system

Abstract

We provide the system. [Solution] A means of acquiring health status data, A generation means for generating an individualized brain function training program based on acquired health status data, A means of providing a generated brain function training program, An evaluation method for evaluating the training implementation status by engaging in natural language dialogue with the user, A referral method that provides referrals to medical facilities based on evaluation results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, with the increase in the elderly population, the decline of cognitive function has been regarded as a problem, and its prevention and early response are important. However, it is not easy to receive continuous care by a specialist doctor in busy daily life, and there is a lack of means to easily receive individually optimized training and health management. Therefore, there is a demand for a system that allows users to efficiently and continuously care for their cognitive function based on their own health status.

Means for Solving the Problems

[0005] This invention provides a system that generates an individualized brain function training program based on health status data acquired from a user and provides that program to the user. The system evaluates the progress of the training through natural language dialogue with the user and, based on the results, refers the user to a medical facility as needed. It also includes means for analyzing the acquired health status data and providing the user with optimal health-related advice. Furthermore, by collecting feedback from the user and adjusting the training program based on that feedback, it is possible to continuously provide optimal care.

[0006] "Health status data" refers to a collection of information about an individual's physical and mental health, including measurements obtained through fitness trackers and medical checkups.

[0007] A "personalized brain function training program" is a training method or set of activities specifically designed based on an individual user's health data, with the aim of maintaining or improving cognitive function.

[0008] "Natural language dialogue" is a technology that facilitates communication through the exchange of written language, and is used as a user interface.

[0009] "Referral to a medical facility" refers to the act of suggesting or arranging a visit to a partner medical institution in order to professionally diagnose or address the user's health condition.

[0010] "Health-related advice" refers to information and suggestions provided to maintain and improve the user's health, and is based on an analysis of the user's health status data.

[0011] "Feedback" refers to information provided by users to the system, including evaluations and opinions on implemented programs and services provided. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that provides personalized brain function training programs by acquiring and analyzing users' health data. This system mainly consists of three components: a server, a terminal, and a user.

[0034] First, the user inputs fitness tracker data and separately provided health information through their device. This information is sent to a server as health status data and stored there. The server uses algorithms to analyze the received data to assess the user's health status and generate a personalized brain function training program. This generated program is tailored to the user's physical and cognitive needs.

[0035] The generated program is provided to the user via a terminal. The user can run the provided program and input the results and feedback into the terminal at any time. This feedback is sent back to the server and used to evaluate the training and adjust the program as needed.

[0036] Furthermore, the system features a dialogue function that utilizes natural language processing, allowing users to check the progress of their work and resolve any questions they may have through conversations with the system via their terminals. The server analyzes this dialogue to gain a deeper understanding of the user's needs and the progress of their work.

[0037] Furthermore, the server monitors the user's health status and, if certain criteria are met or if a need arises, will refer the user to an appropriate medical institution. This procedure is carried out quickly and efficiently, based on the user's consent.

[0038] As a concrete example, consider the case of a 65-year-old male user who uses this system. He provides data on his heart rate, steps, and sleep patterns, and as a result, the server suggests a program of moderate-intensity exercise three times a week and meditation for relaxation. The user starts the activities according to the suggestion and records his progress as feedback on the terminal. As a result, when his heart rate stabilizes and his sleep patterns improve, the server recommends continuing the program and makes adjustments as needed. In this way, the present invention provides a program tailored to the individual user and supports the maintenance and improvement of their health.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user activates the device and enters personal health data through the application. This data includes steps, heart rate, and sleep data from a fitness tracker, and, if necessary, the results of a recent medical checkup.

[0042] Step 2:

[0043] The device temporarily stores health data entered by the user and transmits it to the server with security in mind. Encryption technology is used for this transmission, ensuring the data is managed securely.

[0044] Step 3:

[0045] The server stores the received health data in a database. After saving, the data analysis engine starts and uses machine learning algorithms to create a health profile of the user.

[0046] Step 4:

[0047] Based on the analysis results, the server generates a personalized brain function training program optimized for the user's needs. This program includes activities that take into account physical and cognitive characteristics.

[0048] Step 5:

[0049] The server sends the generated training program and related health advice to the terminal.

[0050] Step 6:

[0051] The terminal displays programs received from the server to the user and supports reminders for planned implementation and recording of progress.

[0052] Step 7:

[0053] Users incorporate the provided program into their daily lives and input their results and feedback into their device.

[0054] Step 8:

[0055] The device sends the feedback collected from the user to the server, where it stores the data and prepares it for analysis.

[0056] Step 9:

[0057] The server analyzes the feedback data and adjusts the program content as needed. Furthermore, if the collected data indicates that referral to a medical facility is necessary, it considers appropriate measures.

[0058] Step 10:

[0059] The server then sends the adjusted program back to the terminal, providing the user with the latest training and health advice.

[0060] (Example 1)

[0061] 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."

[0062] In modern society, with its busy lifestyles and increasing stress, there is a growing need to continuously manage individuals' health and provide effective training programs. However, traditional methods often involve standardized programs, making it difficult to provide training optimized for each individual's health condition. Furthermore, there is a lack of processes for appropriately adjusting program implementation and for promptly referring and coordinating with medical institutions.

[0063] 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.

[0064] In this invention, the server includes data collection means for collecting the user's physical data, program generation means for analyzing the collected data and generating individually adapted cognitive training programs, and program provision means for providing the generated programs to the user. This enables the provision of personalized health management and training programs.

[0065] "Data collection means" refers to a device or technology that has the function of acquiring a user's physical data and securing the information for use within the system.

[0066] "Program generation means" refers to a device or method for analyzing collected data and constructing a cognitive training program that is most suitable for each individual.

[0067] "Program delivery means" refers to a device or system for presenting a generated cognitive training program to a user and providing it in an usable format.

[0068] "Dialogue evaluation means" refers to a technology or device that has the function of understanding and evaluating the status of program implementation through natural language communication with the user.

[0069] "Medical referral means" refers to the process or procedure of referring users to appropriate medical institutions as needed, based on evaluation results.

[0070] "Guidance provision means" refers to a device or method for providing users with advice on maintaining their health based on analyzed data.

[0071] A "program adaptation means" is a system or device that has the function of adjusting and optimizing a cognitive training program based on feedback collected from users.

[0072] This invention is a system that provides personalized cognitive training programs by collecting and analyzing users' health data. The system mainly consists of a server, terminals, and users, and operates as follows.

[0073] Users input physical data such as heart rate, steps, and sleep patterns into a device equipped with a fitness tracker or dedicated application. The device receives the input data, formats it appropriately, and then sends it to the server. The data is encrypted for security purposes, ensuring privacy protection.

[0074] The server stores the received data and analyzes it using a generative AI model. This model analyzes the data while taking into account the user's past data and information from other similar users to generate an optimal cognitive training program for each user.

[0075] The generated program is sent from the server to the terminal and provided to the user. The user runs the provided cognitive training program and inputs progress and feedback into the terminal. This feedback is sent back to the server and used to evaluate and adjust the training program.

[0076] Furthermore, the terminal is equipped with a natural language processing dialogue function, which allows it to communicate with the user to understand the progress of their training and resolve any questions the user may have. The server analyzes this dialogue and, if necessary, can provide the user with additional health advice or refer them to an appropriate medical institution.

[0077] For example, a 65-year-old male user of this system provides data on his heart rate, steps, and sleep patterns. The server then suggests a program consisting of three moderate-intensity exercise sessions per week and meditation focused on relaxation. The user begins following this program, recording results and feedback on their device. The server then analyzes the program's effectiveness and adjusts the content as needed.

[0078] For example, by inputting the prompt "Generate a personalized cognitive training program based on the health data of a 65-year-old male" into the AI ​​model, it is possible to generate an appropriate program.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1: The user inputs physical data such as heart rate, steps, and sleep patterns through a fitness tracker or dedicated app. This input data includes information indicating the user's activity level, rest status, and health condition. The device collects this data, formats it, and prepares it for transmission to the server. The output is the user's health data in a format that can be sent to the server.

[0081] Step 2: The device sends the formatted user data to the server. The data is encrypted during this process, ensuring data security and user privacy. The input is formatted health data, and the output is data that securely reaches the server.

[0082] Step 3: The server stores the received user health data in the database. This input data is associated with the past data of each user account and stored as historical data. The output is a user-specific dataset stored in the database.

[0083] Step 4: The server analyzes the accumulated data using a generation AI model. It takes user history data as input, performs data processing and calculations, and generates a personalized cognitive training program. The output is a training program based on the user's physical and cognitive needs.

[0084] Step 5: The generated training program is sent from the server to the terminal. The terminal displays this program to the user and provides it in an executable format. The input is the generated program data, and the output is the program information that the user receives and can view.

[0085] Step 6: The user executes the training program instructed from the terminal and inputs activity details and feedback. The input consists of training completion status and subjective feedback, and the output is updated data sent to the server via the terminal.

[0086] Step 7: The server analyzes the received feedback and adjusts the training program as needed. The input is the latest feedback data, and the server performs data analysis and calculations to output the adjusted program.

[0087] (Application Example 1)

[0088] 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."

[0089] In modern society, there is a demand for personalized health management and improved brain function amidst busy daily lives. Furthermore, training at fitness gyms often fails to meet the individual needs of users, resulting in insufficient effectiveness. Moreover, real-time feedback and timely advice are necessary for users to understand their own health status and train appropriately. These problems are not adequately addressed by current health management services and fitness facilities.

[0090] 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.

[0091] In this invention, the server includes acquisition means for acquiring health indicator data, generation means for generating an individualized cognitive function training program based on the acquired health indicator data, and display means for displaying feedback in real time using a visual device. This allows users to receive on-the-spot guidance through individualized training, maximizing the effectiveness of the training and enabling efficient management of their health status.

[0092] "Health indicator data" refers to data that indicates the user's physical and physiological state, including information such as heart rate, exercise level, and sleep patterns.

[0093] A "cognitive function training program" is a program of individualized physical and mental exercises designed to improve a user's cognitive abilities.

[0094] "Generating means" refers to elements or methods for formulating an individualized training program based on acquired health indicator data.

[0095] "Evaluation means" refers to elements or methods for understanding and evaluating the user's training progress through natural language dialogue.

[0096] "Referral means" refers to elements or methods for referring users to appropriate medical institutions based on evaluation results.

[0097] "Visual devices" are devices used to present information to users visually, and include smart glasses and similar devices.

[0098] This invention constructs a system in which three elements—a server, a terminal, and a user—work in coordination. The following steps are important for implementation.

[0099] First, the fitness tracker or device worn by the user collects and acquires health indicator data such as heart rate, exercise volume, and sleep patterns. This data is transmitted to a server via the device. Based on this acquired data, the server generates a personalized cognitive training program using a dedicated algorithm. This program is tailored to the user's specific health and cognitive needs.

[0100] The generated program is provided to the user through the terminal's visual device. This visual device can include smart glasses or a head-mounted display. This allows the user to receive training programs and feedback in real time.

[0101] Furthermore, the server possesses natural language processing capabilities and interacts with the user through the terminal. This interaction is conducted to evaluate the program's performance and user feedback, and the results are used to refine the program. It also includes a function to provide users with timely health-related advice.

[0102] As a concrete example of use, consider a 65-year-old male user using this system while exercising at a gym. Heart rate data is collected in real time, and based on that data, a moderate-intensity exercise program is displayed on a visual device. Also, when the user asks about their progress, the server uses a generative AI model to analyze the prompt text and provide appropriate feedback.

[0103] An example of a prompt message is, "Based on this user's heart rate and exercise history, suggest the next exercise to perform. This is for men aged 55 and over who require moderate-intensity exercise." In this way, this invention can provide users with appropriate programs tailored to their individual health conditions and needs, maximizing training results.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] Users collect health indicator data such as heart rate, exercise level, and sleep patterns using fitness trackers or devices. The collected data is sent from the device to the server. In this step, health indicator data is entered, and the device processes and sends that entered data to the server.

