System

Abstract

A system is provided.SOLUTION: This system includes a means for registering and managing the information of an aged person, a means for generating an individual rehabilitation menu on the basis of the registered information of the aged person, a means for generating and displaying a conversation relating to rehabilitation by using generated AI in a day unit, a means for recording and analyzing the log of interaction, and a means for monitoring the progress of the aged person and adjusting the rehabilitation menu.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many elderly people who require support or nursing care lack motivation to undergo rehabilitation or do not understand the need for rehabilitation, which prevents them from receiving the rehabilitation they need, leading to social problems such as a decline in QOL and ADL, and even increased social insurance premiums and reduced tax revenues. To solve this problem, effective methods are needed to help elderly people deepen their understanding of rehabilitation and maintain their motivation. [Means for solving the problem]

[0005] The present invention provides a system for maintaining motivation and enhancing the effectiveness of rehabilitation for elderly people. The system includes the following means:

[0006] The system includes a means for registering and managing information about elderly people, a means for generating individual rehabilitation menus based on the registered information about elderly people, a means for generating and displaying rehabilitation conversations on a daily basis using generation AI, a means for recording and analyzing dialogue logs, and a means for monitoring the progress of elderly people and adjusting the rehabilitation menu.

[0007] Furthermore, the effectiveness of rehabilitation is enhanced by displaying the generated rehabilitation menu on the terminal and receiving feedback from the elderly person or caregiver. Also, by including a means for adjusting and displaying the next daily conversation content based on the analysis results of the dialogue log, rehabilitation suited to the individual situation of each elderly person is promoted.

[0008] Below are definitions of important terms included in the claims.

[0009] "Elderly people" refers to people who are older and require assistance or care.

[0010] "Information" refers to personal data such as the senior's name, age, health status, and rehabilitation goals.

[0011] "Management" refers to the process of recording information about the elderly and retrieving, amending, and storing it as needed.

[0012] A "rehabilitation menu" refers to the specific rehabilitation content and exercises proposed to elderly people.

[0013] "Generative AI" is a technology that uses artificial intelligence to automatically generate conversations and questions related to rehabilitation.

[0014] A "conversation" is a text exchange between an elderly person and the generative AI, including questions, answers, and encouraging messages.

[0015] A "log" is recorded data of operations and conversations that take place within the system.

[0016] "Analysis" refers to the process of analyzing collected log data to find trends and patterns.

[0017] "Progress" is an indicator that measures the extent to which an elderly person has achieved the rehabilitation menu.

[0018] "Monitoring" refers to the process of regularly observing and recording an elderly person's progress in their rehabilitation program.

[0019] "Adjustment" refers to modifying the rehabilitation menu based on rehabilitation progress and feedback.

[0020] "Terminal" refers to a device such as a computer or tablet that allows users to input information and display rehabilitation menus and AI-generated conversations.

[0021] "System" refers to a set of software and hardware that work together to implement all of the above means and processes.

[0022] "Feedback" refers to opinions and impressions about the rehabilitation menu that users provide to the system via their terminals.

[0023] "Next daily conversation content" refers to the specific content of the next rehabilitation conversation that the generation AI will have. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] The following system is proposed as a specific embodiment for carrying out the present invention.

[0046] Program Overview

[0047] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[0048] 1. Registering and managing information on elderly people

[0049] 2. Creating rehabilitation menus based on individual motivations

[0050] 3. Generating daily conversations with interactive AI

[0051] 4. Recording and analyzing conversation logs

[0052] 5. Progress monitoring and rehabilitation adjustment

[0053] A natural language description of the program's operation

[0054] 1. Registering and managing information on elderly people

[0055] Terminal: Displays an input form for registering elderly information (including, for example, "name," "age," "health status," and "rehabilitation goals").

[0056] User: Enter the necessary information into the input form. For example, the name is "Yamada Taro," the age is "75 years old," the health condition is "currently undergoing rehabilitation for right knee," and the rehabilitation goal is "playing in the park with grandchildren."

[0057] Terminal: Sends the entered information to the server.

[0058] Server: The received information is stored in a database and used for subsequent dialogue and rehabilitation menu generation.

[0059] 2. Creating rehabilitation menus based on individual motivations

[0060] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[0061] Server: Runs the algorithm that generates the rehabilitation menu and creates a specific rehabilitation menu based on the elderly person's motivation (e.g., "playing in the park with my grandchildren").

[0062] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[0063] 3. Generating daily conversations with interactive AI

[0064] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[0065] Device: Display the generated question or encouraging message. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[0066] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0067] Terminal: Sends the entered answer to the server.

[0068] 4. Recording and analyzing conversation logs

[0069] Server: Records daily interaction logs in a database.

[0070] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0071] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0072] 5. Progress monitoring and rehabilitation adjustment

[0073] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0074] Server: Based on progress data, review the rehabilitation menu and adjust as necessary.

[0075] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0076] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0077] The above is a description of the embodiment of the rehabilitation promotion system. This system is designed to enable individualized rehabilitation promotion according to the motivation and progress of each elderly person, and to achieve sustainable rehabilitation effects.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0081] Step 2:

[0082] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0083] Step 3:

[0084] Terminal: Sends the entered information to the server.

[0085] Step 4:

[0086] Server: Stores the received information in a database.

[0087] Step 5:

[0088] Server: Obtains information about the elderly from the database.

[0089] Step 6:

[0090] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[0091] Step 7:

[0092] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0093] Step 8:

[0094] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[0095] Step 9:

[0096] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0097] Step 10:

[0098] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0099] Step 11:

[0100] Terminal: Sends the entered answer to the server.

[0101] Step 12:

[0102] Server: Records conversation logs in a database.

[0103] Step 13:

[0104] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0105] Step 14:

[0106] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0107] Step 15:

[0108] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0109] Step 16:

[0110] Server: Review rehabilitation menu based on progress data and adjust as necessary.

