themselves and the entity providing the system.

[0295] "Dialogue history" refers to informational data that records the content of past conversations, and analysis is performed based on this data.

[0296] "Analyzing interests" refers to the process of identifying the preferences and interests of the individual being analyzed.

[0297] "Entertainment content" refers to interactive content such as games and quizzes that are intended to provide user enjoyment and improve cognitive function.

[0298] "Voice commands" refer to instructions or commands that a user gives to a device via voice.

[0299] "Related equipment" refers to electrical appliances and digital devices that operate in conjunction with the system.

[0300] This invention is a comprehensive system that continuously manages an individual's health status and supports their daily life. The user wears a wearable device to acquire daily physiological information. Specifically, sensors measure heart rate, blood pressure, body temperature, etc. This data is transmitted to a server via a terminal.

[0301] The server stores the received physiological information in a database for statistical analysis and uses analysis tools (e.g., Python's Pandas or R). A health management plan is generated from the analysis results and notified to the user via push notifications or the application. By using a generative AI model, it is possible to dynamically design a plan tailored to the individual's health condition. For example, the user may be advised to do 30 minutes of aerobic exercise three days a week.

[0302] The device also analyzes past conversation history and uses natural language processing technology to explore the user's interests. Based on this, it generates and provides conversations that match the user's interests. For example, it might prompt a user interested in the weather with a conversation like, "It's sunny today, do you have any plans to go anywhere?" Possible prompts could include something like, "Design an AI system that analyzes user interests and generates appropriate conversations."

[0303] Furthermore, the server considers the user's learning and gaming history to select entertainment content that maintains and improves cognitive function. The device periodically presents this content to the user and records the results. For example, it might provide quizzes or puzzles once a week.

[0304] Users can also control devices in their home using voice commands. The device understands the user's instructions using a voice recognition engine (e.g., common voice recognition software) and operates the device in conjunction with the smart home system. A concrete example is a system that automatically turns on the lights in response to a command such as "Turn on the lights."

[0305] This system is constructed not only to support personal health management but also to improve the efficiency and comfort of overall life.

[0306] The flow of the specific process in Example 1 will be described using FIG. 11.

[0307] Step 1:

[0308] The user starts acquiring physiological information by wearing a wearable device. The device continuously measures data such as heart rate, blood pressure, and body temperature. This physiological information is temporarily stored in the built-in memory of the device.

[0309] Step 2:

[0310] The terminal acquires data from the wearable device using Bluetooth. The acquired physiological information is transmitted to the server via a dedicated application. The transmitted information includes measured heart rate and blood pressure data.

[0311] Step 3:

[0312] The server stores the physiological information received from the terminal in a database. The stored data is analyzed using statistical analysis tools. As the main processes of the analysis, outlier detection and trend analysis of biological data are performed. As an analysis result, a warning flag is output when an abnormality is detected.

[0313] Step 4:

[0314] The server generates a health management plan based on the analysis result. Using a generated AI model, specific proposals according to the user's health condition are created. For example, a plan such as "Recommend 30 minutes of walking a day" is generated. The generated plan is transmitted from the server to the terminal.

[0315] Step 5:

[0316] The device notifies the user of received health management plans. Notifications are sent via push notifications and in-app messaging. The information is also shared with family members and healthcare professionals as needed.

[0317] Step 6:

[0318] The device records the user's conversation history and analyzes it using natural language processing technology. This analysis identifies the user's interests and preferences. Based on the results, it generates and provides conversation content tailored to the user. For example, for a user who likes talking about the weather, it might provide the conversation, "It's sunny today, do you have any plans to go out?"

[0319] Step 7:

[0320] The server analyzes past game history and selects game content to improve cognitive function. The selected games are periodically presented to the user from the device. The user's actions are sent back to the server and stored as history.

[0321] Step 8:

[0322] Users operate household devices by issuing voice commands. The terminal uses a voice recognition engine to analyze the user's voice instructions. Based on the analysis results, it interacts with smart home devices and performs actions according to the instructions. For example, it can automatically turn on the lights as instructed.

[0323] (Application Example 1)

[0324] 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."

[0325] There is a need for efficient and effective health management and cognitive function maintenance for elderly individuals and those with chronic illnesses. However, many current systems are limited to the collection and analysis of physiological information, and fail to provide appropriate feedback and communication to individuals. Furthermore, the integration of life support functions using voice commands has not progressed. The objective of this invention is to solve these problems and realize comprehensive health management and life support.

[0326] 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.

[0327] In this invention, the server includes means for collecting physiological information of an individual; means for receiving and analyzing the physiological information obtained from the means; means for generating a health management plan based on the analysis results and presenting it to the individual; means for providing cognitive training content to the individual based on the analysis results using a generated AI model; and means for recognizing the individual's voice commands and operating home appliances. This enables the provision of real-time, appropriate health management and cognitive function improvement feedback to elderly individuals and those with chronic illnesses, as well as improved convenience in daily life.

[0328] "Individual physiological information" refers to data related to life-sustaining activities such as heart rate, blood pressure, and body temperature, which are used to evaluate the health status of a specific individual.

[0329] A "health management plan" is a set of guidelines proposed to optimize daily lifestyle habits such as exercise, diet, and rest, based on analyzed physiological information.

[0330] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate personalized feedback and content from collected data.

[0331] "Cognitive training content" refers to content that promotes intellectual activities such as games and puzzles, with the aim of maintaining and improving an individual's cognitive function.

[0332] "Voice commands" are instructions given by an individual using words, which information devices analyze and use to perform actions according to those instructions.

[0333] "Home appliances" are electrical devices used in daily life that are operated by voice commands.

[0334] The system implementing this invention is an inclusive system for efficiently managing the health of elderly individuals and those with chronic illnesses. First, when an individual wears a wearable device, physiological information such as heart rate, blood pressure, and body temperature is acquired in real time. This data is transmitted to a server in the cloud via a terminal such as smart glasses.

[0335] The server utilizes a database and implements a generative AI model to analyze this physiological information. The analysis results generate a health management plan tailored to each individual. This plan provides daily activity guidelines to optimize the individual's health.

[0336] Furthermore, a generative AI model customizes cognitive training content based on the individual's past activity history and interests. The device periodically presents this training content to the individual, maintaining and improving their cognitive function.

[0337] Regarding voice commands, the device can recognize the user's voice and send the command to a cloud server, enabling optimal control of home appliances. For example, by giving a voice command such as "Turn off the lights" at night, all the lights in the house will automatically turn off.

[0338] Examples of specific prompts include, "Please tell me the resident's current health status," and "Based on Mr. / Ms. XX's cognitive game history, suggest the next game." This allows nursing home staff to manage health status in real time and provide appropriate care for each individual.

[0339] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0340] Step 1:

[0341] The user wears a wearable device. This device acquires physiological data such as heart rate, blood pressure, and body temperature in real time. The acquired data is transmitted to a device such as smart glasses via Bluetooth or Wi-Fi.

[0342] Input: User's physiological information

[0343] Output: Transmission of real-time physiological data

[0344] Step 2:

[0345] The device sends the received physiological data to a cloud server. On the cloud server, the data is stored in a database and prepared for analysis.

[0346] Input: Physiological data received from a wearable device.

[0347] Output: Data storage in the cloud

[0348] Step 3:

[0349] The server analyzes the received physiological data. A generative AI model is used for the analysis to detect data trends and anomalies.

[0350] Input: Physiological data stored on a cloud server

[0351] Output: Analysis results (e.g., current health risks and abnormalities)

[0352] Step 4:

[0353] Based on the analysis results, the server automatically generates a health management plan tailored to each user. The generated plan is then presented to the user via their terminal.

[0354] Input: Analysis results of physiological data

[0355] Output: User health management plan

[0356] Step 5:

[0357] The server customizes cognitive training content based on the user's past activity history and interests. It utilizes a generative AI model to suggest the most suitable games and puzzles for the user.

[0358] Input: User history data and proposed AI model

[0359] Output: Proposal of cognitive training content

[0360] Step 6:

[0361] The terminal recognizes the user's voice commands and sends them to the server. The server generates signals to control the home appliances and makes them perform actions according to the voice commands.

[0362] Input: User's voice command

[0363] Output: Home appliance control signals based on voice commands

[0364] Step 7:

[0365] The server is configured to allow the generated health management plan and cognitive training results to be shared with third parties such as family members and healthcare professionals.

[0366] Input: User consent and sharing settings

[0367] Output: Data sharing with third parties

[0368] 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.

[0369] This invention is a system that provides more personalized health management support by combining a user's physiological information and emotions. The user continuously acquires physiological information using a wearable device. This data from the device is transmitted to a server via a terminal. The server analyzes the received physiological information and generates a health management plan based on it.

[0370] Furthermore, this invention incorporates user voice and facial expression data and uses an emotion engine to understand the user's emotional state. This allows emotional factors to be incorporated into the health management plan, providing advice tailored to the user's mental state. For example, if a high level of stress is detected, the health management plan will include recommendations for relaxation techniques and light exercise.