[0107] Step 2:

[0108] The server performs data analysis based on the received health indicator data. This analysis uses algorithms to assess the user's health status and cognitive function needs, and generates a personalized cognitive training program. The input is health indicator data, and the output is a personalized training program. Specifically, the server uses a generation AI model to optimize the program to be generated.

[0109] Step 3:

[0110] The generated cognitive training program is displayed on a visual device via a terminal. The user checks and performs the program using a visual device such as smart glasses. Here, the training program is used as input and output as visual feedback to the visual device. The terminal processes the program to present to the user in real time.

[0111] Step 4:

[0112] The user inputs the progress and feedback of the program they have implemented into the terminal. The terminal sends this feedback information to the server. The input is the user's feedback data, and the output is the transmission of the feedback information to the server. The terminal performs the specific actions of collecting the input data from the user and transmitting it.

[0113] Step 5:

[0114] The server analyzes the feedback information and adjusts the generated program as needed. Natural language processing techniques are used for this analysis. The input is feedback information, and the output is the program adjustment as required. The server uses generated prompts to suggest a new program or recommend continuation.

[0115] Step 6:

[0116] Through natural language interaction with the user via the terminal, the server monitors and evaluates the user's training progress. This interaction then provides appropriate health-related advice. The input is the user's dialogue, and the output is health-related feedback and advice. Specifically, a generative AI model is used to generate responses to the prompt "Provide further advice based on this user's feedback."

[0117] 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.

[0118] This invention provides a system that offers personalized brain function training programs by recognizing the user's health status data and emotions. This system integrates the acquisition of health status data, emotion recognition by an emotion engine, generation of personalized programs, program delivery and adjustment, natural language dialogue, and means of appropriate referral to medical facilities.

[0119] First, users participate in the system by entering their personal health data via their device. This data includes information obtained from fitness trackers and medical checkups. The transmitted data is received by the server and stored in a database for analysis.

[0120] The server performs analysis based on health status data and the user's emotions detected by the emotion engine. The emotion engine recognizes emotions through interaction with the user and facial expression analysis using the camera, and creates an emotion profile. Using this information, the server generates a brain function training program that is appropriate for the user's current state.

[0121] The generated program is provided to the user via a terminal and includes a feedback function that responds to the user's emotions. The user performs the presented program and inputs feedback on progress and emotions into the system via the terminal. This feedback is sent to a server and used to re-evaluate and adjust the personalized program.

[0122] Furthermore, the natural language dialogue function responds to user feedback and questions in real time, adjusting the content and tone of the dialogue using information from the emotion engine. This allows users to receive more empathetic and appropriate feedback.

[0123] For example, consider a 70-year-old female user of this system. When the emotion engine detects that this user is feeling down and stressed, the server generates a relaxation-focused program and provides it along with advice to reduce stress. If the user continues to follow the program and improvements are seen, the server makes appropriate adjustments and proposes a new program.

[0124] Thus, the present invention aims to provide appropriate care and support by comprehensively considering the user's health and emotions.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user activates the device and retrieves health data (e.g., steps, heart rate, sleep data) from a fitness tracker, then inputs or syncs it to the device.

[0128] Step 2:

[0129] The device sends health status data entered or synchronized by the user to the server. This data is transmitted using a secure communication protocol.

[0130] Step 3:

[0131] The server stores the received health data in a database and uses that data to initiate analysis using machine learning algorithms. This analysis creates a basic health profile of the user.

[0132] Step 4:

[0133] The device activates an emotion engine to recognize the user's current emotions. The emotion engine uses facial recognition via the camera and analysis of voice tone to obtain the user's emotions.

[0134] Step 5:

[0135] The server integrates analyzed health data with user emotion data recognized by the emotion engine to generate a personalized brain function training program.

[0136] Step 6:

[0137] The server sends the generated program and associated health advice to the terminal. The content provided is tailored to the user's health status and emotional state.

[0138] Step 7:

[0139] The terminal displays programs and advice received from the server to the user. It also generates notifications and reminders to encourage the user to implement the plan.

[0140] Step 8:

[0141] Users complete the provided program and input feedback on the results and their own feelings into the device.

[0142] Step 9:

[0143] The device sends user feedback data to the server. This feedback data also includes information about changes in emotions.

[0144] Step 10:

[0145] The server analyzes the feedback data and adjusts the training program and advice as needed. Based on the results, it also evaluates whether a referral to a medical institution is necessary.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] In modern society, properly managing and improving an individual's health and mental state is a crucial challenge. Traditional approaches have struggled to comprehensively analyze health and emotions and provide individualized training based on that analysis, and have lacked the means to flexibly adjust programs based on feedback. As a result, there has been a problem in providing effective and long-term support that is tailored to individual needs.

[0149] 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.

[0150] In this invention, the server includes acquisition means for acquiring health status information, generation means for generating personalized brain function training based on the acquired health status information and an emotional profile obtained by an emotion recognition means, and provision means for providing the generated brain function training and collecting feedback through natural language dialogue with the user. This makes it possible to provide appropriate and timely health support to individuals.

[0151] "Health status information" refers to data about an individual's physical function and condition obtained from fitness trackers, health checkups, and other sources.

[0152] "Emotion recognition means" refers to technology for generating an emotion profile from a user's facial expressions and voice, and includes algorithms and devices for analyzing specific emotions.

[0153] "Brain function training" refers to a series of activities and exercises aimed at improving a user's cognitive abilities and psychological well-being.

[0154] A "generative AI model" is an algorithm that uses machine learning techniques to generate personalized programs and advice.

[0155] "Feedback" refers to information about changes in emotions or the effectiveness of a program that users provide to the system, and this data is used to adjust the program.

[0156] "Referral methods" refer to a function that suggests visits to specialized facilities or medical institutions based on the user's health status and feedback.

[0157] The system of this invention provides personalized brain function training based on the user's health status information and emotions. Specifically, it begins with the user using a device to input health status information obtained through a fitness tracker or health checkup. This device refers to a mobile information terminal such as a smartphone or tablet. Similarly, the user uses the camera and microphone on the device to activate emotion recognition means. This analyzes the user's facial expressions and voice, and generates an emotion profile.

[0158] The server receives acquired health status information and emotional profiles and securely stores them in a database. Based on this, a generative AI model is activated to generate brain function training optimized for the user. This generative AI model is a machine learning algorithm used to generate personalized programs and advice.

[0159] The generated training program is provided to the user via a terminal. The provided program includes activities that focus on the user's current health and emotions. The user completes this program and sends feedback on changes in emotions and the effectiveness of the training to the server via the terminal. The server analyzes the collected feedback and makes appropriate adjustments to the program.

[0160] For example, if a 70-year-old user is using the system and the emotion recognition system detects that they are experiencing stress, the server will generate a relaxation program specifically designed to reduce stress and provide it to the user's device. Based on feedback after the program is implemented, the program content will be appropriately updated.

[0161] An example of a prompt might be, "If a 70-year-old user is experiencing stress, what relaxation program should be suggested?" The system comprehensively supports the user's health and emotions, enabling it to provide individually optimized care.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users input health information obtained from fitness trackers and health checkups via a device. This input includes vital data such as heart rate, sleep duration, and exercise level. The device centralizes this data and formats it for transmission to the server.

[0165] Step 2:

[0166] The device captures the user's facial expressions and voice using its camera and microphone, activating emotion recognition mechanisms. This process collects real-time facial and voice samples from the user. The device analyzes this data and sends it to a server as input to generate emotional profiles such as joy, sadness, and stress.

[0167] Step 3:

[0168] The server receives health status information and emotional profiles sent from the terminal and stores them in a database. The stored data is used as the basis for processing the generative AI model. The server verifies that the data has been received accurately and performs error checking.

[0169] Step 4:

[0170] The server activates a generative AI model based on health status information and emotional profiles to generate a personalized brain function training program for the user. This process involves formulating the optimal program for the user based on past success stories and relevant research data. The generated program is created in a format that allows for adjustments based on user feedback.

[0171] Step 5:

[0172] The device receives the generated brain function training program and displays it to the user as a schedule of specific exercises and activities. The device uses its notification function to send reminders to the user to continue training.

[0173] Step 6:

[0174] Users perform the provided program and input feedback on changes in their emotions and the effectiveness of the training into their device. As specific feedback, users record subjective impressions such as, "After performing the relaxation exercises, I felt an improvement in my stress levels," and send this information to the server via their device.

[0175] Step 7:

[0176] The server analyzes the collected feedback and, if necessary, reuses the generated AI model to adjust the brain function training program. This process provides insights into which elements of the feedback should be improved, and these are reflected in the next program generation. The server sends these results to the terminal and distributes program update information.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] Providing appropriate customer service to each individual customer in a physical store is not easy. Individualized service tailored to each customer's emotions and health condition is required, but traditional customer service methods may not adequately address this. This invention aims to improve the quality of lean-inclusive customer service by utilizing customer health data and emotions.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes a collection means for collecting health data, a generation means for generating a personalized brain function improvement program based on the collected health data and sentiment analysis, a presentation means for presenting the generated brain function improvement program, an evaluation means for engaging in natural language dialogue with the user via a dialogue device and evaluating customer satisfaction, and an adjustment means for adjusting the service provision method based on the evaluation results. This enables appropriate customer service for each individual customer in a physical store.

[0182] "Health data" refers to information about the user's physical condition and vital signs, including heart rate, blood pressure, body temperature, and stress level.

[0183] "Emotion analysis" is a technology that identifies and profiles a user's emotional state from their facial expressions and voice.

[0184] A "brain function improvement program" refers to a personalized training and activity plan designed to improve a user's cognitive abilities and mental health.

[0185] "Presentation means" refers to methods or devices for visually or audibly showing a generated program or information to a user.

[0186] A "dialogue device" refers to a device or system that enables natural language communication with a user, receiving user input and providing a corresponding response.

[0187] "Evaluation methods" refer to methods and systems for evaluating the effectiveness of a service and customer satisfaction based on natural language dialogue with users and behavioral data.

[0188] "Adjustment measures" refer to methods and algorithms for modifying the content of services and programs provided based on evaluation results, thereby enhancing individualized support.

[0189] The system implementing this invention is centered around a server that collects health data and analyzes it to understand the customer's unique emotions and state. Health data is acquired from smart devices (e.g., smartwatches and fitness trackers). The collected data is managed by the server, and facial recognition and emotion analysis are performed using OpenCV and TENSORFLOW® technologies. This creates a user emotion profile.

[0190] Subsequently, the server uses a generative AI model to generate a personalized brain function enhancement program. This program includes appropriate menus tailored to the user's health condition and emotions, and is delivered to the user through presentation methods, such as smart glasses or a smartphone.

[0191] The dialogue system collects user feedback and training progress in real time. Users can communicate their feelings and requests through natural language input, and the server provides immediate feedback accordingly.

[0192] For example, if a customer is perceived as stressed in a physical store, the system generates a relaxation-focused program and suggests customer service methods aligned with that program to the staff. The server generates a prompt such as, "Please suggest appropriate measures for relaxation."

[0193] This allows users to receive care and services tailored to their individual needs, which is expected to result in a high level of satisfaction.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The device collects the user's health data using various sensors. This data includes heart rate, blood pressure, and body temperature. The device sends this data to a server. By processing these variables, the server can perform a detailed analysis of the user's health status.

[0197] Step 2:

[0198] The server performs image analysis using OpenCV on health data received and facial expression data acquired using smart glasses or smartphone cameras, and then performs emotion analysis using TensorFlow. As a result, a user emotion profile is generated. This profile is updated in real time and recorded on the server.

[0199] Step 3:

[0200] The server inputs health data and emotional profiles into a generating AI model to create a brain function improvement program optimized for the user. The generated program includes training menus such as relaxation, stress reduction, and concentration enhancement. This information is provided to the user through presentation tools based on prompt messages.

[0201] Step 4:

[0202] Users begin activities using the provided program. They input feedback via their device regarding the progress of their activities and changes in their emotions. This feedback is sent to the server and used to evolve and improve individual programs. This ensures that programs are always tailored to the user's needs.