[0111] Step 17:

[0112] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0113] Step 18:

[0114] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0115] Step 19:

[0116] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[0117] The above are the specific processing steps for executing the program in this system. The system can automatically adjust the rehabilitation menu to suit the progress of each elderly person and maintain their motivation.

[0118] Example 1

[0119] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] In elderly rehabilitation, providing appropriate rehabilitation menus for each elderly person and maintaining their motivation are difficult challenges. It is also necessary to properly monitor rehabilitation progress and adjust rehabilitation menus in real time. Conventional methods lacked methods for solving these challenges efficiently and effectively.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0122] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for inputting elderly information into an input form, means for displaying the generated rehabilitation menu on a terminal, and means for calling a generation AI model to generate questions. This makes it possible to provide an appropriate rehabilitation menu for each elderly person, maintain their motivation, and adjust the rehabilitation menu in real time according to their progress.

[0123] "Elderly information" is personal profile data including the elderly person's name, age, health status, rehabilitation goals, etc.

[0124] "Information registration and management means" refers to a combination of hardware and software for collecting information about seniors, storing it in a database, and accessing and updating it as needed.

[0125] The "means for generating a rehabilitation menu" refers to an algorithm that creates an individually appropriate rehabilitation program based on information about the elderly person, and the environment in which it is executed.

[0126] "Generative AI" is an artificial intelligence (AI) model that automatically generates dialogue and questions, and generally utilizes machine learning and natural language processing technologies.

[0127] "Daily generation method" refers to the process of creating new rehabilitation conversations using generative AI on a regular basis every day.

[0128] The "means for recording and analyzing dialogue logs" refers to software and a database for saving the content of conversations with elderly people and analyzing that data.

[0129] "Means for monitoring progress and adjusting rehabilitation menus" refers to a system for regularly checking the rehabilitation status of elderly people and appropriately adjusting the rehabilitation program based on the results.

[0130] An "input form" is a screen or interface that runs on a computer or mobile device and allows seniors to enter their information.

[0131] "Devices" refer to computers and mobile devices used by seniors and their caregivers.

[0132] "Means for calling a generative AI model to generate questions" refers to the process of inputting the rehabilitation progress of an elderly person into a generative AI model and generating questions based on that information.

[0133] MODE FOR CARRYING OUT THE INVENTION

[0134] This invention is a system for efficiently and effectively supporting elderly rehabilitation. This system has the function of registering and managing elderly information, generating individual rehabilitation menus, conducting daily dialogues using AI, analyzing the logs, and adjusting the rehabilitation menu according to the elderly's progress.

[0135] Hardware and software used

[0136] Hardware

[0137] Devices: Includes computers and mobile devices (e.g., tablets and smartphones) used by seniors and their caregivers.

[0138] Server: Includes a high-performance server (e.g., a cloud-based server) for managing and processing the entire system.

[0139] software

[0140] Database management system: RDBMS such as MySQL or MongoDB is used to store and manage information and conversation logs of the elderly.

[0141] Rehabilitation menu generation algorithm: An algorithm that generates an individual rehabilitation menu based on the elderly person's motivation and health condition.

[0142] Generative AI models: Includes natural language generation models (e.g., GPT-3) to generate rehabilitation questions and conversations on a daily basis.

[0143] Interaction log analysis algorithm: A machine learning algorithm to analyze collected interaction logs and evaluate motivation and progress.

[0144] Example of a system

[0145] 1. Registering and managing information on elderly people

[0146] Terminal: Displays an input interface for the user to enter information into a form, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0147] User: For example, enter "Yamada Taro" as the name, "75 years old" as the age, "currently undergoing rehabilitation for right knee" as the health condition, and "playing in the park with grandchildren" as the rehabilitation goal.

[0148] Terminal: Sends the entered data to the server.

[0149] Server: Stores the received data in a database.

[0150] 2. Creating rehabilitation menus based on individual motivations

[0151] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[0152] Server: Uses a rehabilitation menu generation algorithm to create a rehabilitation menu based on the motivations of specific elderly people.

[0153] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[0154] 3. Generating daily conversations with interactive AI

[0155] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[0156] Terminal: Displays the generated questions and encouraging messages to the user. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[0157] User: For example, "Today I completed 3 sets of 10 knee bends and straightens."

[0158] Terminal: Sends the user's answer to the server.

[0159] 4. Recording and analyzing conversation logs

[0160] Server: Records daily interaction logs in a database.

[0161] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0162] Terminal: Display a feedback message, for example, "Great job! Try a few more things next time."

[0163] 5. Progress monitoring and rehabilitation adjustment

[0164] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[0165] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0166] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[0167] Example prompt sentence:

[0168] To help you move forward with your rehabilitation, please let us know your progress: How much rehabilitation have you done today?

[0169] With the above configuration, this system enables rehabilitation to be effectively carried out according to the individual needs of the elderly, achieving lasting rehabilitation effects.

[0170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0171] Processing step details

[0172] Step 1: Registering the elderly person's information

[0173] Terminal: Displays an elderly person information registration form to the user, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0174] Input: Information about the elderly person (e.g., name "Yamada Taro", age "75 years old", health condition "currently undergoing rehabilitation for right knee", rehabilitation goal "playing in the park with grandchildren").

[0175] Output: The data in a format that sends the information entered by the user to the server.

[0176] User: Enters the required information into the form.

[0177] Terminal: Sends the entered information to the server.

[0178] Specific operation: When you click the submit button, the input content is encoded in JSON format or similar and sent to the server as an HTTP request.

[0179] Step 2: Save your information

[0180] Server: Stores the received information of the elderly in a database.

[0181] Input: Elderly person's information data sent from the terminal.

[0182] Output: The records stored in the database.

[0183] Specific behavior: Establishes a database connection and executes an INSERT statement to persist the information.

[0184] Step 3: Creating a rehabilitation menu

[0185] Server: Retrieves information about elderly people from the database and analyzes their motivation for rehabilitation.