[0371] The user's emotions, recognized by the emotion engine, are reflected in the dialogue. The device responds with themes and tones appropriate to the user's emotions, customizing the conversation individually. This allows the user to enjoy a more comfortable dialogue. For example, if the user appears depressed, the device might respond with something like, "Is there something bothering you? Shall we talk?"

[0372] Furthermore, the game content is individually adjusted to improve cognitive function by utilizing information from the emotion engine. When the user is relaxed, slightly more challenging game content is provided, while when concentration is required, simpler content that can be progressed smoothly is offered.

[0373] A device that recognizes a user's voice commands can not only operate smart home devices, but also take into account the user's emotional state and make suggestions such as, "You seem tired today. Shall we dim the lights a little?"

[0374] This invention provides users with support that comprehensively considers their physiological and emotional needs, creating an environment that allows them to live a safe and comfortable life.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The device retrieves daily physiological information (heart rate, blood pressure, body temperature, etc.) from the user's wearable device and stores it in a database.

[0378] Step 2:

[0379] The device acquires physiological information along with data such as the user's voice and facial expressions. The device uses an emotion engine to analyze this data and recognize the user's emotional state.

[0380] Step 3:

[0381] The device sends acquired physiological information and recognized emotional data to the server. The server analyzes this data and executes an algorithm to comprehensively evaluate the user's health and emotions.

[0382] Step 4:

[0383] The server generates a personalized health management plan based on the analyzed health status and emotions. This plan includes suggestions for exercise and relaxation methods that take into account not only physical health but also mental health.

[0384] Step 5:

[0385] The device presents the generated health management plan to the user and notifies them via voice and screen display to ensure they follow it properly.

[0386] Step 6:

[0387] The device analyzes the user's past conversation history and prepares appropriate conversation topics based on the user's emotional state.

[0388] Step 7:

[0389] The server generates appropriate dialogue content and sends it to the terminal, which then initiates a conversation with the user based on this content. During the conversation, the terminal dynamically adjusts the dialogue content in response to changes in the user's emotions.

[0390] Step 8:

[0391] The server designs game content based on the user's emotions and learning history, and delivers it to the user through the device. The device sends new data obtained from the user playing the game to the server, which is then used to inform the next learning cycle.

[0392] Step 9:

[0393] When a user uses voice commands, the device recognizes them and operates smart home devices. It also makes suggestions based on the user's emotional state, such as, "I'll play music that's perfect for your current mood."

[0394] Thus, the present invention comprehensively utilizes the user's physiological data and emotional information to provide individually optimized health management and lifestyle support.

[0395] (Example 2)

[0396] 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".

[0397] In health management, it is necessary to consider not only physiological data but also emotional factors and provide individualized support. However, conventional systems do not integrate physiological information analysis and emotional analysis, making it difficult to provide comprehensive support for individual needs. Furthermore, there is insufficient provision of measures to improve cognitive function using this information. Against this backdrop, there is a need for a system that comprehensively analyzes physiological and emotional information and provides support optimized for the individual.

[0398] 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.

[0399] In this invention, the server includes equipment for collecting physiological data of an individual, means for constructing and presenting a health management plan tailored to the individual based on the data analysis results, and means for providing individualized support based on emotional state and the health management plan. This enables health support that comprehensively considers the physiological and emotional needs of the individual.

[0400] "Physiological data" refers to information that indicates an individual's physical functions and condition, including heart rate, body temperature, and activity level.

[0401] "Equipment" refers to a physical or electronic device used to collect, transmit, or receive data.

[0402] "Data processing" refers to activities that involve technical operations to analyze received information and derive appropriate conclusions or judgments.

[0403] A "health management plan" refers to specific guidelines and recommendations formulated to maintain or improve an individual's health.

[0404] "Emotional state" refers to an individual's emotional response and psychological state, and is inferred from information such as voice and facial expressions.

[0405] "Digital content" refers to content provided electronically, such as games and educational materials, that aims to improve knowledge and skills.

[0406] This invention aims to achieve individually optimized health management by comprehensively analyzing an individual's physiological data and emotional state. Users acquire physiological data such as heart rate and body temperature in real time using wearable devices. Common smartwatches and fitness trackers are used as these devices.

[0407] The terminal receives physiological data acquired from wearable devices and transmits it to a server using its communication function. The server processes the data and analyzes it using a generative AI model. For example, the AI ​​model can detect abnormal heart rates early and reflect this in a health management plan. The terminal also incorporates the user's voice and facial expression information into an emotion engine to determine the individual's emotional state. This emotion engine is implemented using general speech recognition and image processing technologies.

[0408] Based on the analysis results, the server generates a personalized health management plan for the user and presents it to the user via the terminal. The terminal then incorporates individualized support into the health management plan according to the user's emotional state. For example, if the user is feeling stressed, relaxation techniques will be recommended. Conversely, if the user is in a good emotional state, more challenging cognitive function improvement content will be presented.

[0409] A possible example of a specific prompt might be: "Please suggest some relaxation methods that would be recommended when the user's heart rate is higher than normal. Also, please provide example responses for when the user is feeling depressed." In this way, users can receive individually optimized support based on their current health and emotional state.

[0410] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0411] Step 1:

[0412] The user wears a wearable device. The wearable device records physiological data such as the user's heart rate, body temperature, and activity level in real time. The input physiological data is detected by sensors within the device and transmitted to the terminal in digital format. During this process, data is collected at regular intervals and stored in the terminal.

[0413] Step 2:

[0414] The terminal receives physiological data transmitted from the wearable device. The received data is standardized within the terminal and then sent to the server via the network. Here, the data is sent to the server via Wi-Fi or a mobile data network. The output is the organized physiological data that arrives at the server.

[0415] Step 3:

[0416] The server receives physiological data transmitted from the terminal. Based on this input data, an analysis is performed using an AI algorithm. The analysis detects anomalies and patterns in the data and evaluates the health status. As output, a health evaluation result is generated.

[0417] Step 4:

[0418] Based on the analysis results, the server uses a generated AI model to create a personalized health management plan for the user. This plan includes suggestions for lifestyle improvements, exercise, and dietary advice. After the program generates the plan, it is output and sent to the terminal.

[0419] Step 5:

[0420] The device receives a health management plan transmitted from the server. Simultaneously, the device uses an emotion engine to analyze the user's voice and facial expression data to determine their emotional state. Speech recognition and image analysis technologies are used in the emotion analysis, and the user's emotional state is estimated as output.

[0421] Step 6:

[0422] The device integrates the received health management plan and analyzed emotional state to provide individually optimized advice to the user. Based on the emotional state, recommended activities and conversations are selected and presented to the user. The output provides customized instructions and advice.

[0423] Step 7:

[0424] The user takes action based on advice from the device. For example, they might perform recommended exercises or review their diet. The device also collects feedback from the user regarding their progress and sends this data back to the server to be incorporated into the next health management plan. The output is new data for the next plan.

[0425] (Application Example 2)

[0426] 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."

[0427] In recent years, in an aging society, comprehensive support that takes into account not only an individual's physiological state but also their emotional state is needed to improve health management and quality of life. However, while conventional health management systems primarily focused on collecting and analyzing physiological information, systems that grasp emotional states in real time and reflect them in health management have not been adequately realized. As a result, there are challenges in providing optimal advice and dialogue tailored to each individual, as well as appropriate content to improve cognitive function.

[0428] 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.

[0429] In this invention, the server includes means for acquiring an individual's physiological information, means for generating a health management plan based on the analyzed physiological information, means for providing a health management plan based on the individual's emotional state using emotion analysis technology, and means for sharing the generated health management plan and analysis results with a third party. This enables more personalized health management by comprehensively considering an individual's physiological and emotional needs.

[0430] "Physiological information" refers to data that indicates an individual's physical condition, including vital signs such as heart rate, body temperature, and blood pressure, as well as activity levels.

[0431] "Emotional analysis technology" is a technology that identifies an individual's emotional state at a given time by analyzing data such as their voice, facial expressions, and behavior.

[0432] A "health management plan" is a specific plan that provides guidelines for maintaining and promoting an individual's health, based on analyzed physiological information and emotional state.

[0433] A "third party" refers to anyone other than the individual receiving health management services, such as medical professionals, caregivers, or family members, who are authorized to share information.

[0434] "Emotional state" refers to an individual's mental and psychological condition, which is expressed by emotional categories such as joy, anger, sadness, and pleasure.

[0435] The system for realizing this invention collects and analyzes an individual's physiological information and emotional state to provide a health management plan. The server continuously acquires physiological information from a wearable device. The acquired data is transmitted to the server via wireless communication. The server analyzes the received physiological information, generates a health management plan, and presents it to the terminal.

[0436] The device uses speech recognition APIs and facial recognition technology to analyze the user's voice and facial information using emotion analysis technology. The emotional state obtained from the analysis is sent to the server. Based on the analysis results, the server generates a health management plan and dialogue content that takes the emotional state into account, and presents them to the user individually.

[0437] For example, if a user says, "I'm tired today," the device will recognize the voice and offer advice such as, "Let's take a short break. I recommend 10 minutes of meditation." If the device detects that the user is feeling anxious based on their facial expression, it will notify them with a message like, "Have you been stressed lately? Please let us know if there's anything we can do to help."