[0203] Step 5:

[0204] The server's dialogue system uses natural language processing to interpret the collected feedback and adjust the program content as needed. During this process, the user receives additional advice and new instructions through dialogue. An example of a prompt might be, "Please tell me how to improve my concentration in the next step."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention is a system that provides personalized brain function training programs by acquiring and analyzing users' health data. This system mainly consists of three components: a server, a terminal, and a user.

[0222] First, the user inputs fitness tracker data and separately provided health information through their device. This information is sent to a server as health status data and stored there. The server uses algorithms to analyze the received data to assess the user's health status and generate a personalized brain function training program. This generated program is tailored to the user's physical and cognitive needs.

[0223] The generated program is provided to the user via a terminal. The user can run the provided program and input the results and feedback into the terminal at any time. This feedback is sent back to the server and used to evaluate the training and adjust the program as needed.

[0224] Furthermore, the system features a dialogue function that utilizes natural language processing, allowing users to check the progress of their work and resolve any questions they may have through conversations with the system via their terminals. The server analyzes this dialogue to gain a deeper understanding of the user's needs and the progress of their work.

[0225] Furthermore, the server monitors the user's health status and, if certain criteria are met or if a need arises, will refer the user to an appropriate medical institution. This procedure is carried out quickly and efficiently, based on the user's consent.

[0226] As a concrete example, consider the case of a 65-year-old male user who uses this system. He provides data on his heart rate, steps, and sleep patterns, and as a result, the server suggests a program of moderate-intensity exercise three times a week and meditation for relaxation. The user starts the activities according to the suggestion and records his progress as feedback on the terminal. As a result, when his heart rate stabilizes and his sleep patterns improve, the server recommends continuing the program and makes adjustments as needed. In this way, the present invention provides a program tailored to the individual user and supports the maintenance and improvement of their health.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user activates the device and enters personal health data through the application. This data includes steps, heart rate, and sleep data from a fitness tracker, and, if necessary, the results of a recent medical checkup.

[0230] Step 2:

[0231] The device temporarily stores health data entered by the user and transmits it to the server with security in mind. Encryption technology is used for this transmission, ensuring the data is managed securely.

[0232] Step 3:

[0233] The server stores the received health data in a database. After saving, the data analysis engine starts and uses machine learning algorithms to create a health profile of the user.

[0234] Step 4:

[0235] Based on the analysis results, the server generates a personalized brain function training program optimized for the user's needs. This program includes activities that take into account physical and cognitive characteristics.

[0236] Step 5:

[0237] The server sends the generated training program and related health advice to the terminal.

[0238] Step 6:

[0239] The terminal displays programs received from the server to the user and supports reminders for planned implementation and recording of progress.

[0240] Step 7:

[0241] Users incorporate the provided program into their daily lives and input their results and feedback into their device.

[0242] Step 8:

[0243] The device sends the feedback collected from the user to the server, where it stores the data and prepares it for analysis.

[0244] Step 9:

[0245] The server analyzes the feedback data and adjusts the program content as needed. Furthermore, if the collected data indicates that referral to a medical facility is necessary, it considers appropriate measures.

[0246] Step 10:

[0247] The server then sends the adjusted program back to the terminal, providing the user with the latest training and health advice.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] In modern society, with its busy lifestyles and increasing stress, there is a growing need to continuously manage individuals' health and provide effective training programs. However, traditional methods often involve standardized programs, making it difficult to provide training optimized for each individual's health condition. Furthermore, there is a lack of processes for appropriately adjusting program implementation and for promptly referring and coordinating with medical institutions.

[0251] 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.

[0252] In this invention, the server includes data collection means for collecting the user's physical data, program generation means for analyzing the collected data and generating individually adapted cognitive training programs, and program provision means for providing the generated programs to the user. This enables the provision of personalized health management and training programs.

[0253] "Data collection means" refers to a device or technology that has the function of acquiring a user's physical data and securing the information for use within the system.

[0254] "Program generation means" refers to a device or method for analyzing collected data and constructing a cognitive training program that is most suitable for each individual.

[0255] "Program delivery means" refers to a device or system for presenting a generated cognitive training program to a user and providing it in an usable format.

[0256] "Dialogue evaluation means" refers to a technology or device that has the function of understanding and evaluating the status of program implementation through natural language communication with the user.

[0257] "Medical referral means" refers to the process or procedure of referring users to appropriate medical institutions as needed, based on evaluation results.

[0258] "Guidance provision means" refers to a device or method for providing users with advice on maintaining their health based on analyzed data.

[0259] A "program adaptation means" is a system or device that has the function of adjusting and optimizing a cognitive training program based on feedback collected from users.

[0260] This invention is a system that provides personalized cognitive training programs by collecting and analyzing users' health data. The system mainly consists of a server, terminals, and users, and operates as follows.

[0261] Users input physical data such as heart rate, steps, and sleep patterns into a device equipped with a fitness tracker or dedicated application. The device receives the input data, formats it appropriately, and then sends it to the server. The data is encrypted for security purposes, ensuring privacy protection.

[0262] The server stores the received data and analyzes it using a generative AI model. This model analyzes the data while taking into account the user's past data and information from other similar users to generate an optimal cognitive training program for each user.

[0263] The generated program is sent from the server to the terminal and provided to the user. The user runs the provided cognitive training program and inputs progress and feedback into the terminal. This feedback is sent back to the server and used to evaluate and adjust the training program.

[0264] Furthermore, the terminal is equipped with a natural language processing dialogue function, which allows it to communicate with the user to understand the progress of their training and resolve any questions the user may have. The server analyzes this dialogue and, if necessary, can provide the user with additional health advice or refer them to an appropriate medical institution.

[0265] For example, a 65-year-old male user of this system provides data on his heart rate, steps, and sleep patterns. The server then suggests a program consisting of three moderate-intensity exercise sessions per week and meditation focused on relaxation. The user begins following this program, recording results and feedback on their device. The server then analyzes the program's effectiveness and adjusts the content as needed.

[0266] For example, by inputting the prompt "Generate a personalized cognitive training program based on the health data of a 65-year-old male" into the AI ​​model, it is possible to generate an appropriate program.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1: The user inputs physical data such as heart rate, steps, and sleep patterns through a fitness tracker or dedicated app. This input data includes information indicating the user's activity level, rest status, and health condition. The device collects this data, formats it, and prepares it for transmission to the server. The output is the user's health data in a format that can be sent to the server.

[0269] Step 2: The device sends the formatted user data to the server. The data is encrypted during this process, ensuring data security and user privacy. The input is formatted health data, and the output is data that securely reaches the server.

[0270] Step 3: The server stores the received user health data in the database. This input data is associated with the past data of each user account and stored as historical data. The output is a user-specific dataset stored in the database.

[0271] Step 4: The server analyzes the accumulated data using a generation AI model. It takes user history data as input, performs data processing and calculations, and generates a personalized cognitive training program. The output is a training program based on the user's physical and cognitive needs.

[0272] Step 5: The generated training program is sent from the server to the terminal. The terminal displays this program to the user and provides it in an executable format. The input is the generated program data, and the output is the program information that the user receives and can view.

[0273] Step 6: The user executes the training program instructed from the terminal and inputs activity details and feedback. The input consists of training completion status and subjective feedback, and the output is updated data sent to the server via the terminal.

[0274] Step 7: The server analyzes the received feedback and adjusts the training program as needed. The input is the latest feedback data, and the server performs data analysis and calculations to output the adjusted program.

[0275] (Application Example 1)

[0276] 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."

[0277] In modern society, there is a demand for personalized health management and improved brain function amidst busy daily lives. Furthermore, training at fitness gyms often does not meet the individual needs of users, resulting in insufficient effectiveness. Moreover, real-time feedback and timely advice are necessary for users to understand their own health status and train appropriately. These problems are not adequately addressed by current health management services and fitness facilities.

[0278] 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.

[0279] In this invention, the server includes an acquisition means for acquiring health index data, a generation means for generating an individualized cognitive function training program based on the acquired health index data, and a display means for displaying feedback in real time using a visual device. As a result, the user can receive on-site guidance through individualized training, maximizing the training effect and enabling efficient management of the health condition.

[0280] "Health index data" refers to data indicating the physical and physiological conditions of the user, including information such as heart rate, amount of exercise, and sleep pattern.

[0281] "Cognitive function training program" refers to a program related to individualized exercises and mental activities for improving the user's cognitive ability.

[0282] "Generation means" refers to an element or method for formulating an individualized training program based on the acquired health index data.

[0283] "Evaluation means" refers to an element or method for grasping and evaluating the user's training implementation status through dialogue in natural language.

[0284] "Referral means" refers to an element or method for referring the user to an appropriate medical institution based on the evaluation result.

[0285] "Visual device" refers to a device for presenting information visually to the user, including smart glasses, etc.

[0286] In this invention, a system mainly composed of three elements, namely the server, the terminal, and the user, operates in cooperation. In implementation, the following procedures are important.

[0287] First, the fitness tracker or device worn by the user collects and acquires health indicator data such as heart rate, exercise volume, and sleep patterns. This data is transmitted to a server via the device. Based on this acquired data, the server generates a personalized cognitive training program using a dedicated algorithm. This program is tailored to the user's specific health and cognitive needs.

[0288] The generated program is provided to the user through the terminal's visual device. This visual device can include smart glasses or a head-mounted display. This allows the user to receive training programs and feedback in real time.

[0289] Furthermore, the server possesses natural language processing capabilities and interacts with the user through the terminal. This interaction is conducted to evaluate the program's performance and user feedback, and the results are used to refine the program. It also includes a function to provide users with timely health-related advice.

[0290] As a concrete example of use, consider a 65-year-old male user using this system while exercising at a gym. Heart rate data is collected in real time, and based on that data, a moderate-intensity exercise program is displayed on a visual device. Also, when the user asks about their progress, the server uses a generative AI model to analyze the prompt text and provide appropriate feedback.

[0291] An example of a prompt message is, "Based on this user's heart rate and exercise history, suggest the next exercise to perform. This is for men aged 55 and over who require moderate-intensity exercise." In this way, this invention can provide users with appropriate programs tailored to their individual health conditions and needs, maximizing training results.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] Users collect health indicator data such as heart rate, exercise level, and sleep patterns using fitness trackers or devices. The collected data is sent from the device to the server. In this step, health indicator data is entered, and the device processes and sends that entered data to the server.

[0295] Step 2:

[0296] The server performs data analysis based on the received health indicator data. This analysis uses algorithms to assess the user's health status and cognitive function needs, and generates a personalized cognitive training program. The input is health indicator data, and the output is a personalized training program. Specifically, the server uses a generation AI model to optimize the program to be generated.

[0297] Step 3:

[0298] The generated cognitive training program is displayed on a visual device via a terminal. The user checks and performs the program using a visual device such as smart glasses. Here, the training program is used as input and output as visual feedback to the visual device. The terminal processes the program to present to the user in real time.

[0299] Step 4:

[0300] The user inputs the progress and feedback of the program they have implemented into the terminal. The terminal sends this feedback information to the server. The input is the user's feedback data, and the output is the transmission of the feedback information to the server. The terminal performs the specific actions of collecting the input data from the user and transmitting it.

[0301] Step 5:

[0302] The server analyzes the feedback information and adjusts the program generated as needed. Natural language processing technology is used for this analysis. The input is the feedback information, and the output is the adjustment of the program as needed. The server uses a generation prompt sentence to propose a new program or give a continuation advice.

[0303] Step 6:

[0304] Through the natural language interaction with the user via the terminal, the server checks and evaluates the user's training implementation status. Through this interaction, appropriate health-related advice is provided. The input is the content of the interaction from the user, and the output is health-related feedback and advice. Specifically, by utilizing the generation AI model, a response to the prompt sentence "Please provide further advice based on this user's feedback." is generated.

[0305] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0306] The present invention is a system that provides an individualized brain function training program by recognizing the user's health status data and emotion. This system integrates the acquisition of health status data, emotion recognition by the emotion engine, generation of an individualized program, provision and adjustment of the program, natural language interaction, and appropriate introduction means to a medical facility.