[0186] Input: Elderly information obtained from the database.

[0187] Output: Individual rehabilitation menu.

[0188] Specific actions: The program automatically generates a menu based on the motivation. For example, if the motivation is "playing in the park with my grandchildren," the program will generate "10 knee bends, 3 sets."

[0189] Server: Sends the generated rehabilitation menu to the terminal.

[0190] Terminal: Displays the rehabilitation menu to the user.

[0191] Specific operation: Display the received data in HTML or in the app interface.

[0192] Step 4: Generative AI interaction

[0193] Server: Calls the generative AI model and generates questions to check daily rehabilitation progress.

[0194] Input: A specific prompt (e.g., Hello Yamada-san, how much progress have you made in your rehabilitation today?).

[0195] Output: Questions or messages generated by the generative AI.

[0196] Specific behavior: Calls the AI ​​API to send prompts, receives the generated results, and formats them.

[0197] Terminal: displays the generated question to the user.

[0198] User: Answers questions with text.

[0199] Input: User response (e.g., I completed 3 sets of 10 knee bends and straightens today).

[0200] Terminal: Sends the answer to the server.

[0201] Step 5: Record and analyze conversation logs

[0202] Server: Records the received user answers in a database.

[0203] Input: User response data.

[0204] Output: Interaction logs stored in a database.

[0205] Specific operation: Executes an INSERT statement in the database to persist the log.

[0206] Server: Analyzes the dialogue log data and evaluates the user's motivation and rehabilitation progress.

[0207] Input: Saved interaction log data.

[0208] Output: Analysis results (e.g. motivation rating, feedback messages).

[0209] What it does: Runs analytical algorithms to perform pattern recognition and statistical analysis.

[0210] On the device: Display feedback to the user based on the analysis results (e.g., "Great job! Try a few more things next time").

[0211] Output: Display of feedback message.

[0212] Step 6: Monitoring progress and adjusting rehabilitation

[0213] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[0214] Input: Historical rehabilitation progress data.

[0215] Output: Adjusted rehabilitation menu.

[0216] Specific actions: Perform weekly analysis and calculate new menus.

[0217] Terminal: Notifies the user of new rehabilitation menus and adjustments.

[0218] Specific behavior: Uses notifications to display new content to the user.

[0219] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[0220] Input: Feedback on rehabilitation content.

[0221] (Application example 1)

[0222] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0223] This invention relates to a rehabilitation support system for elderly workers. The challenge is to promote the continuation of elderly rehabilitation and to provide effective rehabilitation menus that correspond to the individual conditions and goals. In particular, to improve the rehabilitation effect of elderly workers in workplaces such as factories, it is important to maintain motivation and monitor progress, but conventional systems have not been able to adequately address these issues.

[0224] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0225] In this invention, the server includes a means for registering and managing information about elderly people, a means for generating individual rehabilitation menus based on the registered information about elderly people, a means for generating and displaying rehabilitation conversations on a daily basis using generation AI, a means for recording and analyzing dialogue logs, a means for monitoring the elderly person's progress and adjusting the rehabilitation menu, and a display means using a wearable device for displaying the rehabilitation menu and progress information. This makes it possible to provide rehabilitation menus tailored to the individual conditions and goals of elderly workers, maintaining and improving their motivation, and providing optimal rehabilitation support according to their progress.

[0226] "Elderly person information" refers to personal data such as the name, age, health status, and rehabilitation goals of the elderly person being rehabilitated.

[0227] "Registration and management means" refers to systems and methods that allow elderly people's information to be entered, stored, and accessed as needed.

[0228] The "means for generating an individual rehabilitation menu" refers to an algorithm or program that proposes and creates optimal rehabilitation activities based on the individual information of the elderly person.

[0229] "Generative AI" refers to artificial intelligence technology that generates natural language and dialogue, and is a model used in particular to generate conversations related to rehabilitation.

[0230] "Means for recording and analyzing dialogue logs" refers to a method or system for registering generated conversations and user responses in a database and analyzing the data.

[0231] "Progress monitoring measures" are systems or methods for regularly checking the progress of elderly people's rehabilitation and evaluating the results.

[0232] "Means for adjusting the rehabilitation menu" refers to an algorithm or system that updates the rehabilitation menu according to progress and adjusts it to an appropriate level of difficulty and content.

[0233] "Display means using wearable devices" refers to a method of displaying rehabilitation menus and progress information using devices such as smart glasses and head-mounted displays.

[0234] We will now describe an example of how this invention can be implemented. This is a system designed to support the rehabilitation of elderly workers in factories. The system includes a sensor device, a data management system, an interactive generative AI model, and a wearable device that displays a rehabilitation menu.

[0235] First, a smartphone or tablet is used as a device to register and manage information about users (elderly workers). Users enter necessary information such as their name, age, health status, and rehabilitation goals into these devices, and send it to a server. The server stores the received information in a database. This database can use cloud data storage (for example, DynamoDB from Amazon Web Services).

[0236] The server generates an optimal rehabilitation menu for the elderly worker based on the registered information. For example, an algorithm creates a rehabilitation menu based on the registered health condition and rehabilitation goals, and the menu is sent from the server to a wearable device such as smart glasses. The wearable device visually displays the rehabilitation menu to the user, allowing the user to proceed with the work while checking the rehabilitation menu.

[0237] To check daily rehabilitation progress, the server utilizes a generative AI model (e.g., GPT-3). The server uses this AI model to generate conversations to check daily rehabilitation progress, generating questions such as, "How much rehabilitation progress have you made today?" These questions are displayed on the wearable device, and the user answers via text or voice input. The user's answers are sent back to the server, which records them in a database.

[0238] The server then analyzes the recorded dialogue logs to assess the user's progress and motivation level. For example, it uses data analysis libraries such as pandas and numpy to analyze the user's progress data. Based on the results, it automatically adjusts the rehabilitation menu and optimizes the next day's rehabilitation content. The new rehabilitation menu and adjustments are then sent to the wearable device, and the user is notified.