[0438] The hardware used includes wearable devices (Fitbit and common smartwatches), smartphones, and smart glasses. For software, Google Speech-to-Text API is used for speech recognition, Microsoft Azure's emotion recognition API for sentiment analysis, and OpenCV for facial expression recognition.

[0439] An example of a prompt using a generative AI model is, "What activities are suitable for elderly people when they are relaxed?" This allows the system to provide information appropriate to the user's state.

[0440] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0441] Step 1:

[0442] The server receives physiological data from wearable devices. This data is transmitted in the form of heart rate, activity level, etc. The server analyzes this data and monitors the individual's health status in real time. The input is physiological information from the wearable device, and the output is analyzed health indicator data.

[0443] Step 2:

[0444] The device collects the user's voice using a microphone and converts it into text data using a speech recognition API. The device then inputs this text data into an emotion analysis engine to determine the user's emotional state. The input is audio data, and the output is the estimated emotional state.

[0445] Step 3:

[0446] The device uses a camera to capture the user's face and facial recognition software to collect the user's facial expression data. This data is sent to an emotion analysis engine to supplement the emotion. The input is facial image data, and the output is the supplemented emotional state.

[0447] Step 4:

[0448] The server integrates health analysis results based on physiological information with emotional analysis results to generate a customized health management plan. The inputs are health indicator data and emotional state data, and the output is a personalized health management plan.

[0449] Step 5:

[0450] The user receives a health management plan provided via their device. They can also receive suggestions in the form of dialogue, such as, "You seem tired today. Shall we take a short break?" The input is the user's health status and emotional state, and the output is the health management plan and dialogue content.

[0451] Step 6:

[0452] The server shares the generated health management plan and related information with third parties in a secure cloud environment. This allows caregivers and medical professionals to share necessary information. The input is health management plan data, and the output is the shared information.

[0453] 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.

[0454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] 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.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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".

[0469] This invention is a system that supports health management and aims to maintain and improve cognitive function by acquiring and analyzing an individual's physiological information. By wearing a wearable device, the user continuously acquires physiological information such as heart rate, blood pressure, and body temperature. This data is transmitted from the device to a server, which then analyzes the received physiological information.

[0470] The server generates an optimal health management plan based on the analysis results. This plan is provided to the user via their device, for example, as suggestions to adjust daily exercise levels or as notifications prompting them to visit a medical institution. The server can also share the analysis results with pre-registered family members and medical professionals as needed.

[0471] Furthermore, the device analyzes the user's conversation history and generates conversations on themes based on the user's personal interests and preferences. This reduces feelings of loneliness and allows users to enjoy communication. For example, for a user who enjoys talking about the weather, the device might offer a conversation such as, "It's a nice day today. Are you planning to go for a walk?"

[0472] Furthermore, the server analyzes the user's past gaming history and provides game content aimed at maintaining and improving cognitive function. The terminal periodically presents the user with content such as quizzes and puzzles and records the results. This allows users to activate their cognitive functions while having fun.

[0473] When a user issues a voice command, the device recognizes it and can operate home appliances accordingly. For example, it can recognize a voice command to turn on a light and automatically coordinate with a smart home system to execute the action.

[0474] Thus, this invention provides a comprehensive system that not only supports individual health management but also enhances daily life and enables rapid response in crisis management.

[0475] The following describes the processing flow.

[0476] Step 1:

[0477] The terminal acquires physiological information from the user's wearable device and stores it in a local database.

[0478] Step 2:

[0479] The terminal periodically connects to the server and transfers stored physiological information. The server receives this data and begins analysis.

[0480] Step 3:

[0481] The server uses an analysis algorithm to assess its health status and identify any abnormalities or areas that need improvement.

[0482] Step 4:

[0483] The server generates an optimal health management plan based on the analysis results and sends that plan to the terminal.

[0484] Step 5:

[0485] The device notifies the user of the generated health management plan and offers suggestions to encourage appropriate exercise and lifestyle improvements.

[0486] Step 6:

[0487] The server periodically shares the user's health data and analysis results with pre-registered third parties (family members or medical professionals).

[0488] Step 7:

[0489] The device references the user's past conversation history to determine a theme for generating a new conversation.

[0490] Step 8:

[0491] The server generates a dialogue script based on the user's interests and preferences and sends it back to the terminal.

[0492] Step 9:

[0493] The device conducts a voice conversation with the user based on a script, receives user responses, and adjusts the flow of the conversation accordingly.

[0494] Step 10:

[0495] The server analyzes the user's game history and designs new game content aimed at improving cognitive function.

[0496] Step 11:

[0497] The device provides users with designed game content and records the results of gameplay.

[0498] Step 12:

[0499] When a user issues a voice command, the device recognizes the command and sends instructions to operate the smart home device.

[0500] Step 13:

[0501] If the device detects an emergency voice keyword, it will communicate with the server to notify registered emergency contacts and call for necessary assistance.

[0502] (Example 1)

[0503] 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."

[0504] There is a growing need for systems that comprehensively support individual health management and quality of life improvement. This requires technologies that integrate continuous acquisition and analysis of physiological data, dialogue based on individual interests, maintenance and improvement of cognitive function, and even automation of the living environment. However, conventional systems cannot provide these functions comprehensively; they only address them as individual functions, resulting in a lack of user convenience and efficiency.

[0505] 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.

[0506] In this invention, the server includes means for acquiring personal physiological information, means for performing statistical analysis, and means for generating and notifying a health management plan. This enables comprehensive support for health management and improvement of the quality of daily life.

[0507] "Personal physiological information" refers to physical data that indicates an individual's health status, such as heart rate, blood pressure, and body temperature.

[0508] "Statistical analysis" refers to the computational processes and methods used to organize data and understand its characteristics.

[0509] A "health management plan" refers to a specific action plan proposed based on an individual's health condition, aimed at improving lifestyle habits such as exercise and diet.

[0510] "Notification" refers to the process of informing a user of information, and this includes methods such as using voice, text, and email.

[0511] "Third party" refers to all individuals or organizations other than the user themselves and the entity providing the system.

[0512] "Dialogue history" refers to informational data that records the content of past conversations, and analysis is performed based on this data.

[0513] "Analyzing interests" refers to the process of identifying the preferences and interests of the individual being analyzed.

[0514] "Entertainment content" refers to interactive content such as games and quizzes that are intended to provide user enjoyment and improve cognitive function.

[0515] "Voice commands" refer to instructions or commands that a user gives to a device via voice.

[0516] "Related equipment" refers to electrical appliances and digital devices that operate in conjunction with the system.

[0517] This invention is a comprehensive system that continuously manages an individual's health status and supports their daily life. The user wears a wearable device to acquire daily physiological information. Specifically, sensors measure heart rate, blood pressure, body temperature, etc. This data is transmitted to a server via a terminal.

[0518] The server stores the received physiological information in a database for statistical analysis and uses analysis tools (e.g., Python's Pandas or R). A health management plan is generated from the analysis results and notified to the user via push notifications or the application. By using a generative AI model, it is possible to dynamically design a plan tailored to the individual's health condition. For example, the user may be advised to do 30 minutes of aerobic exercise three days a week.

[0519] The device also analyzes past conversation history and uses natural language processing technology to explore the user's interests. Based on this, it generates and provides conversations that match the user's interests. For example, it might prompt a user interested in the weather with a conversation like, "It's sunny today, do you have any plans to go anywhere?" Possible prompts could include something like, "Design an AI system that analyzes user interests and generates appropriate conversations."

[0520] Furthermore, the server considers the user's learning and gaming history to select entertainment content that maintains and improves cognitive function. The device periodically presents this content to the user and records the results. For example, it might provide quizzes or puzzles once a week.

[0521] Users can also control devices in their home using voice commands. The device understands the user's instructions using a voice recognition engine (e.g., common voice recognition software) and operates the device in conjunction with the smart home system. A concrete example is a system that automatically turns on the lights in response to a command such as "Turn on the lights."

[0522] This system is designed not only to support individual health management but also to improve overall efficiency and comfort in daily life.

[0523] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0524] Step 1:

[0525] The user begins acquiring physiological information by wearing a wearable device. The device continuously measures data such as heart rate, blood pressure, and body temperature. This physiological information is temporarily stored in the device's internal memory.

[0526] Step 2:

[0527] The device acquires data from wearable devices using Bluetooth. The acquired physiological information is then transmitted to a server via a dedicated application. The transmitted information includes measured heart rate and blood pressure data.

[0528] Step 3:

[0529] The server stores physiological information received from the terminal in a database. The stored data is analyzed using statistical analysis tools. The main processes of the analysis include detecting anomalies in the biological data and analyzing trends. If an anomaly is detected as a result of the analysis, a warning flag is output.

[0530] Step 4:

[0531] The server generates a health management plan based on the analysis results. Using a generation AI model, it creates specific suggestions tailored to the user's health condition. For example, it might generate a plan recommending "30 minutes of walking per day." The generated plan is then sent from the server to the terminal.

[0532] Step 5:

[0533] The device notifies the user of received health management plans. Notifications are sent via push notifications and in-app messaging. The information is also shared with family members and healthcare professionals as needed.