[0307] First, the user participates in the system by inputting personal health data through the terminal. This data includes information obtained from a fitness tracker or a medical check. The transmitted data is received by the server and stored in a database for analysis.

[0308] The server performs analysis based on health status data and the user's emotions detected by the emotion engine. The emotion engine recognizes emotions through interaction with the user and facial expression analysis using the camera, and creates an emotion profile. Using this information, the server generates a brain function training program that is appropriate for the user's current state.

[0309] The generated program is provided to the user via a terminal and includes a feedback function that responds to the user's emotions. The user performs the presented program and inputs feedback on progress and emotions into the system via the terminal. This feedback is sent to a server and used to re-evaluate and adjust the personalized program.

[0310] Furthermore, the natural language dialogue function responds to user feedback and questions in real time, adjusting the content and tone of the dialogue using information from the emotion engine. This allows users to receive more empathetic and appropriate feedback.

[0311] For example, consider a 70-year-old female user of this system. When the emotion engine detects that this user is feeling down and stressed, the server generates a relaxation-focused program and provides it along with advice to reduce stress. If the user continues to follow the program and improvements are seen, the server makes appropriate adjustments and proposes a new program.

[0312] Thus, the present invention aims to provide appropriate care and support by comprehensively considering the user's health and emotions.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The user activates the device and retrieves health data (e.g., steps, heart rate, sleep data) from a fitness tracker, then inputs or syncs it to the device.

[0316] Step 2:

[0317] The device sends health status data entered or synchronized by the user to the server. This data is transmitted using a secure communication protocol.

[0318] Step 3:

[0319] The server stores the received health data in a database and uses that data to initiate analysis using machine learning algorithms. This analysis creates a basic health profile of the user.

[0320] Step 4:

[0321] The device activates an emotion engine to recognize the user's current emotions. The emotion engine uses facial recognition via the camera and analysis of voice tone to obtain the user's emotions.

[0322] Step 5:

[0323] The server integrates analyzed health data with user emotion data recognized by the emotion engine to generate a personalized brain function training program.

[0324] Step 6:

[0325] The server sends the generated program and associated health advice to the terminal. The content provided is tailored to the user's health status and emotional state.

[0326] Step 7:

[0327] The terminal displays programs and advice received from the server to the user. It also generates notifications and reminders to encourage the user to implement the plan.

[0328] Step 8:

[0329] Users complete the provided program and input feedback on the results and their own feelings into the device.

[0330] Step 9:

[0331] The device sends user feedback data to the server. This feedback data also includes information about changes in emotions.

[0332] Step 10:

[0333] The server analyzes the feedback data and adjusts the training program and advice as needed. Based on the results, it also evaluates whether a referral to a medical institution is necessary.

[0334] (Example 2)

[0335] 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".

[0336] In modern society, properly managing and improving an individual's health and mental state is a crucial challenge. Traditional approaches have struggled to comprehensively analyze health and emotions and provide individualized training based on that analysis, and have lacked the means to flexibly adjust programs based on feedback. As a result, there has been a problem in providing effective and long-term support that is tailored to individual needs.

[0337] 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.

[0338] In this invention, the server includes acquisition means for acquiring health status information, generation means for generating personalized brain function training based on the acquired health status information and an emotional profile obtained by an emotion recognition means, and provision means for providing the generated brain function training and collecting feedback through natural language dialogue with the user. This makes it possible to provide appropriate and timely health support to individuals.

[0339] "Health status information" refers to data about an individual's physical function and condition obtained from fitness trackers, health checkups, and other sources.

[0340] "Emotion recognition means" refers to technology for generating an emotion profile from a user's facial expressions and voice, and includes algorithms and devices for analyzing specific emotions.

[0341] "Brain function training" refers to a series of activities and exercises aimed at improving a user's cognitive abilities and psychological well-being.

[0342] A "generative AI model" is an algorithm that uses machine learning techniques to generate personalized programs and advice.

[0343] "Feedback" refers to information about changes in emotions or the effectiveness of a program that users provide to the system, and this data is used to adjust the program.

[0344] "Referral methods" refer to a function that suggests visits to specialized facilities or medical institutions based on the user's health status and feedback.

[0345] The system of this invention provides personalized brain function training based on the user's health status information and emotions. Specifically, it begins with the user using a device to input health status information obtained through a fitness tracker or health checkup. This device refers to a mobile information terminal such as a smartphone or tablet. Similarly, the user uses the camera and microphone on the device to activate emotion recognition means. This analyzes the user's facial expressions and voice, and generates an emotion profile.

[0346] The server receives acquired health status information and emotional profiles and securely stores them in a database. Based on this, a generative AI model is activated to generate brain function training optimized for the user. This generative AI model is a machine learning algorithm used to generate personalized programs and advice.

[0347] The generated training program is provided to the user via a terminal. The provided program includes activities that focus on the user's current health and emotions. The user completes this program and sends feedback on changes in emotions and the effectiveness of the training to the server via the terminal. The server analyzes the collected feedback and makes appropriate adjustments to the program.

[0348] For example, if a 70-year-old user is using the system and the emotion recognition system detects that they are experiencing stress, the server will generate a relaxation program specifically designed to reduce stress and provide it to the user's device. Based on feedback after the program is implemented, the program content will be appropriately updated.

[0349] An example of a prompt might be, "If a 70-year-old user is experiencing stress, what relaxation program should be suggested?" The system comprehensively supports the user's health and emotions, enabling the provision of individually optimized care.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] Users input health information obtained from fitness trackers and health checkups via a device. This input includes vital data such as heart rate, sleep duration, and exercise level. The device centralizes this data and formats it for transmission to the server.

[0353] Step 2:

[0354] The device captures the user's facial expressions and voice using its camera and microphone, activating emotion recognition mechanisms. This process collects real-time facial and voice samples from the user. The device analyzes this data and sends it to a server as input to generate emotional profiles such as joy, sadness, and stress.

[0355] Step 3:

[0356] The server receives health status information and emotional profiles sent from the terminal and stores them in a database. The stored data is used as the basis for processing the generative AI model. The server verifies that the data has been received accurately and performs error checking.

[0357] Step 4:

[0358] The server activates a generative AI model based on health status information and emotional profiles to generate a personalized brain function training program for the user. This process involves formulating the optimal program for the user based on past success stories and relevant research data. The generated program is created in a format that allows for adjustments based on user feedback.

[0359] Step 5:

[0360] The device receives the generated brain function training program and displays it to the user as a schedule of specific exercises and activities. The device uses its notification function to send reminders to the user to continue training.

[0361] Step 6:

[0362] Users perform the provided program and input feedback on changes in their emotions and the effectiveness of the training into their device. As specific feedback, users record subjective impressions such as, "After performing the relaxation exercises, I felt an improvement in my stress levels," and send this information to the server via their device.

[0363] Step 7:

[0364] The server analyzes the collected feedback and, if necessary, reuses the generated AI model to adjust the brain function training program. This process provides insights into which elements of the feedback should be improved, which are then incorporated into the next program generation. The server sends these results to the terminal and distributes program update information.

[0365] (Application Example 2)

[0366] 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."

[0367] Providing appropriate customer service to each individual customer in a physical store is not easy. Individualized service tailored to each customer's emotions and health condition is required, but traditional customer service methods may not adequately address this. This invention aims to improve the quality of lean-inclusive customer service by utilizing customer health data and emotions.

[0368] 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.

[0369] In this invention, the server includes a collection means for collecting health data, a generation means for generating a personalized brain function improvement program based on the collected health data and sentiment analysis, a presentation means for presenting the generated brain function improvement program, an evaluation means for engaging in natural language dialogue with the user via a dialogue device and evaluating customer satisfaction, and an adjustment means for adjusting the service provision method based on the evaluation results. This enables appropriate customer service for each individual customer in a physical store.

[0370] "Health data" refers to information about the user's physical condition and vital signs, including heart rate, blood pressure, body temperature, and stress level.

[0371] "Emotion analysis" is a technology that identifies and profiles a user's emotional state from their facial expressions and voice.

[0372] A "brain function improvement program" refers to a personalized training and activity plan designed to improve a user's cognitive abilities and mental health.

[0373] "Presentation means" refers to methods or devices for visually or audibly showing a generated program or information to a user.

[0374] A "dialogue device" refers to a device or system that enables natural language communication with a user, receiving user input and providing a corresponding response.

[0375] "Evaluation methods" refer to methods and systems for evaluating the effectiveness of a service and customer satisfaction based on natural language dialogue with users and behavioral data.

[0376] "Adjustment measures" refer to methods and algorithms for modifying the content of services and programs provided based on evaluation results, thereby enhancing individualized support.

[0377] The system implementing this invention is centered around a server that collects health data and analyzes it to understand the customer's unique emotions and state. Health data is acquired from smart devices (e.g., smartwatches and fitness trackers). The collected data is managed by the server, and facial recognition and emotion analysis are performed using OpenCV and TensorFlow technologies. This creates a user emotion profile.

[0378] Subsequently, the server uses a generative AI model to generate a personalized brain function enhancement program. This program includes appropriate menus tailored to the user's health condition and emotions, and is delivered to the user through presentation methods, such as smart glasses or a smartphone.

[0379] The dialogue system collects user feedback and training progress in real time. Users can communicate their feelings and requests through natural language input, and the server provides immediate feedback accordingly.

[0380] For example, if a customer is perceived as stressed in a physical store, the system generates a relaxation-focused program and suggests customer service methods aligned with that program to the staff. The server generates a prompt such as, "Please suggest appropriate measures for relaxation."

[0381] This allows users to receive care and services tailored to their individual needs, which is expected to result in a high level of satisfaction.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The device collects the user's health data using various sensors. This data includes heart rate, blood pressure, and body temperature. The device sends this data to a server. By processing these variables, the server can perform a detailed analysis of the user's health status.

[0385] Step 2:

[0386] The server performs image analysis using OpenCV on health data received and facial expression data acquired using smart glasses or smartphone cameras, and then performs emotion analysis using TensorFlow. As a result, a user emotion profile is generated. This profile is updated in real time and recorded on the server.

[0387] Step 3:

[0388] The server inputs health data and emotional profiles into a generating AI model to create a brain function improvement program optimized for the user. The generated program includes training menus such as relaxation, stress reduction, and concentration enhancement. This information is provided to the user through presentation tools based on prompt messages.

[0389] Step 4:

[0390] Users begin activities using the provided program. They input feedback via their device regarding the progress of their activities and changes in their emotions. This feedback is sent to the server and used to evolve and improve individual programs. This ensures that programs are always tailored to the user's needs.

[0391] Step 5:

[0392] The server's dialogue system uses natural language processing to interpret the collected feedback and adjust the program content as needed. During this process, the user receives additional advice and new instructions through dialogue. An example of a prompt might be, "Please tell me how to improve my concentration in the next step."

[0393] 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.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a system that provides personalized brain function training programs by acquiring and analyzing users' health data. This system mainly consists of three components: a server, a terminal, and a user.

[0410] First, the user inputs fitness tracker data and separately provided health information through their device. This information is sent to a server as health status data and stored there. The server uses algorithms to analyze the received data to assess the user's health status and generate a personalized brain function training program. This generated program is tailored to the user's physical and cognitive needs.

[0411] The generated program is provided to the user via a terminal. The user can run the provided program and input the results and feedback into the terminal at any time. This feedback is sent back to the server and used to evaluate the training and adjust the program as needed.

[0412] Furthermore, the system features a dialogue function that utilizes natural language processing, allowing users to check the progress of their work and resolve any questions they may have through conversations with the system via their terminals. The server analyzes this dialogue to gain a deeper understanding of the user's needs and the progress of their work.

[0413] Furthermore, the server monitors the user's health status and, if certain criteria are met or if a need arises, will refer the user to an appropriate medical institution. This procedure is carried out quickly and efficiently, based on the user's consent.