[0239] For example, you can generate a prompt like this:

[0240] "Hello, how far have you progressed in your rehabilitation today?"

[0241] This system supports the rehabilitation of elderly workers and can provide effective rehabilitation menus tailored to their individual conditions. Furthermore, it automatically monitors daily progress and continuously provides optimal rehabilitation support. This makes it possible to maintain the health of elderly workers and improve their work efficiency.

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] The user enters the information of the elderly worker into the terminal. The user enters the necessary information such as name, age, health condition, and rehabilitation goals into a smartphone or tablet. The input data is sent to the server by pressing the send button.

[0245] Step 2:

[0246] The server then stores the received information about the elderly workers in a database. The data is stored in cloud storage such as AWS DynamoDB, ensuring basic data that can be used to generate future rehabilitation menus and monitor progress.

[0247] Step 3:

[0248] The server generates an individual rehabilitation menu based on the elderly worker's information. For example, if a user needs knee rehabilitation, the algorithm generates "10 knee bending and straightening exercises, 3 sets." The generated menu is sent to the wearable device.

[0249] Step 4:

[0250] The wearable device visually displays the generated rehabilitation menu to the user, who can then check the menu through smart glasses or other devices and follow the instructions to perform the rehabilitation.

[0251] Step 5:

[0252] The server generates daily conversations using a generative AI model to check daily rehabilitation progress. For example, it uses GPT-3 to generate questions such as "How much rehabilitation progress have you made today?" and sends them to the wearable device.

[0253] Step 6:

[0254] The wearable device displays the generated conversational messages to the user, who then answers questions by voice or text, and the responses are sent to the server.

[0255] Step 7:

[0256] The server records the received user responses in a database, which is saved as a dialogue log and later used for progress analysis.

[0257] Step 8:

[0258] The server analyzes the recorded dialogue logs to evaluate the user's progress and motivation. It then analyzes the progress data using a data analysis library (e.g., pandas, numpy) and generates a new rehabilitation menu based on the results.

[0259] Step 9:

[0260] The server then generates new rehabilitation menus and adjustments based on the evaluation results and sends them to the wearable device, providing the user with an optimal menu for effective rehabilitation the next day.

[0261] Step 10:

[0262] The wearable device notifies the user of a new rehabilitation menu, and the user follows the instructions to carry out the next day's rehabilitation and inputs new progress.

[0263] These are the specific processing steps of this system. Each step is designed to seamlessly send and receive data between the user, terminal, and server, and to comprehensively support the rehabilitation of elderly workers.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] As a specific embodiment of the present invention, we propose the following system. This system is designed primarily to effectively promote rehabilitation for the elderly, and incorporates an emotion engine that recognizes the user's emotions, providing more advanced rehabilitation support.

[0266] Program Overview

[0267] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[0268] 1. Registering and managing information on elderly people

[0269] 2. Creating rehabilitation menus based on individual motivations

[0270] 3. Generating daily conversations with interactive AI

[0271] 4. Recording and analyzing conversation logs

[0272] 5. Progress monitoring and rehabilitation adjustment

[0273] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[0274] A natural language description of the program's operation

[0275] 1. Registering and managing information on elderly people

[0276] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0277] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0278] Terminal: Sends the entered information to the server.

[0279] Server: Stores the received information in a database.

[0280] 2. Creating rehabilitation menus based on individual motivations

[0281] Server: Obtains information about the elderly from the database.

[0282] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[0283] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0284] 3. Generating daily conversations with interactive AI

[0285] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[0286] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0287] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0288] Terminal: Sends the entered answer to the server.

[0289] 4. Recording and analyzing conversation logs

[0290] Server: Records daily interaction logs in a database.

[0291] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0292] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0293] 5. Progress monitoring and rehabilitation adjustment

[0294] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0295] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0296] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0297] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0298] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[0299] Server: Calls the emotion engine that analyzes the text and voice input from the user and recognizes the user's emotional state.

[0300] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[0301] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[0302] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[0303] The above is a description of the embodiment of the rehabilitation promotion system. This system can promote rehabilitation individually based on each elderly person's motivation, progress, and even emotional state. It is designed with the aim of realizing continuous and effective rehabilitation support.

[0304] The processing flow will be explained below.

[0305] Step 1:

[0306] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0307] Step 2:

[0308] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0309] Step 3:

[0310] Terminal: Sends the entered information to the server.

[0311] Step 4:

[0312] Server: Stores the received information in a database.

[0313] Step 5:

[0314] Server: Obtains information about the elderly from the database.

[0315] Step 6:

[0316] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[0317] Step 7:

[0318] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0319] Step 8:

[0320] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[0321] Step 9:

[0322] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0323] Step 10:

[0324] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0325] Step 11:

[0326] Terminal: Sends the entered answer to the server.

[0327] Step 12:

[0328] Server: Records conversation logs in a database.

[0329] Step 13:

[0330] Server: Calls the emotion engine and analyzes the user's input text and voice data to recognize the user's emotional state.

[0331] Step 14:

[0332] Server: Records the user's emotional state (e.g., joy, sadness, anger, depression, etc.) recognized by the emotion engine in the dialogue log.

[0333] Step 15:

[0334] Server: Analyzes the dialogue log data and runs algorithms to assess the user's motivation, rehabilitation progress, and emotional state.

[0335] Step 16:

[0336] On the device: Display feedback based on the analysis results. For example, if the user is feeling down, display an encouraging message like, "Today was difficult, but you did well!"

[0337] Step 17:

[0338] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0339] Step 18:

[0340] Server: Adjust the rehabilitation menu based on the progress data and the user's emotional state. For example, if the achievement rate is low and the user is feeling depressed, reduce the rehabilitation menu.