[0534] Step 6:

[0535] The device records the user's conversation history and analyzes it using natural language processing technology. This analysis identifies the user's interests and preferences. Based on the results, it generates and provides conversation content tailored to the user. For example, for a user who likes talking about the weather, it might provide the conversation, "It's sunny today, do you have any plans to go out?"

[0536] Step 7:

[0537] The server analyzes past game history and selects game content to improve cognitive function. The selected games are periodically presented to the user from the device. The user's actions are sent back to the server and stored as history.

[0538] Step 8:

[0539] Users operate household devices by issuing voice commands. The terminal uses a voice recognition engine to analyze the user's voice instructions. Based on the analysis results, it interacts with smart home devices and performs actions according to the instructions. For example, it can automatically turn on the lights as instructed.

[0540] (Application Example 1)

[0541] 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."

[0542] There is a need for efficient and effective health management and cognitive function maintenance for elderly individuals and those with chronic illnesses. However, many current systems are limited to the collection and analysis of physiological information, and fail to provide appropriate feedback and communication to individuals. Furthermore, the integration of life support functions using voice commands has not progressed. The objective of this invention is to solve these problems and realize comprehensive health management and life support.

[0543] 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.

[0544] In this invention, the server includes means for collecting physiological information of an individual; means for receiving and analyzing the physiological information obtained from the means; means for generating a health management plan based on the analysis results and presenting it to the individual; means for providing cognitive training content to the individual based on the analysis results using a generated AI model; and means for recognizing the individual's voice commands and operating home appliances. This enables the provision of real-time, appropriate health management and cognitive function improvement feedback to elderly individuals and those with chronic illnesses, as well as improved convenience in daily life.

[0545] "Individual physiological information" refers to data related to life-sustaining activities such as heart rate, blood pressure, and body temperature, which are used to evaluate the health status of a specific individual.

[0546] A "health management plan" is a set of guidelines proposed to optimize daily lifestyle habits such as exercise, diet, and rest, based on analyzed physiological information.

[0547] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate personalized feedback and content from collected data.

[0548] "Cognitive training content" refers to content that promotes intellectual activities such as games and puzzles, with the aim of maintaining and improving an individual's cognitive function.

[0549] "Voice commands" are instructions given by an individual using words, which information devices analyze and use to perform actions according to those instructions.

[0550] "Home appliances" are electrical devices used in daily life that are operated by voice commands.

[0551] The system implementing this invention is an inclusive system for efficiently managing the health of elderly individuals and those with chronic illnesses. First, when an individual wears a wearable device, physiological information such as heart rate, blood pressure, and body temperature is acquired in real time. This data is transmitted to a server in the cloud via a terminal such as smart glasses.

[0552] The server utilizes a database and implements a generative AI model to analyze this physiological information. The analysis results generate a health management plan tailored to each individual. This plan provides daily activity guidelines to optimize the individual's health.

[0553] Furthermore, a generative AI model customizes cognitive training content based on the individual's past activity history and interests. The device periodically presents this training content to the individual, maintaining and improving their cognitive function.

[0554] Regarding voice commands, the device can recognize the user's voice and send the command to a cloud server, enabling optimal control of home appliances. For example, by giving a voice command such as "Turn off the lights" at night, all the lights in the house will automatically turn off.

[0555] Examples of specific prompts include, "Please tell me the resident's current health status," and "Based on Mr. / Ms. XX's cognitive game history, suggest the next game." This allows nursing home staff to manage health status in real time and provide appropriate care for each individual.

[0556] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0557] Step 1:

[0558] The user wears a wearable device. This device acquires physiological data such as heart rate, blood pressure, and body temperature in real time. The acquired data is transmitted to a device such as smart glasses via Bluetooth or Wi-Fi.

[0559] Input: User's physiological information

[0560] Output: Transmission of real-time physiological data

[0561] Step 2:

[0562] The device sends the received physiological data to a cloud server. On the cloud server, the data is stored in a database and prepared for analysis.

[0563] Input: Physiological data received from a wearable device.

[0564] Output: Data storage in the cloud

[0565] Step 3:

[0566] The server analyzes the received physiological data. A generative AI model is used for the analysis to detect data trends and anomalies.

[0567] Input: Physiological data stored on a cloud server

[0568] Output: Analysis results (e.g., current health risks and abnormalities)

[0569] Step 4:

[0570] Based on the analysis results, the server automatically generates a health management plan tailored to each user. The generated plan is then presented to the user via their terminal.

[0571] Input: Analysis results of physiological data

[0572] Output: User health management plan

[0573] Step 5:

[0574] The server customizes cognitive training content based on the user's past activity history and interests. It utilizes a generative AI model to suggest the most suitable games and puzzles for the user.

[0575] Input: User history data and proposed AI model

[0576] Output: Proposal of cognitive training content

[0577] Step 6:

[0578] The terminal recognizes the user's voice commands and sends them to the server. The server generates signals to control the home appliances and makes them perform actions according to the voice commands.

[0579] Input: User's voice command

[0580] Output: Home appliance control signals based on voice commands

[0581] Step 7:

[0582] The server is configured to allow the generated health management plan and cognitive training results to be shared with third parties such as family members and healthcare professionals.

[0583] Input: User consent and sharing settings

[0584] Output: Data sharing with third parties

[0585] 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.

[0586] This invention is a system that provides more personalized health management support by combining a user's physiological information and emotions. The user continuously acquires physiological information using a wearable device. This data from the device is transmitted to a server via a terminal. The server analyzes the received physiological information and generates a health management plan based on it.

[0587] Furthermore, this invention incorporates user voice and facial expression data and uses an emotion engine to understand the user's emotional state. This allows emotional factors to be incorporated into the health management plan, providing advice tailored to the user's mental state. For example, if a high level of stress is detected, the health management plan will include recommendations for relaxation techniques and light exercise.

[0588] The user's emotions, recognized by the emotion engine, are reflected in the dialogue. The device responds with themes and tones appropriate to the user's emotions, customizing the conversation individually. This allows the user to enjoy a more comfortable dialogue. For example, if the user appears depressed, the device might respond with something like, "Is there something bothering you? Shall we talk?"

[0589] Furthermore, the game content is individually adjusted to improve cognitive function by utilizing information from the emotion engine. When the user is relaxed, slightly more challenging game content is provided, while when concentration is required, simpler content that can be progressed smoothly is offered.

[0590] A device that recognizes a user's voice commands can not only operate smart home devices, but also take into account the user's emotional state and make suggestions such as, "You seem tired today. Shall we dim the lights a little?"

[0591] This invention provides users with support that comprehensively considers their physiological and emotional needs, creating an environment that allows them to live a safe and comfortable life.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device retrieves daily physiological information (heart rate, blood pressure, body temperature, etc.) from the user's wearable device and stores it in a database.

[0595] Step 2:

[0596] The device acquires physiological information along with data such as the user's voice and facial expressions. The device uses an emotion engine to analyze this data and recognize the user's emotional state.

[0597] Step 3:

[0598] The device sends acquired physiological information and recognized emotional data to the server. The server analyzes this data and executes an algorithm to comprehensively evaluate the user's health and emotions.

[0599] Step 4:

[0600] The server generates a personalized health management plan based on the analyzed health status and emotions. This plan includes suggestions for exercise and relaxation methods that take into account not only physical health but also mental health.

[0601] Step 5:

[0602] The device presents the generated health management plan to the user and notifies them via voice and screen display to ensure they follow it properly.

[0603] Step 6:

[0604] The device analyzes the user's past conversation history and prepares appropriate conversation topics based on the user's emotional state.

[0605] Step 7:

[0606] The server generates appropriate dialogue content and sends it to the terminal, which then initiates a conversation with the user based on this content. During the conversation, the terminal dynamically adjusts the dialogue content in response to changes in the user's emotions.

[0607] Step 8:

[0608] The server designs game content based on the user's emotions and learning history, and delivers it to the user through the device. The device sends new data obtained from the user playing the game to the server, which is then used to inform the next learning cycle.

[0609] Step 9:

[0610] When a user uses voice commands, the device recognizes them and operates smart home devices. It also makes suggestions based on the user's emotional state, such as, "I'll play music that's perfect for your current mood."

[0611] Thus, the present invention comprehensively utilizes the user's physiological data and emotional information to provide individually optimized health management and lifestyle support.

[0612] (Example 2)

[0613] 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."

[0614] In health management, it is necessary to consider not only physiological data but also emotional factors and provide individualized support. However, conventional systems do not integrate physiological information analysis and emotional analysis, making it difficult to provide comprehensive support for individual needs. Furthermore, there is insufficient provision of measures to improve cognitive function using this information. Against this backdrop, there is a need for a system that comprehensively analyzes physiological and emotional information and provides support optimized for the individual.

[0615] 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.

[0616] In this invention, the server includes equipment for collecting physiological data of an individual, means for constructing and presenting a health management plan tailored to the individual based on the data analysis results, and means for providing individualized support based on emotional state and the health management plan. This enables health support that comprehensively considers the physiological and emotional needs of the individual.