[0414] As a concrete example, consider the case of a 65-year-old male user who uses this system. He provides data on his heart rate, steps, and sleep patterns, and as a result, the server suggests a program of moderate-intensity exercise three times a week and meditation for relaxation. The user starts the activities according to the suggestion and records his progress as feedback on the terminal. As a result, when his heart rate stabilizes and his sleep patterns improve, the server recommends continuing the program and makes adjustments as needed. In this way, the present invention provides a program tailored to the individual user and supports the maintenance and improvement of their health.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The user activates the device and enters personal health data through the application. This data includes steps, heart rate, and sleep data from a fitness tracker, and, if necessary, the results of a recent medical checkup.

[0418] Step 2:

[0419] The device temporarily stores health data entered by the user and transmits it to the server with security in mind. Encryption technology is used for this transmission, ensuring the data is managed securely.

[0420] Step 3:

[0421] The server stores the received health data in a database. After saving, the data analysis engine starts and uses machine learning algorithms to create a health profile of the user.

[0422] Step 4:

[0423] Based on the analysis results, the server generates a personalized brain function training program optimized for the user's needs. This program includes activities that take into account physical and cognitive characteristics.

[0424] Step 5:

[0425] The server sends the generated training program and related health advice to the terminal.

[0426] Step 6:

[0427] The terminal displays programs received from the server to the user and supports reminders for planned implementation and recording of progress.

[0428] Step 7:

[0429] Users incorporate the provided program into their daily lives and input their results and feedback into their device.

[0430] Step 8:

[0431] The device sends the feedback collected from the user to the server, where it stores the data and prepares it for analysis.

[0432] Step 9:

[0433] The server analyzes the feedback data and adjusts the program content as needed. Furthermore, if the collected data indicates that referral to a medical facility is necessary, it considers appropriate measures.

[0434] Step 10:

[0435] The server then sends the adjusted program back to the terminal, providing the user with the latest training and health advice.

[0436] (Example 1)

[0437] 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."

[0438] In modern society, with its busy lifestyles and increasing stress, there is a growing need to continuously manage individuals' health and provide effective training programs. However, traditional methods often involve standardized programs, making it difficult to provide training optimized for each individual's health condition. Furthermore, there is a lack of processes for appropriately adjusting program implementation and for promptly referring and coordinating with medical institutions.

[0439] 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.

[0440] In this invention, the server includes data collection means for collecting the user's physical data, program generation means for analyzing the collected data and generating individually adapted cognitive training programs, and program provision means for providing the generated programs to the user. This enables the provision of personalized health management and training programs.

[0441] "Data collection means" refers to a device or technology that has the function of acquiring a user's physical data and securing the information for use within the system.

[0442] "Program generation means" refers to a device or method for analyzing collected data and constructing a cognitive training program that is most suitable for each individual.

[0443] "Program delivery means" refers to a device or system for presenting a generated cognitive training program to a user and providing it in an usable format.

[0444] "Dialogue evaluation means" refers to a technology or device that has the function of understanding and evaluating the status of program implementation through natural language communication with the user.

[0445] "Medical referral means" refers to the process or procedure of referring users to appropriate medical institutions as needed, based on evaluation results.

[0446] "Guidance provision means" refers to a device or method for providing users with advice on maintaining their health based on analyzed data.

[0447] A "program adaptation means" is a system or device that has the function of adjusting and optimizing a cognitive training program based on feedback collected from users.

[0448] This invention is a system that provides personalized cognitive training programs by collecting and analyzing users' health data. The system mainly consists of a server, terminals, and users, and operates as follows.

[0449] Users input physical data such as heart rate, steps, and sleep patterns into a device equipped with a fitness tracker or dedicated application. The device receives the input data, formats it appropriately, and then sends it to the server. The data is encrypted for security purposes, ensuring privacy protection.

[0450] The server stores the received data and analyzes it using a generative AI model. This model analyzes the data while taking into account the user's past data and information from other similar users to generate an optimal cognitive training program for each user.

[0451] The generated program is sent from the server to the terminal and provided to the user. The user runs the provided cognitive training program and inputs progress and feedback into the terminal. This feedback is sent back to the server and used to evaluate and adjust the training program.

[0452] Furthermore, the terminal is equipped with a natural language processing dialogue function, which allows it to communicate with the user to understand the progress of their training and resolve any questions the user may have. The server analyzes this dialogue and, if necessary, can provide the user with additional health advice or refer them to an appropriate medical institution.

[0453] For example, a 65-year-old male user of this system provides data on his heart rate, steps, and sleep patterns. The server then suggests a program consisting of three moderate-intensity exercise sessions per week and meditation focused on relaxation. The user begins following this program, recording results and feedback on their device. The server then analyzes the program's effectiveness and adjusts the content as needed.

[0454] For example, by inputting the prompt "Generate a personalized cognitive training program based on the health data of a 65-year-old male" into the AI ​​model, it is possible to generate an appropriate program.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1: The user inputs physical data such as heart rate, steps, and sleep patterns through a fitness tracker or dedicated app. This input data includes information indicating the user's activity level, rest status, and health condition. The device collects this data, formats it, and prepares it for transmission to the server. The output is the user's health data in a format that can be sent to the server.

[0457] Step 2: The device sends the formatted user data to the server. The data is encrypted during this process, ensuring data security and user privacy. The input is formatted health data, and the output is data that securely reaches the server.

[0458] Step 3: The server stores the received user health data in the database. This input data is associated with the past data of each user account and stored as historical data. The output is a user-specific dataset stored in the database.

[0459] Step 4: The server analyzes the accumulated data using a generation AI model. It takes user history data as input, performs data processing and calculations, and generates a personalized cognitive training program. The output is a training program based on the user's physical and cognitive needs.

[0460] Step 5: The generated training program is sent from the server to the terminal. The terminal displays this program to the user and provides it in an executable format. The input is the generated program data, and the output is the program information that the user receives and can view.

[0461] Step 6: The user executes the training program instructed from the terminal and inputs activity details and feedback. The input consists of training completion status and subjective feedback, and the output is updated data sent to the server via the terminal.

[0462] Step 7: The server analyzes the received feedback and adjusts the training program as needed. The input is the latest feedback data, and the server performs data analysis and calculations to output the adjusted program.

[0463] (Application Example 1)

[0464] 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."

[0465] In modern society, there is a demand for personalized health management and improved brain function amidst busy daily lives. Furthermore, training at fitness gyms often does not meet the individual needs of users, resulting in insufficient effectiveness. Moreover, real-time feedback and timely advice are necessary for users to understand their own health status and train appropriately. These problems are not adequately addressed by current health management services and fitness facilities.

[0466] 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.

[0467] In this invention, the server includes acquisition means for acquiring health indicator data, generation means for generating an individualized cognitive function training program based on the acquired health indicator data, and display means for displaying feedback in real time using a visual device. This allows users to receive on-the-spot guidance through individualized training, maximizing the effectiveness of the training and enabling efficient management of their health status.

[0468] "Health indicator data" refers to data that indicates the user's physical and physiological state, including information such as heart rate, exercise level, and sleep patterns.

[0469] A "cognitive function training program" is a program of individualized physical and mental exercises designed to improve a user's cognitive abilities.

[0470] "Generating means" refers to elements or methods for formulating an individualized training program based on acquired health indicator data.

[0471] "Evaluation means" refers to elements or methods for understanding and evaluating the user's training progress through natural language dialogue.

[0472] "Referral means" refers to elements or methods for referring users to appropriate medical institutions based on evaluation results.

[0473] "Visual devices" are devices used to present information to users visually, and include smart glasses and similar devices.

[0474] This invention constructs a system in which three elements—a server, a terminal, and a user—work in coordination. The following steps are important for implementation.

[0475] First, the fitness tracker or device worn by the user collects and acquires health indicator data such as heart rate, exercise volume, and sleep patterns. This data is transmitted to a server via the device. Based on this acquired data, the server generates a personalized cognitive training program using a dedicated algorithm. This program is tailored to the user's specific health and cognitive needs.

[0476] The generated program is provided to the user through the terminal's visual device. This visual device can include smart glasses or a head-mounted display. This allows the user to receive training programs and feedback in real time.

[0477] Furthermore, the server possesses natural language processing capabilities and interacts with the user through the terminal. This interaction is conducted to evaluate the program's performance and user feedback, and the results are used to refine the program. It also includes a function to provide users with timely health-related advice.

[0478] As a concrete example of use, consider a 65-year-old male user using this system while exercising at a gym. Heart rate data is collected in real time, and based on that data, a moderate-intensity exercise program is displayed on a visual device. Also, when the user asks about their progress, the server uses a generative AI model to analyze the prompt text and provide appropriate feedback.

[0479] An example of a prompt message is, "Based on this user's heart rate and exercise history, suggest the next exercise to perform. This is for men aged 55 and over who require moderate-intensity exercise." In this way, this invention can provide users with appropriate programs tailored to their individual health conditions and needs, maximizing training results.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] Users collect health indicator data such as heart rate, exercise level, and sleep patterns using fitness trackers or devices. The collected data is sent from the device to the server. In this step, health indicator data is entered, and the device processes and sends that entered data to the server.

[0483] Step 2:

[0484] The server performs data analysis based on the received health indicator data. This analysis uses algorithms to assess the user's health status and cognitive function needs, and generates a personalized cognitive training program. The input is health indicator data, and the output is a personalized training program. Specifically, the server uses a generation AI model to optimize the program to be generated.

[0485] Step 3:

[0486] The generated cognitive training program is displayed on a visual device via a terminal. The user checks and performs the program using a visual device such as smart glasses. Here, the training program is used as input and output as visual feedback to the visual device. The terminal processes the program to present to the user in real time.

[0487] Step 4:

[0488] The user inputs the progress and feedback of the program they have implemented into the terminal. The terminal sends this feedback information to the server. The input is the user's feedback data, and the output is the transmission of the feedback information to the server. The terminal performs the specific actions of collecting the input data from the user and transmitting it.

[0489] Step 5:

[0490] The server analyzes the feedback information and adjusts the generated program as needed. Natural language processing techniques are used for this analysis. The input is feedback information, and the output is the program adjustment as required. The server uses generated prompts to suggest a new program or recommend continuation.

[0491] Step 6:

[0492] Through natural language interaction with the user via the terminal, the server monitors and evaluates the user's training progress. This interaction then provides appropriate health-related advice. The input is the user's dialogue, and the output is health-related feedback and advice. Specifically, a generative AI model is used to generate responses to the prompt "Provide further advice based on this user's feedback."

[0493] 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.

[0494] This invention relates to a system that provides personalized brain function training programs by recognizing the user's health status data and emotions. This system integrates the acquisition of health status data, emotion recognition by an emotion engine, generation of personalized programs, program delivery and adjustment, natural language dialogue, and means of appropriate referral to medical facilities.

[0495] First, users participate in the system by entering their personal health data via their device. This data includes information obtained from fitness trackers and medical checkups. The transmitted data is received by the server and stored in a database for analysis.

[0496] The server performs analysis based on health status data and the user's emotions detected by the emotion engine. The emotion engine recognizes emotions through interaction with the user and facial expression analysis using the camera, and creates an emotion profile. Using this information, the server generates a brain function training program that is appropriate for the user's current state.

[0497] The generated program is provided to the user via a terminal and includes a feedback function that responds to the user's emotions. The user performs the presented program and inputs feedback on progress and emotions into the system via the terminal. This feedback is sent to a server and used to re-evaluate and adjust the personalized program.

[0498] Furthermore, the natural language dialogue function responds to user feedback and questions in real time, adjusting the content and tone of the dialogue using information from the emotion engine. This allows users to receive more empathetic and appropriate feedback.

[0499] For example, consider a 70-year-old female user of this system. When the emotion engine detects that this user is feeling down and stressed, the server generates a relaxation-focused program and provides it along with advice to reduce stress. If the user continues to follow the program and improvements are seen, the server makes appropriate adjustments and proposes a new program.