[0341] Step 19:

[0342] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0343] Step 20:

[0344] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0345] Step 21:

[0346] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[0347] The above are the specific processing steps for executing the program in this system. Taking into account the progress and emotional state of each elderly person, the system can automatically adjust appropriate rehabilitation programs and maintain motivation.

[0348] Example 2

[0349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0350] With conventional rehabilitation systems, it was difficult to customize rehabilitation according to the motivation and emotional state of each elderly person, making it difficult to maintain the elderly's motivation. Furthermore, when providing effective rehabilitation menus and managing progress, automatic adjustments to meet individual needs were not sufficiently performed. This led to the issue of elderly people losing motivation to continue rehabilitation.

[0351] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0352] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, and means for recognizing the elderly's emotional state using an emotion recognition engine and adjusting the rehabilitation conversation content based on the recognized emotional state. This makes it possible to provide an effective rehabilitation menu and manage progress according to the elderly's individual needs and emotional state while maintaining their motivation for rehabilitation.

[0353] "Elderly person information" refers to personal information such as the elderly person's name, age, health status, and rehabilitation goals.

[0354] A "rehabilitation menu" refers to a customized exercise and activity program based on an elderly person's health condition and rehabilitation goals.

[0355] "Generative AI" refers to an artificial intelligence model that performs natural language generation and is used to generate rehabilitation questions and conversations.

[0356] "Dialogue logs" refer to data that records daily conversations with the generating AI and responses from the elderly.

[0357] "Progress monitoring" refers to the process of regularly observing and evaluating the progress made by older adults through rehabilitation.

[0358] An "emotion recognition engine" refers to a software system that analyzes and recognizes the emotional state of elderly people from their input text or voice.

[0359] "Feedback" refers to the evaluation and encouraging messages given to elderly people based on the results and progress of their rehabilitation program.

[0360] As a specific embodiment of this invention, we propose a rehabilitation support system that effectively supports elderly rehabilitation and maintains their motivation by registering user information, generating daily conversations using a generative AI model, and monitoring progress.

[0361] Key components and their roles

[0362] 1. Registering and managing information on elderly people

[0363] Terminal: Display an input form for elderly person information registration. A software form containing input fields such as "name," "age," "health status," and "rehabilitation goals" is used.

[0364] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0365] Terminal: Sends the entered information to the server.

[0366] Server: Stores the received information in a database.

[0367] 2. Creating rehabilitation menus based on individual motivations

[0368] Server: Obtains information about the elderly from the database.

[0369] Server: Runs the algorithm that generates the rehabilitation menu. This algorithm includes the ability to automatically generate a customized menu based on the user's motivation (e.g., "playing in the park with my grandchildren").

[0370] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0371] 3. Generating daily conversations with interactive AI

[0372] Server: Uses a generative AI model (e.g., OpenAI GPT-4) to generate questions related to elderly rehabilitation.

[0373] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0374] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0375] Terminal: Sends the entered answer to the server.

[0376] 4. Recording and analyzing conversation logs

[0377] Server: Records daily interaction logs in a database.

[0378] Server: Analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress, for example, using the Python Pandas library.

[0379] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0380] 5. Progress monitoring and rehabilitation adjustment

[0381] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0382] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0383] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0384] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[0385] Server: Calls an emotion engine (e.g., IBM Watson's Tone Analyzer) to analyze the text and voice input from the user and recognize the user's emotional state.

[0386] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[0387] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[0388] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[0389] This system will provide rehabilitation support tailored to the individual needs and emotional state of the elderly, thereby achieving sustainable and effective rehabilitation. As a concrete example, the following prompt sentences are provided:

[0390] Example prompt sentence:

[0391] "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[0392] This system makes it easier for elderly people to maintain motivation for daily rehabilitation, allowing them to proceed with rehabilitation more effectively.

[0393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0394] System processing flow

[0395] Step 1: Enter and register the senior citizen's information

[0396] Input: The user enters their information into an input form.

[0397] Terminal: Displays an input form for registering elderly information. Input fields include "name," "age," "health status," and "rehabilitation goals."

[0398] User: Enter information in the above fields. For example, enter "Name: Taro Yamada", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0399] Specific operation: When the user clicks the "Submit" button on the input form, the terminal sends this information to the server.

[0400] Output: The entered information is sent to the server and stored.

[0401] Server: Saves the received information in a database and generates a message confirming the save.

[0402] Specific operation: After the data is successfully saved to the database, the server returns a confirmation message to the terminal saying "The information has been saved successfully."

[0403] Step 2: Creating an individual rehabilitation menu

[0404] Input: Information about the elderly stored in a database.

[0405] Server: Obtains information about the elderly from the database.

[0406] Server: Runs the algorithm that generates the rehabilitation menu and generates a rehabilitation menu based on the acquired information and motivation (e.g., "playing in the park with my grandchildren").

[0407] Specific actions: Use Python scripts as algorithms to design rehabilitation menus.

[0408] Output: The generated rehabilitation menu.

[0409] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0410] Specific operation: A rehabilitation menu will be displayed on the device screen, and the user can check it.

[0411] Step 3: Generate daily rehabilitation conversations

[0412] Input: Elderly information and rehabilitation menu.

[0413] Server: Calls a generative AI model (e.g., OpenAI GPT-4) and generates questions related to elderly rehabilitation.

[0414] An example of a specific prompt sentence: The server generates "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[0415] Output: Generated rehabilitation questions.

[0416] Terminal: Display the generated question.

[0417] Specific behavior: A screen is launched that displays a question to the user.

[0418] Step 4: User answers and submits

[0419] Input: The generated question and the user's text answer.

[0420] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0421] Specific behavior: The user enters an answer and clicks the "Submit" button.

[0422] Output: The user's text response is sent to the server.

[0423] Terminal: Sends the user's answer to the server.

[0424] Step 5: Record and analyze conversation logs

[0425] Input: User response data.

[0426] Server: Records the user's answers in a database.