[0617] "Physiological data" refers to information that indicates an individual's physical functions and condition, including heart rate, body temperature, and activity level.

[0618] "Equipment" refers to a physical or electronic device used to collect, transmit, or receive data.

[0619] "Data processing" refers to activities that involve technical operations to analyze received information and derive appropriate conclusions or judgments.

[0620] A "health management plan" refers to specific guidelines and recommendations formulated to maintain or improve an individual's health.

[0621] "Emotional state" refers to an individual's emotional response and psychological state, and is inferred from information such as voice and facial expressions.

[0622] "Digital content" refers to content provided electronically, such as games and educational materials, that aims to improve knowledge and skills.

[0623] This invention aims to achieve individually optimized health management by comprehensively analyzing an individual's physiological data and emotional state. Users acquire physiological data such as heart rate and body temperature in real time using wearable devices. Common smartwatches and fitness trackers are used as these devices.

[0624] The terminal receives physiological data acquired from wearable devices and transmits it to a server using its communication function. The server processes the data and analyzes it using a generative AI model. For example, the AI ​​model can detect abnormal heart rates early and reflect this in a health management plan. The terminal also incorporates the user's voice and facial expression information into an emotion engine to determine the individual's emotional state. This emotion engine is implemented using general speech recognition and image processing technologies.

[0625] Based on the analysis results, the server generates a personalized health management plan for the user and presents it to the user via the terminal. The terminal then incorporates individualized support into the health management plan according to the user's emotional state. For example, if the user is feeling stressed, relaxation techniques will be recommended. Conversely, if the user is in a good emotional state, more challenging cognitive function improvement content will be presented.

[0626] A possible example of a specific prompt might be: "Please suggest some relaxation methods that would be recommended when the user's heart rate is higher than normal. Also, please provide example responses for when the user is feeling depressed." In this way, users can receive individually optimized support based on their current health and emotional state.

[0627] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0628] Step 1:

[0629] The user wears a wearable device. The wearable device records physiological data such as the user's heart rate, body temperature, and activity level in real time. The input physiological data is detected by sensors within the device and transmitted to the terminal in digital format. During this process, data is collected at regular intervals and stored in the terminal.

[0630] Step 2:

[0631] The terminal receives physiological data transmitted from the wearable device. The received data is standardized within the terminal and then sent to the server via the network. Here, the data is sent to the server via Wi-Fi or a mobile data network. The output is the organized physiological data that arrives at the server.

[0632] Step 3:

[0633] The server receives physiological data transmitted from the terminal. Based on this input data, an analysis is performed using an AI algorithm. The analysis detects anomalies and patterns in the data and evaluates the health status. As output, a health evaluation result is generated.

[0634] Step 4:

[0635] Based on the analysis results, the server uses a generated AI model to create a personalized health management plan for the user. This plan includes suggestions for lifestyle improvements, exercise, and dietary advice. After the program generates the plan, it is output and sent to the terminal.

[0636] Step 5:

[0637] The device receives a health management plan transmitted from the server. Simultaneously, the device uses an emotion engine to analyze the user's voice and facial expression data to determine their emotional state. Speech recognition and image analysis technologies are used in the emotion analysis, and the user's emotional state is estimated as output.

[0638] Step 6:

[0639] The device integrates the received health management plan and analyzed emotional state to provide individually optimized advice to the user. Based on the emotional state, recommended activities and conversations are selected and presented to the user. The output provides customized instructions and advice.

[0640] Step 7:

[0641] The user takes action based on advice from the device. For example, they might perform recommended exercises or review their diet. The device also collects feedback from the user regarding their progress and sends this data back to the server to be incorporated into the next health management plan. The output is new data for the next plan.

[0642] (Application Example 2)

[0643] 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."

[0644] In recent years, in an aging society, comprehensive support that takes into account not only an individual's physiological state but also their emotional state is needed to improve health management and quality of life. However, while conventional health management systems primarily focused on collecting and analyzing physiological information, systems that grasp emotional states in real time and reflect them in health management have not been adequately realized. As a result, there are challenges in providing optimal advice and dialogue tailored to each individual, as well as appropriate content to improve cognitive function.

[0645] 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.

[0646] In this invention, the server includes means for acquiring an individual's physiological information, means for generating a health management plan based on the analyzed physiological information, means for providing a health management plan based on the individual's emotional state using emotion analysis technology, and means for sharing the generated health management plan and analysis results with a third party. This enables more personalized health management by comprehensively considering an individual's physiological and emotional needs.

[0647] "Physiological information" refers to data that indicates an individual's physical condition, including vital signs such as heart rate, body temperature, and blood pressure, as well as activity levels.

[0648] "Emotional analysis technology" is a technology that identifies an individual's emotional state at a given time by analyzing data such as their voice, facial expressions, and behavior.

[0649] A "health management plan" is a specific plan that provides guidelines for maintaining and promoting an individual's health, based on analyzed physiological information and emotional state.

[0650] A "third party" refers to anyone other than the individual receiving health management services, such as medical professionals, caregivers, or family members, who are authorized to share information.

[0651] "Emotional state" refers to an individual's mental and psychological condition, which is expressed by emotional categories such as joy, anger, sadness, and pleasure.

[0652] The system for realizing this invention collects and analyzes an individual's physiological information and emotional state to provide a health management plan. The server continuously acquires physiological information from a wearable device. The acquired data is transmitted to the server via wireless communication. The server analyzes the received physiological information, generates a health management plan, and presents it to the terminal.

[0653] The device uses speech recognition APIs and facial recognition technology to analyze the user's voice and facial information using emotion analysis technology. The emotional state obtained from the analysis is sent to the server. Based on the analysis results, the server generates a health management plan and dialogue content that takes the emotional state into account, and presents them to the user individually.

[0654] For example, if a user says, "I'm tired today," the device will recognize the voice and offer advice such as, "Let's take a short break. I recommend 10 minutes of meditation." If the device detects that the user is feeling anxious based on their facial expression, it will notify them with a message like, "Have you been stressed lately? Please let us know if there's anything we can do to help."

[0655] The hardware used includes wearable devices (Fitbit and common smartwatches), smartphones, and smart glasses. For software, Google Speech-to-Text API is used for speech recognition, Microsoft Azure's emotion recognition API for sentiment analysis, and OpenCV for facial expression recognition.

[0656] An example of a prompt using a generative AI model is, "What activities are suitable for elderly people when they are relaxed?" This allows the system to provide information appropriate to the user's state.

[0657] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0658] Step 1:

[0659] The server receives physiological data from wearable devices. This data is transmitted in the form of heart rate, activity level, etc. The server analyzes this data and monitors the individual's health status in real time. The input is physiological information from the wearable device, and the output is analyzed health indicator data.

[0660] Step 2:

[0661] The device collects the user's voice using a microphone and converts it into text data using a speech recognition API. The device then inputs this text data into an emotion analysis engine to determine the user's emotional state. The input is audio data, and the output is the estimated emotional state.

[0662] Step 3:

[0663] The device uses a camera to capture the user's face and facial recognition software to collect the user's facial expression data. This data is sent to an emotion analysis engine to supplement the emotion. The input is facial image data, and the output is the supplemented emotional state.

[0664] Step 4:

[0665] The server integrates health analysis results based on physiological information with emotional analysis results to generate a customized health management plan. The inputs are health indicator data and emotional state data, and the output is a personalized health management plan.

[0666] Step 5:

[0667] The user receives a health management plan provided via their device. They can also receive suggestions in the form of dialogue, such as, "You seem tired today. Shall we take a short break?" The input is the user's health status and emotional state, and the output is the health management plan and dialogue content.

[0668] Step 6:

[0669] The server shares the generated health management plan and related information with third parties in a secure cloud environment. This allows caregivers and medical professionals to share necessary information. The input is health management plan data, and the output is the shared information.

[0670] 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.

[0671] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] 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.

[0673] [Fourth Embodiment]

[0674] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0675] 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.

[0676] 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).

[0677] 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.

[0678] 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.

[0679] 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).

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] 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".

[0687] This invention is a system that supports health management and aims to maintain and improve cognitive function by acquiring and analyzing an individual's physiological information. By wearing a wearable device, the user continuously acquires physiological information such as heart rate, blood pressure, and body temperature. This data is transmitted from the device to a server, which then analyzes the received physiological information.

[0688] The server generates an optimal health management plan based on the analysis results. This plan is provided to the user via their device, for example, as suggestions to adjust daily exercise levels or as notifications prompting them to visit a medical institution. The server can also share the analysis results with pre-registered family members and medical professionals as needed.

[0689] Furthermore, the device analyzes the user's conversation history and generates conversations on themes based on the user's personal interests and preferences. This reduces feelings of loneliness and allows users to enjoy communication. For example, for a user who enjoys talking about the weather, the device might offer a conversation such as, "It's a nice day today. Are you planning to go for a walk?"

[0690] Furthermore, the server analyzes the user's past gaming history and provides game content aimed at maintaining and improving cognitive function. The terminal periodically presents the user with content such as quizzes and puzzles and records the results. This allows users to activate their cognitive functions while having fun.

[0691] When a user issues a voice command, the device recognizes it and can operate home appliances accordingly. For example, it can recognize a voice command to turn on a light and automatically coordinate with a smart home system to execute the action.