[0500] Thus, the present invention aims to provide appropriate care and support by comprehensively considering the user's health and emotions.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The user activates the device and retrieves health data (e.g., steps, heart rate, sleep data) from a fitness tracker, then inputs or syncs it to the device.

[0504] Step 2:

[0505] The device sends health status data entered or synchronized by the user to the server. This data is transmitted using a secure communication protocol.

[0506] Step 3:

[0507] The server stores the received health data in a database and uses that data to initiate analysis using machine learning algorithms. This analysis creates a basic health profile of the user.

[0508] Step 4:

[0509] The device activates an emotion engine to recognize the user's current emotions. The emotion engine uses facial recognition via the camera and analysis of voice tone to obtain the user's emotions.

[0510] Step 5:

[0511] The server integrates analyzed health data with user emotion data recognized by the emotion engine to generate a personalized brain function training program.

[0512] Step 6:

[0513] The server sends the generated program and associated health advice to the terminal. The content provided is tailored to the user's health status and emotional state.

[0514] Step 7:

[0515] The terminal displays programs and advice received from the server to the user. It also generates notifications and reminders to encourage the user to implement the plan.

[0516] Step 8:

[0517] Users complete the provided program and input feedback on the results and their own feelings into the device.

[0518] Step 9:

[0519] The device sends user feedback data to the server. This feedback data also includes information about changes in emotions.

[0520] Step 10:

[0521] The server analyzes the feedback data and adjusts the training program and advice as needed. Based on the results, it also evaluates whether a referral to a medical institution is necessary.

[0522] (Example 2)

[0523] 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."

[0524] In modern society, properly managing and improving an individual's health and mental state is a crucial challenge. Traditional approaches have struggled to comprehensively analyze health and emotions and provide individualized training based on that analysis, and have lacked the means to flexibly adjust programs based on feedback. As a result, there has been a problem in providing effective and long-term support that is tailored to individual needs.

[0525] 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.

[0526] In this invention, the server includes acquisition means for acquiring health status information, generation means for generating personalized brain function training based on the acquired health status information and an emotional profile obtained by an emotion recognition means, and provision means for providing the generated brain function training and collecting feedback through natural language dialogue with the user. This makes it possible to provide appropriate and timely health support to individuals.

[0527] "Health status information" refers to data about an individual's physical function and condition obtained from fitness trackers, health checkups, and other sources.

[0528] "Emotion recognition means" refers to technology for generating an emotion profile from a user's facial expressions and voice, and includes algorithms and devices for analyzing specific emotions.

[0529] "Brain function training" refers to a series of activities and exercises aimed at improving a user's cognitive abilities and psychological well-being.

[0530] A "generative AI model" is an algorithm that uses machine learning techniques to generate personalized programs and advice.

[0531] "Feedback" refers to information about changes in emotions or the effectiveness of a program that users provide to the system, and this data is used to adjust the program.

[0532] "Referral methods" refer to a function that suggests visits to specialized facilities or medical institutions based on the user's health status and feedback.

[0533] The system of this invention provides personalized brain function training based on the user's health status information and emotions. Specifically, it begins with the user using a device to input health status information obtained through a fitness tracker or health checkup. This device refers to a mobile information terminal such as a smartphone or tablet. Similarly, the user uses the camera and microphone on the device to activate emotion recognition means. This analyzes the user's facial expressions and voice, and generates an emotion profile.

[0534] The server receives acquired health status information and emotional profiles and securely stores them in a database. Based on this, a generative AI model is activated to generate brain function training optimized for the user. This generative AI model is a machine learning algorithm used to generate personalized programs and advice.

[0535] The generated training program is provided to the user via a terminal. The provided program includes activities that focus on the user's current health and emotions. The user completes this program and sends feedback on changes in emotions and the effectiveness of the training to the server via the terminal. The server analyzes the collected feedback and makes appropriate adjustments to the program.

[0536] For example, if a 70-year-old user is using the system and the emotion recognition system detects that they are experiencing stress, the server will generate a relaxation program specifically designed to reduce stress and provide it to the user's device. Based on feedback after the program is implemented, the program content will be appropriately updated.

[0537] An example of a prompt might be, "If a 70-year-old user is experiencing stress, what relaxation program should be suggested?" The system comprehensively supports the user's health and emotions, enabling the provision of individually optimized care.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] Users input health information obtained from fitness trackers and health checkups via a device. This input includes vital data such as heart rate, sleep duration, and exercise level. The device centralizes this data and formats it for transmission to the server.

[0541] Step 2:

[0542] The device captures the user's facial expressions and voice using its camera and microphone, activating emotion recognition mechanisms. This process collects real-time facial and voice samples from the user. The device analyzes this data and sends it to a server as input to generate emotional profiles such as joy, sadness, and stress.

[0543] Step 3:

[0544] The server receives health status information and emotional profiles sent from the terminal and stores them in a database. The stored data is used as the basis for processing the generative AI model. The server verifies that the data has been received accurately and performs error checking.

[0545] Step 4:

[0546] The server activates a generative AI model based on health status information and emotional profiles to generate a personalized brain function training program for the user. This process involves formulating the optimal program for the user based on past success stories and relevant research data. The generated program is created in a format that allows for adjustments based on user feedback.

[0547] Step 5:

[0548] The device receives the generated brain function training program and displays it to the user as a schedule of specific exercises and activities. The device uses its notification function to send reminders to the user to continue training.

[0549] Step 6:

[0550] Users perform the provided program and input feedback on changes in their emotions and the effectiveness of the training into their device. As specific feedback, users record subjective impressions such as, "After performing the relaxation exercises, I felt an improvement in my stress levels," and send this information to the server via their device.

[0551] Step 7:

[0552] The server analyzes the collected feedback and, if necessary, reuses the generated AI model to adjust the brain function training program. This process provides insights into which elements of the feedback should be improved, which are then incorporated into the next program generation. The server sends these results to the terminal and distributes program update information.

[0553] (Application Example 2)

[0554] 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."

[0555] Providing appropriate customer service to each individual customer in a physical store is not easy. Individualized service tailored to each customer's emotions and health condition is required, but traditional customer service methods may not adequately address this. This invention aims to improve the quality of lean-inclusive customer service by utilizing customer health data and emotions.

[0556] 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.

[0557] In this invention, the server includes a collection means for collecting health data, a generation means for generating a personalized brain function improvement program based on the collected health data and sentiment analysis, a presentation means for presenting the generated brain function improvement program, an evaluation means for engaging in natural language dialogue with the user via a dialogue device and evaluating customer satisfaction, and an adjustment means for adjusting the service provision method based on the evaluation results. This enables appropriate customer service for each individual customer in a physical store.

[0558] "Health data" refers to information about the user's physical condition and vital signs, including heart rate, blood pressure, body temperature, and stress level.

[0559] "Emotion analysis" is a technology that identifies and profiles a user's emotional state from their facial expressions and voice.

[0560] A "brain function improvement program" refers to a personalized training and activity plan designed to improve a user's cognitive abilities and mental health.

[0561] "Presentation means" refers to methods or devices for visually or audibly showing a generated program or information to a user.

[0562] A "dialogue device" refers to a device or system that enables natural language communication with a user, receiving user input and providing a corresponding response.

[0563] "Evaluation methods" refer to methods and systems for evaluating the effectiveness of a service and customer satisfaction based on natural language dialogue with users and behavioral data.

[0564] "Adjustment measures" refer to methods and algorithms for modifying the content of services and programs provided based on evaluation results, thereby enhancing individualized support.

[0565] The system implementing this invention is centered around a server that collects health data and analyzes it to understand the customer's unique emotions and state. Health data is acquired from smart devices (e.g., smartwatches and fitness trackers). The collected data is managed by the server, and facial recognition and emotion analysis are performed using OpenCV and TensorFlow technologies. This creates a user emotion profile.

[0566] Subsequently, the server uses a generative AI model to generate a personalized brain function enhancement program. This program includes appropriate menus tailored to the user's health condition and emotions, and is delivered to the user through presentation methods, such as smart glasses or a smartphone.

[0567] The dialogue system collects user feedback and training progress in real time. Users can communicate their feelings and requests through natural language input, and the server provides immediate feedback accordingly.

[0568] For example, if a customer is perceived as stressed in a physical store, the system generates a relaxation-focused program and suggests customer service methods aligned with that program to the staff. The server generates a prompt such as, "Please suggest appropriate measures for relaxation."

[0569] This allows users to receive care and services tailored to their individual needs, which is expected to result in a high level of satisfaction.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The device collects the user's health data using various sensors. This data includes heart rate, blood pressure, and body temperature. The device sends this data to a server. By processing these variables, the server can perform a detailed analysis of the user's health status.

[0573] Step 2:

[0574] The server performs image analysis using OpenCV on health data received and facial expression data acquired using smart glasses or smartphone cameras, and then performs emotion analysis using TensorFlow. As a result, a user emotion profile is generated. This profile is updated in real time and recorded on the server.

[0575] Step 3:

[0576] The server inputs health data and emotional profiles into a generating AI model to create a brain function improvement program optimized for the user. The generated program includes training menus such as relaxation, stress reduction, and concentration enhancement. This information is provided to the user through presentation tools based on prompt messages.

[0577] Step 4:

[0578] Users begin activities using the provided program. They input feedback via their device regarding the progress of their activities and changes in their emotions. This feedback is sent to the server and used to evolve and improve individual programs. This ensures that programs are always tailored to the user's needs.

[0579] Step 5:

[0580] The server's dialogue system uses natural language processing to interpret the collected feedback and adjust the program content as needed. During this process, the user receives additional advice and new instructions through dialogue. An example of a prompt might be, "Please tell me how to improve my concentration in the next step."

[0581] 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.

[0582] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0583] 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.

[0584] [Fourth Embodiment]

[0585] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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".

[0598] This invention is a system that provides personalized brain function training programs by acquiring and analyzing users' health data. This system mainly consists of three components: a server, a terminal, and a user.

[0599] First, the user inputs fitness tracker data and separately provided health information through their device. This information is sent to a server as health status data and stored there. The server uses algorithms to analyze the received data to assess the user's health status and generate a personalized brain function training program. This generated program is tailored to the user's physical and cognitive needs.

[0600] The generated program is provided to the user via a terminal. The user can run the provided program and input the results and feedback into the terminal at any time. This feedback is sent back to the server and used to evaluate the training and adjust the program as needed.

[0601] Furthermore, the system features a dialogue function that utilizes natural language processing, allowing users to check the progress of their work and resolve any questions they may have through conversations with the system via their terminals. The server analyzes this dialogue to gain a deeper understanding of the user's needs and the progress of their work.

[0602] Furthermore, the server monitors the user's health status and, if certain criteria are met or if a need arises, will refer the user to an appropriate medical institution. This procedure is carried out quickly and efficiently, based on the user's consent.

[0603] As a concrete example, consider the case of a 65-year-old male user who uses this system. He provides data on his heart rate, steps, and sleep patterns, and as a result, the server suggests a program of moderate-intensity exercise three times a week and meditation for relaxation. The user starts the activities according to the suggestion and records his progress as feedback on the terminal. As a result, when his heart rate stabilizes and his sleep patterns improve, the server recommends continuing the program and makes adjustments as needed. In this way, the present invention provides a program tailored to the individual user and supports the maintenance and improvement of their health.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The user activates the device and enters personal health data through the application. This data includes steps, heart rate, and sleep data from a fitness tracker, and, if necessary, the results of a recent medical checkup.

[0607] Step 2:

[0608] The device temporarily stores health data entered by the user and transmits it to the server with security in mind. Encryption technology is used for this transmission, ensuring the data is managed securely.

[0609] Step 3:

[0610] The server stores the received health data in a database. After saving, the data analysis engine starts and uses machine learning algorithms to create a health profile of the user.

[0611] Step 4:

[0612] Based on the analysis results, the server generates a personalized brain function training program optimized for the user's needs. This program includes activities that take into account physical and cognitive characteristics.

[0613] Step 5:

[0614] The server sends the generated training program and related health advice to the terminal.

[0615] Step 6:

[0616] The terminal displays programs received from the server to the user and supports reminders for planned implementation and recording of progress.