[0427] Server: Analyzes daily interaction log data and runs algorithms to evaluate user motivation and rehabilitation progress, using, for example, the Python Pandas library.

[0428] Specific operation: The server stores the dialogue log in a database and analyzes the progress data.

[0429] Output: Feedback based on the analysis results.

[0430] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0431] Step 6: Progress monitoring and rehabilitation adjustment

[0432] Input: Accumulated progress data.

[0433] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0434] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0435] What it does: It periodically runs a progress check script and generates menu adjustment results.

[0436] Output: New rehab menu after adjustments.

[0437] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0438] Specific operation: A new rehabilitation menu will be displayed on the device and the user will confirm it.

[0439] Step 7: Emotion-aware conversation adjustment

[0440] Input: User input text and voice data.

[0441] Server: Calls an emotion recognition engine (e.g., IBM Watson's Tone Analyzer) to analyze the user's emotional state.

[0442] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[0443] Specific operation: Uses the analysis results to provide prompts to the generative AI and adjust the conversation content.

[0444] Output: The adjusted conversation.

[0445] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[0446] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[0447] Specific behavior: The adjusted message is displayed on the screen and recorded as a conversation log for the user.

[0448] (Application example 2)

[0449] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0450] Improving the effectiveness of elderly rehabilitation requires personalized responses tailored to each individual's condition and emotions. However, current rehabilitation systems have difficulty understanding individual situations and emotions in real time and providing appropriate rehabilitation programs based on that understanding. Furthermore, due to a lack of rehabilitation support for elderly people using self-driving cars, there is no monitoring of rehabilitation status or real-time support while traveling. This poses a significant challenge that significantly impacts the rehabilitation progress and motivation of elderly people.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for recognizing the elderly's emotional state using an emotion engine and optimizing the conversation content and rehabilitation menu, means for displaying the rehabilitation menu in real time on an in-vehicle display when the elderly uses a self-driving car, and means for collecting the elderly's rehabilitation status and impressions through voice input. This enables appropriate rehabilitation support based on the elderly's condition and emotions in real time.

[0452] The "Means for registering and managing information on elderly people" is part of a system that records and stores basic information about elderly people, their health status, rehabilitation goals, etc. in a database, and has the function of accessing and editing that information as needed.

[0453] The "means for generating an individual rehabilitation menu" is part of the system that executes an algorithm that automatically creates a rehabilitation plan tailored to the individual needs and goals of the registered elderly person based on their information.

[0454] The "means for generating rehabilitation conversations using generative AI" is part of a system that uses a generative AI model to automatically generate and display rehabilitation questions and conversations suitable for elderly people on a daily basis.

[0455] The "means for recording and analyzing dialogue logs" is part of a system for recording dialogues with elderly people and analyzing them to evaluate their rehabilitation progress and motivation.

[0456] The "means for monitoring the elderly person's progress and adjusting the rehabilitation menu" is part of a system that continuously monitors the elderly person's rehabilitation progress and dynamically changes and optimizes the rehabilitation menu according to that progress.

[0457] The "means for recognizing emotional states using an emotion engine" is part of a system that has the function of analyzing input text and voice data of elderly people, recognizing their emotional states, and reflecting this in rehabilitation support.

[0458] The "in-vehicle display means for self-driving vehicles" is part of a system that displays rehabilitation menus and related information in real time on the in-vehicle display when elderly people use self-driving vehicles.

[0459] The "means for collecting status through voice input" is part of a system that allows elderly people to input their rehabilitation status and impressions via voice in self-driving cars, etc., and collect that data.

[0460] As an embodiment of the present invention, we propose an elderly rehabilitation support system that is installed in an autonomous vehicle. This system has multiple functions for effectively carrying out rehabilitation within the autonomous vehicle.

[0461] Program Overview

[0462] The system includes the following main features:

[0463] 1. Registering and managing information on elderly people

[0464] 2. Creating an individual rehabilitation menu

[0465] 3. Generating Conversations About Rehabilitation Using Generative AI

[0466] 4. Recording and analyzing conversation logs

[0467] 5. Progress monitoring and rehabilitation adjustment

[0468] 6. Emotional state recognition and reflection using an emotion engine

[0469] 7. Displaying rehabilitation menus on an in-vehicle display

[0470] 8. Collecting rehabilitation status information through voice input

[0471] How the system is implemented

[0472] 1. Registering and managing information on elderly people

[0473] Device: An input form for elderly information is displayed on the in-car display or smartphone. Input fields include "name," "age," "health status," and "rehabilitation goals."

[0474] User: Enter the required information into the input form. Example: "Name: Hanako Suzuki", "Age: 76", "Health condition: Currently undergoing rehabilitation for lower back pain", "Rehabilitation goal: Enjoy traveling with grandchildren"

[0475] Terminal: Sends the entered information to the server.

[0476] Server: Stores the received information in a database.

[0477] 2. Creating an individual rehabilitation menu

[0478] Server: Obtains information about the elderly from the database.

[0479] Server: Executes the algorithm for generating a rehabilitation menu and creates a rehabilitation menu based on the elderly person's goals.

[0480] Terminal: Display the generated rehabilitation menu on the in-car display. Example: "Waist stretching exercises: 5 minutes, 2 sets."

[0481] 3. Generating Conversations About Rehabilitation Using Generative AI

[0482] Server: Calls a generative AI model (e.g., GPT-4) and generates questions related to elderly rehabilitation.

[0483] Terminal: Display the generated question on the display. Example: "Hello, Hanako. How much progress did you make in your rehabilitation today?"

[0484] User: Answers questions verbally.

[0485] Terminal: The input voice data is sent to the server and converted into text using voice recognition software.

[0486] 4. Recording and analyzing conversation logs

[0487] Server: Records daily interaction logs in a database.