[0692] Thus, this invention provides a comprehensive system that not only supports individual health management but also enhances daily life and enables rapid response in crisis management.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The terminal acquires physiological information from the user's wearable device and stores it in a local database.

[0696] Step 2:

[0697] The terminal periodically connects to the server and transfers stored physiological information. The server receives this data and begins analysis.

[0698] Step 3:

[0699] The server uses an analysis algorithm to assess its health status and identify any abnormalities or areas that need improvement.

[0700] Step 4:

[0701] The server generates an optimal health management plan based on the analysis results and sends that plan to the terminal.

[0702] Step 5:

[0703] The device notifies the user of the generated health management plan and offers suggestions to encourage appropriate exercise and lifestyle improvements.

[0704] Step 6:

[0705] The server periodically shares the user's health data and analysis results with pre-registered third parties (family members or medical professionals).

[0706] Step 7:

[0707] The device references the user's past conversation history to determine a theme for generating a new conversation.

[0708] Step 8:

[0709] The server generates a dialogue script based on the user's interests and preferences and sends it back to the terminal.

[0710] Step 9:

[0711] The device conducts a voice conversation with the user based on a script, receives user responses, and adjusts the flow of the conversation accordingly.

[0712] Step 10:

[0713] The server analyzes the user's game history and designs new game content aimed at improving cognitive function.

[0714] Step 11:

[0715] The device provides users with designed game content and records the results of gameplay.

[0716] Step 12:

[0717] When a user issues a voice command, the device recognizes the command and sends instructions to operate the smart home device.

[0718] Step 13:

[0719] If the device detects an emergency voice keyword, it will communicate with the server to notify registered emergency contacts and call for necessary assistance.

[0720] (Example 1)

[0721] 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".

[0722] There is a growing need for systems that comprehensively support individual health management and quality of life improvement. This requires technologies that integrate continuous acquisition and analysis of physiological data, dialogue based on individual interests, maintenance and improvement of cognitive function, and even automation of the living environment. However, conventional systems cannot provide these functions comprehensively; they only address them as individual functions, resulting in a lack of user convenience and efficiency.

[0723] 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.

[0724] In this invention, the server includes means for acquiring personal physiological information, means for performing statistical analysis, and means for generating and notifying a health management plan. This enables comprehensive support for health management and improvement of the quality of daily life.

[0725] "Personal physiological information" refers to physical data that indicates an individual's health status, such as heart rate, blood pressure, and body temperature.

[0726] "Statistical analysis" refers to the computational processes and methods used to organize data and understand its characteristics.

[0727] A "health management plan" refers to a specific action plan proposed based on an individual's health condition, aimed at improving lifestyle habits such as exercise and diet.

[0728] "Notification" refers to the process of informing a user of information, and this includes methods such as using voice, text, and email.

[0729] "Third party" refers to all individuals or organizations other than the user themselves and the entity providing the system.

[0730] "Dialogue history" refers to informational data that records the content of past conversations, and analysis is performed based on this data.

[0731] "Analyzing interests" refers to the process of identifying the preferences and interests of the individual being analyzed.

[0732] "Entertainment content" refers to interactive content such as games and quizzes that are intended to provide user enjoyment and improve cognitive function.

[0733] "Voice commands" refer to instructions or commands that a user gives to a device via voice.

[0734] "Related equipment" refers to electrical appliances and digital devices that operate in conjunction with the system.

[0735] This invention is a comprehensive system that continuously manages an individual's health status and supports their daily life. The user wears a wearable device to acquire daily physiological information. Specifically, sensors measure heart rate, blood pressure, body temperature, etc. This data is transmitted to a server via a terminal.

[0736] The server stores the received physiological information in a database for statistical analysis and uses analysis tools (e.g., Python's Pandas or R). A health management plan is generated from the analysis results and notified to the user via push notifications or the application. By using a generative AI model, it is possible to dynamically design a plan tailored to the individual's health condition. For example, the user may be advised to do 30 minutes of aerobic exercise three days a week.

[0737] The device also analyzes past conversation history and uses natural language processing technology to explore the user's interests. Based on this, it generates and provides conversations that match the user's interests. For example, it might prompt a user interested in the weather with a conversation like, "It's sunny today, do you have any plans to go anywhere?" Possible prompts could include something like, "Design an AI system that analyzes user interests and generates appropriate conversations."

[0738] Furthermore, the server considers the user's learning and gaming history to select entertainment content that maintains and improves cognitive function. The device periodically presents this content to the user and records the results. For example, it might provide quizzes or puzzles once a week.

[0739] Users can also control devices in their home using voice commands. The device understands the user's instructions using a voice recognition engine (e.g., common voice recognition software) and operates the device in conjunction with the smart home system. A concrete example is a system that automatically turns on the lights in response to a command such as "Turn on the lights."

[0740] This system is designed not only to support individual health management but also to improve overall efficiency and comfort in daily life.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1:

[0743] The user begins acquiring physiological information by wearing a wearable device. The device continuously measures data such as heart rate, blood pressure, and body temperature. This physiological information is temporarily stored in the device's internal memory.

[0744] Step 2:

[0745] The device acquires data from wearable devices using Bluetooth. The acquired physiological information is then transmitted to a server via a dedicated application. The transmitted information includes measured heart rate and blood pressure data.

[0746] Step 3:

[0747] The server stores physiological information received from the terminal in a database. The stored data is analyzed using statistical analysis tools. The main processes of the analysis include detecting anomalies in the biological data and analyzing trends. If an anomaly is detected as a result of the analysis, a warning flag is output.

[0748] Step 4:

[0749] The server generates a health management plan based on the analysis results. Using a generation AI model, it creates specific suggestions tailored to the user's health condition. For example, it might generate a plan recommending "30 minutes of walking per day." The generated plan is then sent from the server to the terminal.

[0750] Step 5:

[0751] The device notifies the user of received health management plans. Notifications are sent via push notifications and in-app messaging. The information is also shared with family members and healthcare professionals as needed.

[0752] Step 6:

[0753] The device records the user's conversation history and analyzes it using natural language processing technology. This analysis identifies the user's interests and preferences. Based on the results, it generates and provides conversation content tailored to the user. For example, for a user who likes talking about the weather, it might provide the conversation, "It's sunny today, do you have any plans to go out?"

[0754] Step 7:

[0755] The server analyzes past game history and selects game content to improve cognitive function. The selected games are periodically presented to the user from the device. The user's actions are sent back to the server and stored as history.

[0756] Step 8:

[0757] Users operate household devices by issuing voice commands. The terminal uses a voice recognition engine to analyze the user's voice instructions. Based on the analysis results, it interacts with smart home devices and performs actions according to the instructions. For example, it can automatically turn on the lights as instructed.

[0758] (Application Example 1)

[0759] 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".

[0760] There is a need for efficient and effective health management and cognitive function maintenance for elderly individuals and those with chronic illnesses. However, many current systems are limited to the collection and analysis of physiological information, and fail to provide appropriate feedback and communication to individuals. Furthermore, the integration of life support functions using voice commands has not progressed. The objective of this invention is to solve these problems and realize comprehensive health management and life support.

[0761] 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.

[0762] In this invention, the server includes means for collecting physiological information of an individual; means for receiving and analyzing the physiological information obtained from the means; means for generating a health management plan based on the analysis results and presenting it to the individual; means for providing cognitive training content to the individual based on the analysis results using a generated AI model; and means for recognizing the individual's voice commands and operating home appliances. This enables the provision of real-time, appropriate health management and cognitive function improvement feedback to elderly individuals and those with chronic illnesses, as well as improved convenience in daily life.

[0763] "Individual physiological information" refers to data related to life-sustaining activities such as heart rate, blood pressure, and body temperature, which are used to evaluate the health status of a specific individual.

[0764] A "health management plan" is a set of guidelines proposed to optimize daily lifestyle habits such as exercise, diet, and rest, based on analyzed physiological information.

[0765] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate personalized feedback and content from collected data.

[0766] "Cognitive training content" refers to content that promotes intellectual activities such as games and puzzles, with the aim of maintaining and improving an individual's cognitive function.

[0767] "Voice commands" are instructions given by an individual using words, which information devices analyze and use to perform actions according to those instructions.

[0768] "Home appliances" are electrical devices used in daily life that are operated by voice commands.

[0769] The system implementing this invention is an inclusive system for efficiently managing the health of elderly individuals and those with chronic illnesses. First, when an individual wears a wearable device, physiological information such as heart rate, blood pressure, and body temperature is acquired in real time. This data is transmitted to a server in the cloud via a terminal such as smart glasses.

[0770] The server utilizes a database and implements a generative AI model to analyze this physiological information. The analysis results generate a health management plan tailored to each individual. This plan provides daily activity guidelines to optimize the individual's health.

[0771] Furthermore, a generative AI model customizes cognitive training content based on the individual's past activity history and interests. The device periodically presents this training content to the individual, maintaining and improving their cognitive function.

[0772] Regarding voice commands, the device can recognize the user's voice and send the command to a cloud server, enabling optimal control of home appliances. For example, by giving a voice command such as "Turn off the lights" at night, all the lights in the house will automatically turn off.