[0617] Step 7:

[0618] Users incorporate the provided program into their daily lives and input their results and feedback into their device.

[0619] Step 8:

[0620] The device sends the feedback collected from the user to the server, where it stores the data and prepares it for analysis.

[0621] Step 9:

[0622] The server analyzes the feedback data and adjusts the program content as needed. Furthermore, if the collected data indicates that referral to a medical facility is necessary, it considers appropriate measures.

[0623] Step 10:

[0624] The server then sends the adjusted program back to the terminal, providing the user with the latest training and health advice.

[0625] (Example 1)

[0626] 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".

[0627] In modern society, with its busy lifestyles and increasing stress, there is a growing need to continuously manage individuals' health and provide effective training programs. However, traditional methods often involve standardized programs, making it difficult to provide training optimized for each individual's health condition. Furthermore, there is a lack of processes for appropriately adjusting program implementation and for promptly referring and coordinating with medical institutions.

[0628] 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.

[0629] In this invention, the server includes data collection means for collecting the user's physical data, program generation means for analyzing the collected data and generating individually adapted cognitive training programs, and program provision means for providing the generated programs to the user. This enables the provision of personalized health management and training programs.

[0630] "Data collection means" refers to a device or technology that has the function of acquiring a user's physical data and securing the information for use within the system.

[0631] "Program generation means" refers to a device or method for analyzing collected data and constructing a cognitive training program that is most suitable for each individual.

[0632] "Program delivery means" refers to a device or system for presenting a generated cognitive training program to a user and providing it in an usable format.

[0633] "Dialogue evaluation means" refers to a technology or device that has the function of understanding and evaluating the status of program implementation through natural language communication with the user.

[0634] "Medical referral means" refers to the process or procedure of referring users to appropriate medical institutions as needed, based on evaluation results.

[0635] "Guidance provision means" refers to a device or method for providing users with advice on maintaining their health based on analyzed data.

[0636] A "program adaptation means" is a system or device that has the function of adjusting and optimizing a cognitive training program based on feedback collected from users.

[0637] This invention is a system that provides personalized cognitive training programs by collecting and analyzing users' health data. The system mainly consists of a server, terminals, and users, and operates as follows.

[0638] Users input physical data such as heart rate, steps, and sleep patterns into a device equipped with a fitness tracker or dedicated application. The device receives the input data, formats it appropriately, and then sends it to the server. The data is encrypted for security purposes, ensuring privacy protection.

[0639] The server stores the received data and analyzes it using a generative AI model. This model analyzes the data while taking into account the user's past data and information from other similar users to generate an optimal cognitive training program for each user.

[0640] The generated program is sent from the server to the terminal and provided to the user. The user runs the provided cognitive training program and inputs progress and feedback into the terminal. This feedback is sent back to the server and used to evaluate and adjust the training program.

[0641] Furthermore, the terminal is equipped with a natural language processing dialogue function, which allows it to communicate with the user to understand the progress of their training and resolve any questions the user may have. The server analyzes this dialogue and, if necessary, can provide the user with additional health advice or refer them to an appropriate medical institution.

[0642] For example, a 65-year-old male user of this system provides data on his heart rate, steps, and sleep patterns. The server then suggests a program consisting of three moderate-intensity exercise sessions per week and meditation focused on relaxation. The user begins following this program, recording results and feedback on their device. The server then analyzes the program's effectiveness and adjusts the content as needed.

[0643] For example, by inputting the prompt "Generate a personalized cognitive training program based on the health data of a 65-year-old male" into the AI ​​model, it is possible to generate an appropriate program.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1: The user inputs physical data such as heart rate, steps, and sleep patterns through a fitness tracker or dedicated app. This input data includes information indicating the user's activity level, rest status, and health condition. The device collects this data, formats it, and prepares it for transmission to the server. The output is the user's health data in a format that can be sent to the server.

[0646] Step 2: The device sends the formatted user data to the server. The data is encrypted during this process, ensuring data security and user privacy. The input is formatted health data, and the output is data that securely reaches the server.

[0647] Step 3: The server stores the received user health data in the database. This input data is associated with the past data of each user account and stored as historical data. The output is a user-specific dataset stored in the database.

[0648] Step 4: The server analyzes the accumulated data using a generation AI model. It takes user history data as input, performs data processing and calculations, and generates a personalized cognitive training program. The output is a training program based on the user's physical and cognitive needs.

[0649] Step 5: The generated training program is sent from the server to the terminal. The terminal displays this program to the user and provides it in an executable format. The input is the generated program data, and the output is the program information that the user receives and can view.

[0650] Step 6: The user executes the training program instructed from the terminal and inputs activity details and feedback. The input consists of training completion status and subjective feedback, and the output is updated data sent to the server via the terminal.

[0651] Step 7: The server analyzes the received feedback and adjusts the training program as needed. The input is the latest feedback data, and the server performs data analysis and calculations to output the adjusted program.

[0652] (Application Example 1)

[0653] 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".

[0654] In modern society, there is a demand for personalized health management and improved brain function amidst busy daily lives. Furthermore, training at fitness gyms often does not meet the individual needs of users, resulting in insufficient effectiveness. Moreover, real-time feedback and timely advice are necessary for users to understand their own health status and train appropriately. These problems are not adequately addressed by current health management services and fitness facilities.

[0655] 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.

[0656] In this invention, the server includes acquisition means for acquiring health indicator data, generation means for generating an individualized cognitive function training program based on the acquired health indicator data, and display means for displaying feedback in real time using a visual device. This allows users to receive on-the-spot guidance through individualized training, maximizing the effectiveness of the training and enabling efficient management of their health status.

[0657] "Health indicator data" refers to data that indicates the user's physical and physiological state, including information such as heart rate, exercise level, and sleep patterns.

[0658] A "cognitive function training program" is a program of individualized physical and mental exercises designed to improve a user's cognitive abilities.

[0659] "Generating means" refers to elements or methods for formulating an individualized training program based on acquired health indicator data.

[0660] "Evaluation means" refers to elements or methods for understanding and evaluating the user's training progress through natural language dialogue.

[0661] "Referral means" refers to elements or methods for referring users to appropriate medical institutions based on evaluation results.

[0662] "Visual devices" are devices used to present information to users visually, and include smart glasses and similar devices.

[0663] This invention constructs a system in which three elements—a server, a terminal, and a user—work in coordination. The following steps are important for implementation.

[0664] First, the fitness tracker or device worn by the user collects and acquires health indicator data such as heart rate, exercise volume, and sleep patterns. This data is transmitted to a server via the device. Based on this acquired data, the server generates a personalized cognitive training program using a dedicated algorithm. This program is tailored to the user's specific health and cognitive needs.

[0665] The generated program is provided to the user through the terminal's visual device. This visual device can include smart glasses or a head-mounted display. This allows the user to receive training programs and feedback in real time.

[0666] Furthermore, the server possesses natural language processing capabilities and interacts with the user through the terminal. This interaction is conducted to evaluate the program's performance and user feedback, and the results are used to refine the program. It also includes a function to provide users with timely health-related advice.

[0667] As a concrete example of use, consider a 65-year-old male user using this system while exercising at a gym. Heart rate data is collected in real time, and based on that data, a moderate-intensity exercise program is displayed on a visual device. Also, when the user asks about their progress, the server uses a generative AI model to analyze the prompt text and provide appropriate feedback.

[0668] An example of a prompt message is, "Based on this user's heart rate and exercise history, suggest the next exercise to perform. This is for men aged 55 and over who require moderate-intensity exercise." In this way, this invention can provide users with appropriate programs tailored to their individual health conditions and needs, maximizing training results.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] Users collect health indicator data such as heart rate, exercise level, and sleep patterns using fitness trackers or devices. The collected data is sent from the device to the server. In this step, health indicator data is entered, and the device processes and sends that entered data to the server.

[0672] Step 2:

[0673] The server performs data analysis based on the received health indicator data. This analysis uses algorithms to assess the user's health status and cognitive function needs, and generates a personalized cognitive training program. The input is health indicator data, and the output is a personalized training program. Specifically, the server uses a generation AI model to optimize the program to be generated.

[0674] Step 3:

[0675] The generated cognitive training program is displayed on a visual device via a terminal. The user checks and performs the program using a visual device such as smart glasses. Here, the training program is used as input and output as visual feedback to the visual device. The terminal processes the program to present to the user in real time.

[0676] Step 4:

[0677] The user inputs the progress and feedback of the program they have implemented into the terminal. The terminal sends this feedback information to the server. The input is the user's feedback data, and the output is the transmission of the feedback information to the server. The terminal performs the specific actions of collecting the input data from the user and transmitting it.

[0678] Step 5:

[0679] The server analyzes the feedback information and adjusts the generated program as needed. Natural language processing techniques are used for this analysis. The input is feedback information, and the output is the program adjustment as required. The server uses generated prompts to suggest a new program or recommend continuation.

[0680] Step 6:

[0681] Through natural language interaction with the user via the terminal, the server monitors and evaluates the user's training progress. This interaction then provides appropriate health-related advice. The input is the user's dialogue, and the output is health-related feedback and advice. Specifically, a generative AI model is used to generate responses to the prompt "Provide further advice based on this user's feedback."

[0682] 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.

[0683] This invention relates to a system that provides personalized brain function training programs by recognizing the user's health status data and emotions. This system integrates the acquisition of health status data, emotion recognition by an emotion engine, generation of personalized programs, program delivery and adjustment, natural language dialogue, and means of appropriate referral to medical facilities.

[0684] First, users participate in the system by entering their personal health data via their device. This data includes information obtained from fitness trackers and medical checkups. The transmitted data is received by the server and stored in a database for analysis.

[0685] The server performs analysis based on health status data and the user's emotions detected by the emotion engine. The emotion engine recognizes emotions through interaction with the user and facial expression analysis using the camera, and creates an emotion profile. Using this information, the server generates a brain function training program that is appropriate for the user's current state.

[0686] The generated program is provided to the user via a terminal and includes a feedback function that responds to the user's emotions. The user performs the presented program and inputs feedback on progress and emotions into the system via the terminal. This feedback is sent to a server and used to re-evaluate and adjust the personalized program.

[0687] Furthermore, the natural language dialogue function responds to user feedback and questions in real time, adjusting the content and tone of the dialogue using information from the emotion engine. This allows users to receive more empathetic and appropriate feedback.

[0688] For example, consider a 70-year-old female user of this system. When the emotion engine detects that this user is feeling down and stressed, the server generates a relaxation-focused program and provides it along with advice to reduce stress. If the user continues to follow the program and improvements are seen, the server makes appropriate adjustments and proposes a new program.

[0689] Thus, the present invention aims to provide appropriate care and support by comprehensively considering the user's health and emotions.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The user activates the device and retrieves health data (e.g., steps, heart rate, sleep data) from a fitness tracker, then inputs or syncs it to the device.

[0693] Step 2:

[0694] The device sends health status data entered or synchronized by the user to the server. This data is transmitted using a secure communication protocol.

[0695] Step 3:

[0696] The server stores the received health data in a database and uses that data to initiate analysis using machine learning algorithms. This analysis creates a basic health profile of the user.

[0697] Step 4:

[0698] The device activates an emotion engine to recognize the user's current emotions. The emotion engine uses facial recognition via the camera and analysis of voice tone to obtain the user's emotions.

[0699] Step 5:

[0700] The server integrates analyzed health data with user emotion data recognized by the emotion engine to generate a personalized brain function training program.

[0701] Step 6:

[0702] The server sends the generated program and associated health advice to the terminal. The content provided is tailored to the user's health status and emotional state.

[0703] Step 7:

[0704] The terminal displays programs and advice received from the server to the user. It also generates notifications and reminders to encourage the user to implement the plan.

[0705] Step 8:

[0706] Users complete the provided program and input feedback on the results and their own feelings into the device.

[0707] Step 9:

[0708] The device sends user feedback data to the server. This feedback data also includes information about changes in emotions.