[0488] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0489] 5. Progress monitoring and rehabilitation adjustment

[0490] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0491] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0492] Device: Notifies the new rehabilitation menu and adjustment details on the in-car display. Example: "Low back stretching exercises, 5 minutes, 3 sets."

[0493] 6. Emotional state recognition and reflection using an emotion engine

[0494] Server: Calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state.

[0495] Server: Based on the recognized emotional state, the generative AI adjusts the next rehabilitation conversation. For example, if the user is depressed, it will send more encouraging messages.

[0496] Device: Display the adjusted conversation on the in-car display. Example: "Today was tough, but you did well!"

[0497] 7. Displaying rehabilitation menus on an in-vehicle display

[0498] Terminal: When elderly people use self-driving cars, the device provides a function that displays rehabilitation menus and related information in real time on the in-car display.

[0499] 8. Collecting rehabilitation status information through voice input

[0500] Terminal: Collects elderly people's rehabilitation status and impressions through voice input on the in-vehicle display. The voice input data is sent to the server and converted into text format as needed.

[0501] Software and hardware used

[0502] Hardware: In-car display, voice recognition microphone

[0503] Software: Generative AI models (e.g., GPT-4), emotion recognition APIs, speech recognition software (e.g., speech recognition services)

[0504] Prompt Sentence Examples

[0505] "Hello, how much progress have you made in your rehabilitation today?"

[0506] "How are you feeling today? How is your rehabilitation going?"

[0507] This will enable appropriate rehabilitation support based on the elderly person's condition and emotions in real time.

[0508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0509] Step 1:

[0510] Input: Elderly person's information (name, age, health status, rehabilitation goals)

[0511] Operation: The device displays an input form for elderly information on the in-car display or smartphone. The user enters the necessary information into this input form. For example, "Name: Suzuki Hanako," "Age: 76 years old," "Health condition: Currently undergoing rehabilitation for lower back pain," and "Rehabilitation goal: Enjoy traveling with grandchildren."

[0512] Output: The entered information is sent to the server, which stores the received information in a database.

[0513] Step 2:

[0514] Input: Elderly information retrieved from the database

[0515] Operation: The server retrieves information about the elderly person from the database and runs an algorithm to generate a rehabilitation menu based on that information. An individual rehabilitation menu is generated.

[0516] Output: The generated rehabilitation menu is displayed on the terminal (vehicle display). For example, it may say, "Low back stretching exercises, 5 minutes, 2 sets."

[0517] Step 3:

[0518] Input: Elderly rehabilitation menu progress data

[0519] How it works: The server invokes a generative AI model (e.g., GPT-4) to generate questions related to elderly rehabilitation. The device displays the generated questions on the in-car display. For example, it displays "Hello, Hanako. How much progress have you made in your rehabilitation today?"

[0520] Output: The user responds verbally and the audio data is sent to the server.

[0521] Step 4:

[0522] Input: User's voice data

[0523] How it works: The server uses speech recognition software to convert the voice data into text, which is then stored in a database as a conversation log.

[0524] Output: The conversation log is saved and used for the next conversation generation.

[0525] Step 5:

[0526] Input: Interaction log data

[0527] How it works: The server analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress. It generates feedback based on the analysis results.

[0528] Output: The device displays feedback, for example, "Great progress today! Good luck next time."

[0529] Step 6:

[0530] Input: Rehabilitation progress data

[0531] How it works: The server periodically checks the rehabilitation progress data and evaluates the progress on a weekly basis. It adjusts the rehabilitation menu based on the progress data. For example, if the achievement rate is low, the rehabilitation menu is reduced, and if it is high, the menu is strengthened.

[0532] Output: The new rehabilitation menu will be displayed on the device. For example, "Low back stretching exercises: 5 minutes, 3 sets."

[0533] Step 7:

[0534] Input: Elderly voice input data

[0535] How it works: The server calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state. Based on the recognized emotional state, the generative AI adjusts the content of the next rehabilitation conversation.

[0536] Output: The conversational content corresponding to the emotion will be displayed on the device. For example, "Today was tough, but you did well!"

[0537] Step 8:

[0538] Input: Elderly voice input data

[0539] Operation: When an elderly person uses an autonomous vehicle, the device displays rehabilitation menus and related information in real time on the vehicle's display. It also collects the elderly person's rehabilitation status and impressions through voice input.

[0540] Output: The collected data is sent to the server and used to generate the next rehabilitation menu and adjust the conversation content.

[0541] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0542] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0543] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0544] [Second embodiment]

[0545] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0546] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0547] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0548] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0549] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0550] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0551] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0552] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0553] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0555] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0556] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0557] The following system is proposed as a specific embodiment for carrying out the present invention.

[0558] Program Overview

[0559] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[0560] 1. Registering and managing information on elderly people

[0561] 2. Creating rehabilitation menus based on individual motivations

[0562] 3. Generating daily conversations with interactive AI

[0563] 4. Recording and analyzing conversation logs

[0564] 5. Progress monitoring and rehabilitation adjustment

[0565] A natural language description of the program's operation

[0566] 1. Registering and managing information on elderly people

[0567] Terminal: Displays an input form for registering elderly information (including, for example, "name," "age," "health status," and "rehabilitation goals").

[0568] User: Enter the necessary information into the input form. For example, the name is "Yamada Taro," the age is "75 years old," the health condition is "currently undergoing rehabilitation for right knee," and the rehabilitation goal is "playing in the park with grandchildren."

[0569] Terminal: Sends the entered information to the server.

[0570] Server: The received information is stored in a database and used for subsequent dialogue and rehabilitation menu generation.

[0571] 2. Creating rehabilitation menus based on individual motivations

[0572] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[0573] Server: Runs the algorithm that generates the rehabilitation menu and creates a specific rehabilitation menu based on the elderly person's motivation (e.g., "playing in the park with my grandchildren").

[0574] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[0575] 3. Generating daily conversations with interactive AI

[0576] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[0577] Device: Display the generated question or encouraging message. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[0578] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0579] Terminal: Sends the entered answer to the server.