[0773] Examples of specific prompts include, "Please tell me the resident's current health status," and "Based on Mr. / Ms. XX's cognitive game history, suggest the next game." This allows nursing home staff to manage health status in real time and provide appropriate care for each individual.

[0774] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0775] Step 1:

[0776] The user wears a wearable device. This device acquires physiological data such as heart rate, blood pressure, and body temperature in real time. The acquired data is transmitted to a device such as smart glasses via Bluetooth or Wi-Fi.

[0777] Input: User's physiological information

[0778] Output: Transmission of real-time physiological data

[0779] Step 2:

[0780] The device sends the received physiological data to a cloud server. On the cloud server, the data is stored in a database and prepared for analysis.

[0781] Input: Physiological data received from a wearable device.

[0782] Output: Data storage in the cloud

[0783] Step 3:

[0784] The server analyzes the received physiological data. A generative AI model is used for the analysis to detect data trends and anomalies.

[0785] Input: Physiological data stored on a cloud server

[0786] Output: Analysis results (e.g., current health risks and abnormalities)

[0787] Step 4:

[0788] Based on the analysis results, the server automatically generates a health management plan tailored to each user. The generated plan is then presented to the user via their terminal.

[0789] Input: Analysis results of physiological data

[0790] Output: User health management plan

[0791] Step 5:

[0792] The server customizes cognitive training content based on the user's past activity history and interests. It utilizes a generative AI model to suggest the most suitable games and puzzles for the user.

[0793] Input: User history data and proposed AI model

[0794] Output: Proposal of cognitive training content

[0795] Step 6:

[0796] The terminal recognizes the user's voice commands and sends them to the server. The server generates signals to control the home appliances and makes them perform actions according to the voice commands.

[0797] Input: User's voice command

[0798] Output: Home appliance control signals based on voice commands

[0799] Step 7:

[0800] The server is configured to allow the generated health management plan and cognitive training results to be shared with third parties such as family members and healthcare professionals.

[0801] Input: User consent and sharing settings

[0802] Output: Data sharing with third parties

[0803] 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.

[0804] This invention is a system that provides more personalized health management support by combining a user's physiological information and emotions. The user continuously acquires physiological information using a wearable device. This data from the device is transmitted to a server via a terminal. The server analyzes the received physiological information and generates a health management plan based on it.

[0805] Furthermore, this invention incorporates user voice and facial expression data and uses an emotion engine to understand the user's emotional state. This allows emotional factors to be incorporated into the health management plan, providing advice tailored to the user's mental state. For example, if a high level of stress is detected, the health management plan will include recommendations for relaxation techniques and light exercise.

[0806] The user's emotions, recognized by the emotion engine, are reflected in the dialogue. The device responds with themes and tones appropriate to the user's emotions, customizing the conversation individually. This allows the user to enjoy a more comfortable dialogue. For example, if the user appears depressed, the device might respond with something like, "Is there something bothering you? Shall we talk?"

[0807] Furthermore, the game content is individually adjusted to improve cognitive function by utilizing information from the emotion engine. When the user is relaxed, slightly more challenging game content is provided, while when concentration is required, simpler content that can be progressed smoothly is offered.

[0808] A device that recognizes a user's voice commands can not only operate smart home devices, but also take into account the user's emotional state and make suggestions such as, "You seem tired today. Shall we dim the lights a little?"

[0809] This invention provides users with support that comprehensively considers their physiological and emotional needs, creating an environment that allows them to live a safe and comfortable life.

[0810] The following describes the processing flow.

[0811] Step 1:

[0812] The device retrieves daily physiological information (heart rate, blood pressure, body temperature, etc.) from the user's wearable device and stores it in a database.

[0813] Step 2:

[0814] The device acquires physiological information along with data such as the user's voice and facial expressions. The device uses an emotion engine to analyze this data and recognize the user's emotional state.

[0815] Step 3:

[0816] The device sends acquired physiological information and recognized emotional data to the server. The server analyzes this data and executes an algorithm to comprehensively evaluate the user's health and emotions.

[0817] Step 4:

[0818] The server generates a personalized health management plan based on the analyzed health status and emotions. This plan includes suggestions for exercise and relaxation methods that take into account not only physical health but also mental health.

[0819] Step 5:

[0820] The device presents the generated health management plan to the user and notifies them via voice and screen display to ensure they follow it properly.

[0821] Step 6:

[0822] The device analyzes the user's past conversation history and prepares appropriate conversation topics based on the user's emotional state.

[0823] Step 7:

[0824] The server generates appropriate dialogue content and sends it to the terminal, which then initiates a conversation with the user based on this content. During the conversation, the terminal dynamically adjusts the dialogue content in response to changes in the user's emotions.

[0825] Step 8:

[0826] The server designs game content based on the user's emotions and learning history, and delivers it to the user through the device. The device sends new data obtained from the user playing the game to the server, which is then used to inform the next learning cycle.

[0827] Step 9:

[0828] When a user uses voice commands, the device recognizes them and operates smart home devices. It also makes suggestions based on the user's emotional state, such as, "I'll play music that's perfect for your current mood."

[0829] Thus, the present invention comprehensively utilizes the user's physiological data and emotional information to provide individually optimized health management and lifestyle support.

[0830] (Example 2)

[0831] 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".

[0832] In health management, it is necessary to consider not only physiological data but also emotional factors and provide individualized support. However, conventional systems do not integrate physiological information analysis and emotional analysis, making it difficult to provide comprehensive support for individual needs. Furthermore, there is insufficient provision of measures to improve cognitive function using this information. Against this backdrop, there is a need for a system that comprehensively analyzes physiological and emotional information and provides support optimized for the individual.

[0833] 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.

[0834] In this invention, the server includes equipment for collecting physiological data of an individual, means for constructing and presenting a health management plan tailored to the individual based on the data analysis results, and means for providing individualized support based on emotional state and the health management plan. This enables health support that comprehensively considers the physiological and emotional needs of the individual.

[0835] "Physiological data" refers to information that indicates an individual's physical functions and condition, including heart rate, body temperature, and activity level.

[0836] "Equipment" refers to a physical or electronic device used to collect, transmit, or receive data.

[0837] "Data processing" refers to activities that involve technical operations to analyze received information and derive appropriate conclusions or judgments.

[0838] A "health management plan" refers to specific guidelines and recommendations formulated to maintain or improve an individual's health.

[0839] "Emotional state" refers to an individual's emotional response and psychological state, and is inferred from information such as voice and facial expressions.

[0840] "Digital content" refers to content provided electronically, such as games and educational materials, that aims to improve knowledge and skills.

[0841] This invention aims to achieve individually optimized health management by comprehensively analyzing an individual's physiological data and emotional state. Users acquire physiological data such as heart rate and body temperature in real time using wearable devices. Common smartwatches and fitness trackers are used as these devices.

[0842] The terminal receives physiological data acquired from wearable devices and transmits it to a server using its communication function. The server processes the data and analyzes it using a generative AI model. For example, the AI ​​model can detect abnormal heart rates early and reflect this in a health management plan. The terminal also incorporates the user's voice and facial expression information into an emotion engine to determine the individual's emotional state. This emotion engine is implemented using general speech recognition and image processing technologies.

[0843] Based on the analysis results, the server generates a personalized health management plan for the user and presents it to the user via the terminal. The terminal then incorporates individualized support into the health management plan according to the user's emotional state. For example, if the user is feeling stressed, relaxation techniques will be recommended. Conversely, if the user is in a good emotional state, more challenging cognitive function improvement content will be presented.

[0844] A possible example of a specific prompt might be: "Please suggest some relaxation methods that would be recommended when the user's heart rate is higher than normal. Also, please provide example responses for when the user is feeling depressed." In this way, users can receive individually optimized support based on their current health and emotional state.

[0845] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0846] Step 1:

[0847] The user wears a wearable device. The wearable device records physiological data such as the user's heart rate, body temperature, and activity level in real time. The input physiological data is detected by sensors within the device and transmitted to the terminal in digital format. During this process, data is collected at regular intervals and stored in the terminal.

[0848] Step 2:

[0849] The terminal receives physiological data transmitted from the wearable device. The received data is standardized within the terminal and then sent to the server via the network. Here, the data is sent to the server via Wi-Fi or a mobile data network. The output is the organized physiological data that arrives at the server.

[0850] Step 3:

[0851] The server receives physiological data transmitted from the terminal. Based on this input data, an analysis is performed using an AI algorithm. The analysis detects anomalies and patterns in the data and evaluates the health status. As output, a health evaluation result is generated.

[0852] Step 4:

[0853] Based on the analysis results, the server uses a generated AI model to create a personalized health management plan for the user. This plan includes suggestions for lifestyle improvements, exercise, and dietary advice. After the program generates the plan, it is output and sent to the terminal.

[0854] Step 5:

[0855] The device receives a health management plan transmitted from the server. Simultaneously, the device uses an emotion engine to analyze the user's voice and facial expression data to determine their emotional state. Speech recognition and image analysis technologies are used in the emotion analysis, and the user's emotional state is estimated as output.