[0709] Step 10:

[0710] The server analyzes the feedback data and adjusts the training program and advice as needed. Based on the results, it also evaluates whether a referral to a medical institution is necessary.

[0711] (Example 2)

[0712] 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".

[0713] In modern society, properly managing and improving an individual's health and mental state is a crucial challenge. Traditional approaches have struggled to comprehensively analyze health and emotions and provide individualized training based on that analysis, and have lacked the means to flexibly adjust programs based on feedback. As a result, there has been a problem in providing effective and long-term support that is tailored to individual needs.

[0714] 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.

[0715] In this invention, the server includes acquisition means for acquiring health status information, generation means for generating personalized brain function training based on the acquired health status information and an emotional profile obtained by an emotion recognition means, and provision means for providing the generated brain function training and collecting feedback through natural language dialogue with the user. This makes it possible to provide appropriate and timely health support to individuals.

[0716] "Health status information" refers to data about an individual's physical function and condition obtained from fitness trackers, health checkups, and other sources.

[0717] "Emotion recognition means" refers to technology for generating an emotion profile from a user's facial expressions and voice, and includes algorithms and devices for analyzing specific emotions.

[0718] "Brain function training" refers to a series of activities and exercises aimed at improving a user's cognitive abilities and psychological well-being.

[0719] A "generative AI model" is an algorithm that uses machine learning techniques to generate personalized programs and advice.

[0720] "Feedback" refers to information about changes in emotions or the effectiveness of a program that users provide to the system, and this data is used to adjust the program.

[0721] "Referral methods" refer to a function that suggests visits to specialized facilities or medical institutions based on the user's health status and feedback.

[0722] The system of this invention provides personalized brain function training based on the user's health status information and emotions. Specifically, it begins with the user using a device to input health status information obtained through a fitness tracker or health checkup. This device refers to a mobile information terminal such as a smartphone or tablet. Similarly, the user uses the camera and microphone on the device to activate emotion recognition means. This analyzes the user's facial expressions and voice, and generates an emotion profile.

[0723] The server receives acquired health status information and emotional profiles and securely stores them in a database. Based on this, a generative AI model is activated to generate brain function training optimized for the user. This generative AI model is a machine learning algorithm used to generate personalized programs and advice.

[0724] The generated training program is provided to the user via a terminal. The provided program includes activities that focus on the user's current health and emotions. The user completes this program and sends feedback on changes in emotions and the effectiveness of the training to the server via the terminal. The server analyzes the collected feedback and makes appropriate adjustments to the program.

[0725] For example, if a 70-year-old user is using the system and the emotion recognition system detects that they are experiencing stress, the server will generate a relaxation program specifically designed to reduce stress and provide it to the user's device. Based on feedback after the program is implemented, the program content will be appropriately updated.

[0726] An example of a prompt might be, "If a 70-year-old user is experiencing stress, what relaxation program should be suggested?" The system comprehensively supports the user's health and emotions, enabling the provision of individually optimized care.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] Users input health information obtained from fitness trackers and health checkups via a device. This input includes vital data such as heart rate, sleep duration, and exercise level. The device centralizes this data and formats it for transmission to the server.

[0730] Step 2:

[0731] The device captures the user's facial expressions and voice using its camera and microphone, activating emotion recognition mechanisms. This process collects real-time facial and voice samples from the user. The device analyzes this data and sends it to a server as input to generate emotional profiles such as joy, sadness, and stress.

[0732] Step 3:

[0733] The server receives health status information and emotional profiles sent from the terminal and stores them in a database. The stored data is used as the basis for processing the generative AI model. The server verifies that the data has been received accurately and performs error checking.

[0734] Step 4:

[0735] The server activates a generative AI model based on health status information and emotional profiles to generate a personalized brain function training program for the user. This process involves formulating the optimal program for the user based on past success stories and relevant research data. The generated program is created in a format that allows for adjustments based on user feedback.

[0736] Step 5:

[0737] The device receives the generated brain function training program and displays it to the user as a schedule of specific exercises and activities. The device uses its notification function to send reminders to the user to continue training.

[0738] Step 6:

[0739] Users perform the provided program and input feedback on changes in their emotions and the effectiveness of the training into their device. As specific feedback, users record subjective impressions such as, "After performing the relaxation exercises, I felt an improvement in my stress levels," and send this information to the server via their device.

[0740] Step 7:

[0741] The server analyzes the collected feedback and, if necessary, reuses the generated AI model to adjust the brain function training program. This process provides insights into which elements of the feedback should be improved, which are then incorporated into the next program generation. The server sends these results to the terminal and distributes program update information.

[0742] (Application Example 2)

[0743] 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".

[0744] Providing appropriate customer service to each individual customer in a physical store is not easy. Individualized service tailored to each customer's emotions and health condition is required, but traditional customer service methods may not adequately address this. This invention aims to improve the quality of lean-inclusive customer service by utilizing customer health data and emotions.

[0745] 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.

[0746] In this invention, the server includes a collection means for collecting health data, a generation means for generating a personalized brain function improvement program based on the collected health data and sentiment analysis, a presentation means for presenting the generated brain function improvement program, an evaluation means for engaging in natural language dialogue with the user via a dialogue device and evaluating customer satisfaction, and an adjustment means for adjusting the service provision method based on the evaluation results. This enables appropriate customer service for each individual customer in a physical store.

[0747] "Health data" refers to information about the user's physical condition and vital signs, including heart rate, blood pressure, body temperature, and stress level.

[0748] "Emotion analysis" is a technology that identifies and profiles a user's emotional state from their facial expressions and voice.

[0749] A "brain function improvement program" refers to a personalized training and activity plan designed to improve a user's cognitive abilities and mental health.

[0750] "Presentation means" refers to methods or devices for visually or audibly showing a generated program or information to a user.

[0751] A "dialogue device" refers to a device or system that enables natural language communication with a user, receiving user input and providing a corresponding response.

[0752] "Evaluation methods" refer to methods and systems for evaluating the effectiveness of a service and customer satisfaction based on natural language dialogue with users and behavioral data.

[0753] "Adjustment measures" refer to methods and algorithms for modifying the content of services and programs provided based on evaluation results, thereby enhancing individualized support.

[0754] The system implementing this invention is centered around a server that collects health data and analyzes it to understand the customer's unique emotions and state. Health data is acquired from smart devices (e.g., smartwatches and fitness trackers). The collected data is managed by the server, and facial recognition and emotion analysis are performed using OpenCV and TensorFlow technologies. This creates a user emotion profile.

[0755] Subsequently, the server uses a generative AI model to generate a personalized brain function enhancement program. This program includes appropriate menus tailored to the user's health condition and emotions, and is delivered to the user through presentation methods, such as smart glasses or a smartphone.

[0756] The dialogue system collects user feedback and training progress in real time. Users can communicate their feelings and requests through natural language input, and the server provides immediate feedback accordingly.

[0757] For example, if a customer is perceived as stressed in a physical store, the system generates a relaxation-focused program and suggests customer service methods aligned with that program to the staff. The server generates a prompt such as, "Please suggest appropriate measures for relaxation."

[0758] This allows users to receive care and services tailored to their individual needs, which is expected to result in a high level of satisfaction.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The device collects the user's health data using various sensors. This data includes heart rate, blood pressure, and body temperature. The device sends this data to a server. By processing these variables, the server can perform a detailed analysis of the user's health status.

[0762] Step 2:

[0763] The server performs image analysis using OpenCV on health data received and facial expression data acquired using smart glasses or smartphone cameras, and then performs emotion analysis using TensorFlow. As a result, a user emotion profile is generated. This profile is updated in real time and recorded on the server.

[0764] Step 3:

[0765] The server inputs health data and emotional profiles into a generating AI model to create a brain function improvement program optimized for the user. The generated program includes training menus such as relaxation, stress reduction, and concentration enhancement. This information is provided to the user through presentation tools based on prompt messages.

[0766] Step 4:

[0767] Users begin activities using the provided program. They input feedback via their device regarding the progress of their activities and changes in their emotions. This feedback is sent to the server and used to evolve and improve individual programs. This ensures that programs are always tailored to the user's needs.

[0768] Step 5:

[0769] The server's dialogue system uses natural language processing to interpret the collected feedback and adjust the program content as needed. During this process, the user receives additional advice and new instructions through dialogue. An example of a prompt might be, "Please tell me how to improve my concentration in the next step."

[0770] 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.

[0771] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] A means of acquiring health status data,

[0794] A generation means for generating an individualized brain function training program based on acquired health status data,

[0795] A means of providing a generated brain function training program,

[0796] An evaluation method for evaluating the training implementation status by engaging in natural language dialogue with the user,

[0797] A referral method that provides referrals to medical facilities based on evaluation results,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, comprising an advisory means for providing health-related advice to the user based on an analysis of acquired health status data.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising an adjustment means for collecting user feedback and adjusting the brain function training program based on the collected feedback.

[0803] "Example 1"

[0804] (Claim 1)

[0805] A data collection method for collecting users' physical data,

[0806] A program generation means that analyzes collected data and generates individually adapted cognitive training programs,

[0807] A program provisioning means for providing the generated program to the user,

[0808] A dialogue evaluation method for understanding the program execution status and evaluating the results through natural language interaction with the user,

[0809] A medical referral system to introduce medical institutions when necessary based on the evaluation results,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising a means for providing guidance to the user regarding health promotion based on analyzed data.

[0813] (Claim 3)

[0814] The system according to claim 1, comprising a program adaptation means for collecting user feedback and adapting a cognitive training program based on this feedback.

[0815] "Application Example 1"

[0816] (Claim 1)

[0817] Methods for obtaining health indicator data,

[0818] A generation means for generating an individualized cognitive function training program based on acquired health indicator data,

[0819] A means of providing a generated cognitive function training program,

[0820] An evaluation method for assessing the status of training implementation through natural language dialogue with the user,

[0821] Referral methods that involve referring patients to medical institutions based on evaluation results,

[0822] A display means that displays feedback in real time using a visual device,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, comprising a means for providing health-related guidance to the user based on the analysis of acquired health indicator data.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising adjustment means for collecting user responses and adjusting the cognitive function training program based on the collected responses.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] Means for obtaining health status information,

[0831] A generation means that generates personalized brain function training based on acquired health status information and an emotional profile obtained by an emotion recognition means,

[0832] A means of providing generated brain function training and collecting feedback through natural language dialogue with the user,

[0833] A means of adjusting brain function training based on collected feedback,

[0834] An advisory tool that proposes health-related advice to users,

[0835] A referral system that provides referrals to specialized facilities when it is determined that a referral is necessary,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which optimizes interaction with the user using natural language processing and adjusts the content of the interaction using information from emotion recognition means.

[0839] (Claim 3)

[0840] The system according to claim 1, which uses a generative AI model to optimize brain function training based on collected health status information and emotional profiles.

[0841] "Application example 2 when combining with an emotional engine"

[0842] (Claim 1)

[0843] Methods for collecting health data,

[0844] A generation means for generating a personalized brain function improvement program based on collected health data and emotional analysis,

[0845] A presentation means for presenting a generated brain function improvement program,

[0846] An evaluation method for evaluating customer satisfaction by conducting natural language dialogue with users via a dialogue device,

[0847] A means of adjusting the service delivery method based on the evaluation results,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising a response generation means for generating and automatically displaying a response that corresponds to the user's emotions.

[0851] (Claim 3)

[0852] The system according to claim 1, comprising emotion analysis means for analyzing a customer's facial expressions and voice and forming an emotion profile. [Explanation of Symbols]

[0853] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring health status data, A generation means for generating an individualized brain function training program based on acquired health status data, A means of providing a generated brain function training program, An evaluation method for evaluating the training implementation status by engaging in natural language dialogue with the user, A referral method that provides referrals to medical facilities based on evaluation results, A system that includes this.

2. The system according to claim 1, comprising an advisory means for providing health-related advice to the user based on an analysis of acquired health status data.

3. The system according to claim 1, further comprising an adjustment means for collecting user feedback and adjusting the brain function training program based on the collected feedback.

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