[0856] Step 6:

[0857] The device integrates the received health management plan and analyzed emotional state to provide individually optimized advice to the user. Based on the emotional state, recommended activities and conversations are selected and presented to the user. The output provides customized instructions and advice.

[0858] Step 7:

[0859] The user takes action based on advice from the device. For example, they might perform recommended exercises or review their diet. The device also collects feedback from the user regarding their progress and sends this data back to the server to be incorporated into the next health management plan. The output is new data for the next plan.

[0860] (Application Example 2)

[0861] 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".

[0862] In recent years, in an aging society, comprehensive support that takes into account not only an individual's physiological state but also their emotional state is needed to improve health management and quality of life. However, while conventional health management systems primarily focused on collecting and analyzing physiological information, systems that grasp emotional states in real time and reflect them in health management have not been adequately realized. As a result, there are challenges in providing optimal advice and dialogue tailored to each individual, as well as appropriate content to improve cognitive function.

[0863] 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.

[0864] In this invention, the server includes means for acquiring an individual's physiological information, means for generating a health management plan based on the analyzed physiological information, means for providing a health management plan based on the individual's emotional state using emotion analysis technology, and means for sharing the generated health management plan and analysis results with a third party. This enables more personalized health management by comprehensively considering an individual's physiological and emotional needs.

[0865] "Physiological information" refers to data that indicates an individual's physical condition, including vital signs such as heart rate, body temperature, and blood pressure, as well as activity levels.

[0866] "Emotional analysis technology" is a technology that identifies an individual's emotional state at a given time by analyzing data such as their voice, facial expressions, and behavior.

[0867] A "health management plan" is a specific plan that provides guidelines for maintaining and promoting an individual's health, based on analyzed physiological information and emotional state.

[0868] A "third party" refers to anyone other than the individual receiving health management services, such as medical professionals, caregivers, or family members, who are authorized to share information.

[0869] "Emotional state" refers to an individual's mental and psychological condition, which is expressed by emotional categories such as joy, anger, sadness, and pleasure.

[0870] The system for realizing this invention collects and analyzes an individual's physiological information and emotional state to provide a health management plan. The server continuously acquires physiological information from a wearable device. The acquired data is transmitted to the server via wireless communication. The server analyzes the received physiological information, generates a health management plan, and presents it to the terminal.

[0871] The device uses speech recognition APIs and facial recognition technology to analyze the user's voice and facial information using emotion analysis technology. The emotional state obtained from the analysis is sent to the server. Based on the analysis results, the server generates a health management plan and dialogue content that takes the emotional state into account, and presents them to the user individually.

[0872] For example, if a user says, "I'm tired today," the device will recognize the voice and offer advice such as, "Let's take a short break. I recommend 10 minutes of meditation." If the device detects that the user is feeling anxious based on their facial expression, it will notify them with a message like, "Have you been stressed lately? Please let us know if there's anything we can do to help."

[0873] The hardware used includes wearable devices (Fitbit and common smartwatches), smartphones, and smart glasses. For software, Google Speech-to-Text API is used for speech recognition, Microsoft Azure's emotion recognition API for sentiment analysis, and OpenCV for facial expression recognition.

[0874] An example of a prompt using a generative AI model is, "What activities are suitable for elderly people when they are relaxed?" This allows the system to provide information appropriate to the user's state.

[0875] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0876] Step 1:

[0877] The server receives physiological data from wearable devices. This data is transmitted in the form of heart rate, activity level, etc. The server analyzes this data and monitors the individual's health status in real time. The input is physiological information from the wearable device, and the output is analyzed health indicator data.

[0878] Step 2:

[0879] The device collects the user's voice using a microphone and converts it into text data using a speech recognition API. The device then inputs this text data into an emotion analysis engine to determine the user's emotional state. The input is audio data, and the output is the estimated emotional state.

[0880] Step 3:

[0881] The device uses a camera to capture the user's face and facial recognition software to collect the user's facial expression data. This data is sent to an emotion analysis engine to supplement the emotion. The input is facial image data, and the output is the supplemented emotional state.

[0882] Step 4:

[0883] The server integrates health analysis results based on physiological information with emotional analysis results to generate a customized health management plan. The inputs are health indicator data and emotional state data, and the output is a personalized health management plan.

[0884] Step 5:

[0885] The user receives a health management plan provided via their device. They can also receive suggestions in the form of dialogue, such as, "You seem tired today. Shall we take a short break?" The input is the user's health status and emotional state, and the output is the health management plan and dialogue content.

[0886] Step 6:

[0887] The server shares the generated health management plan and related information with third parties in a secure cloud environment. This allows caregivers and medical professionals to share necessary information. The input is health management plan data, and the output is the shared information.

[0888] 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.

[0889] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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."

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] The following is further disclosed regarding the embodiments described above.

[0910] (Claim 1)

[0911] A device for acquiring personal physiological information,

[0912] A device that receives and analyzes physiological information acquired from the aforementioned device,

[0913] A device that generates a health management plan based on the analyzed results and presents it to the individual,

[0914] A device for sharing the generated health management plan and analysis results with a third party,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, comprising a device that understands an individual's interests based on their conversation history and generates and presents appropriate dialogue.

[0918] (Claim 3)

[0919] The system according to claim 1, comprising a device for providing an individual with game content aimed at maintaining and improving cognitive function, based on analyzed physiological information and the individual's past learning history.

[0920] "Example 1"

[0921] (Claim 1)

[0922] Means of obtaining personal physiological information,

[0923] A means for receiving physiological information obtained from the aforementioned means and performing statistical analysis,

[0924] A means of generating a health management plan based on the analyzed results and notifying the individual,

[0925] A means of sharing the generated health management plan and analysis results with a third party,

[0926] A means of analyzing an individual's interests based on their past conversation history and providing appropriate dialogue,

[0927] A means of providing content aimed at maintaining and improving cognitive function based on analyzed physiological information and learning history,

[0928] A means of recognizing voice commands and operating related devices,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, comprising a device that understands an individual's interests based on their conversation history and generates appropriate conversations.

[0932] (Claim 3)

[0933] The system according to claim 1, comprising a device for providing an individual with entertainment content aimed at maintaining and improving cognitive function, based on analyzed physiological information and the individual's past activity history.

[0934] "Application Example 1"

[0935] (Claim 1)

[0936] Means for collecting physiological information on individuals,

[0937] A means for receiving and analyzing physiological information obtained from the aforementioned means,

[0938] A means of generating a health management plan based on the analyzed results and presenting it to the individual,

[0939] A means of sharing the generated health management plan and analysis results with a third party,

[0940] A means of providing cognitive training content to an individual based on analysis results using a generative AI model,

[0941] A means of recognizing individual voice commands and operating home appliances,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, comprising means for understanding an individual's interests based on their conversation history and generating and presenting appropriate dialogue.

[0945] (Claim 3)

[0946] The system according to claim 1, comprising means for providing an individual with play content aimed at maintaining and improving cognitive function, based on analyzed physiological information and the individual's past activity history.

[0947] "Example 2 of combining an emotion engine"

[0948] (Claim 1)

[0949] Equipment for collecting physiological data of individuals,

[0950] A device that receives physiological data collected from the aforementioned device and processes the data,

[0951] A device that constructs and presents an individualized health management plan based on data analysis results,

[0952] A device that processes individual voice and facial expression information and analyzes emotional states,

[0953] A device that provides individualized support based on emotional state and health management plan,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, comprising means for identifying an individual's interests based on conversation history information and emotional state, and for conducting content-appropriate dialogue.

[0957] (Claim 3)

[0958] The system according to claim 1, comprising means for providing an individual with digital content aimed at maintaining and improving cognitive function, based on analyzed physiological data and emotional state.

[0959] "Application example 2 when combining with an emotional engine"

[0960] (Claim 1)

[0961] Means of obtaining personal physiological information,

[0962] A means for receiving and analyzing physiological information obtained from the aforementioned means,

[0963] A means of generating a health management plan based on the analyzed results and presenting it to the individual,

[0964] A means of providing a health management plan based on an individual's emotional state using emotion analysis technology,

[0965] A means of sharing the generated health management plan and analysis results with a third party,

[0966] A system that includes this.

[0967] (Claim 2)

[0968] The system according to claim 1, comprising means for understanding an individual's interests based on their conversation history and emotional state, and for generating and presenting appropriate dialogue.

[0969] (Claim 3)

[0970] The system according to claim 1, comprising means for providing an individual with a game aimed at maintaining and improving cognitive function, based on analyzed physiological information, an individual's emotional state, and past learning history. [Explanation of Symbols]

[0971] 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. Means for obtaining personal physiological information, A means for receiving and analyzing the acquired physiological information, A means of generating a health management plan based on the analyzed results and presenting it to the individual, A means of sharing the generated health management plan and analysis results with a third party, A system that includes this.

2. The system according to claim 1, comprising means for understanding an individual's interests based on their conversation history and generating and presenting appropriate dialogue.

3. The system according to claim 1, comprising means for providing an individual with game content aimed at maintaining and improving cognitive function, based on analyzed physiological information and the individual's past learning history.

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