[0580] 4. Recording and analyzing conversation logs

[0581] Server: Records daily interaction logs in a database.

[0582] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0583] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0584] 5. Progress monitoring and rehabilitation adjustment

[0585] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0586] Server: Based on progress data, review the rehabilitation menu and adjust as necessary.

[0587] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0588] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0589] The above is a description of the embodiment of the rehabilitation promotion system. This system is designed to enable individualized rehabilitation promotion according to the motivation and progress of each elderly person, and to achieve sustainable rehabilitation effects.

[0590] The processing flow will be explained below.

[0591] Step 1:

[0592] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0593] Step 2:

[0594] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0595] Step 3:

[0596] Terminal: Sends the entered information to the server.

[0597] Step 4:

[0598] Server: Stores the received information in a database.

[0599] Step 5:

[0600] Server: Obtains information about the elderly from the database.

[0601] Step 6:

[0602] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[0603] Step 7:

[0604] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0605] Step 8:

[0606] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[0607] Step 9:

[0608] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0609] Step 10:

[0610] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0611] Step 11:

[0612] Terminal: Sends the entered answer to the server.

[0613] Step 12:

[0614] Server: Records conversation logs in a database.

[0615] Step 13:

[0616] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0617] Step 14:

[0618] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0619] Step 15:

[0620] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0621] Step 16:

[0622] Server: Review rehabilitation menu based on progress data and adjust as necessary.

[0623] Step 17:

[0624] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0625] Step 18:

[0626] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0627] Step 19:

[0628] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[0629] The above are the specific processing steps for executing the program in this system. The system can automatically adjust the rehabilitation menu to suit the progress of each elderly person and maintain their motivation.

[0630] Example 1

[0631] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] In elderly rehabilitation, providing appropriate rehabilitation menus for each elderly person and maintaining their motivation are difficult challenges. It is also necessary to properly monitor rehabilitation progress and adjust rehabilitation menus in real time. Conventional methods lacked methods for solving these challenges efficiently and effectively.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0634] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for inputting elderly information into an input form, means for displaying the generated rehabilitation menu on a terminal, and means for calling a generation AI model to generate questions. This makes it possible to provide an appropriate rehabilitation menu for each elderly person, maintain their motivation, and adjust the rehabilitation menu in real time according to their progress.

[0635] "Elderly information" is personal profile data including the elderly person's name, age, health status, rehabilitation goals, etc.

[0636] "Information registration and management means" refers to a combination of hardware and software for collecting information about seniors, storing it in a database, and accessing and updating it as needed.

[0637] The "means for generating a rehabilitation menu" refers to an algorithm that creates an individually appropriate rehabilitation program based on information about the elderly person, and the environment in which it is executed.

[0638] "Generative AI" is an artificial intelligence (AI) model that automatically generates dialogue and questions, and generally utilizes machine learning and natural language processing technologies.

[0639] "Daily generation method" refers to the process of creating new rehabilitation conversations using generative AI on a regular basis every day.

[0640] The "means for recording and analyzing dialogue logs" refers to software and a database for saving the content of conversations with elderly people and analyzing that data.

[0641] "Means for monitoring progress and adjusting rehabilitation menus" refers to a system for regularly checking the rehabilitation status of elderly people and appropriately adjusting the rehabilitation program based on the results.

[0642] An "input form" is a screen or interface that runs on a computer or mobile device and allows seniors to enter their information.

[0643] "Devices" refer to computers and mobile devices used by seniors and their caregivers.

[0644] "Means for calling a generative AI model to generate questions" refers to the process of inputting the rehabilitation progress of an elderly person into a generative AI model and generating questions based on that information.

[0645] MODE FOR CARRYING OUT THE INVENTION

[0646] This invention is a system for efficiently and effectively supporting elderly rehabilitation. This system has the function of registering and managing elderly information, generating individual rehabilitation menus, conducting daily dialogues using AI, analyzing the logs, and adjusting the rehabilitation menu according to the elderly's progress.

[0647] Hardware and software used

[0648] Hardware

[0649] Devices: Includes computers and mobile devices (e.g., tablets and smartphones) used by seniors and their caregivers.

[0650] Server: Includes a high-performance server (e.g., a cloud-based server) for managing and processing the entire system.

[0651] software

[0652] Database management system: RDBMS such as MySQL or MongoDB is used to store and manage information and conversation logs of the elderly.

[0653] Rehabilitation menu generation algorithm: An algorithm that generates an individual rehabilitation menu based on the elderly person's motivation and health condition.

[0654] Generative AI models: Includes natural language generation models (e.g., GPT-3) to generate rehabilitation questions and conversations on a daily basis.

[0655] Interaction log analysis algorithm: A machine learning algorithm to analyze collected interaction logs and evaluate motivation and progress.

[0656] Example of a system

[0657] 1. Registering and managing information on elderly people

[0658] Terminal: Displays an input interface for the user to enter information into a form, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0659] User: For example, enter "Yamada Taro" as the name, "75 years old" as the age, "currently undergoing rehabilitation for right knee" as the health condition, and "playing in the park with grandchildren" as the rehabilitation goal.

[0660] Terminal: Sends the entered data to the server.

[0661] Server: Stores the received data in a database.

[0662] 2. Creating rehabilitation menus based on individual motivations

[0663] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[0664] Server: Uses a rehabilitation menu generation algorithm to create a rehabilitation menu based on the motivations of specific elderly people.

[0665] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[0666] 3. Generating daily conversations with interactive AI

[0667] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[0668] Terminal: Displays the generated questions and encouraging messages to the user. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[0669] User: For example, "Today I completed 3 sets of 10 knee bends and straightens."

[0670] Terminal: Sends the user's answer to the server.

[0671] 4. Recording and analyzing conversation logs

[0672] Server: Records daily interaction logs in a database.

[0673] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0674] Terminal: Display a feedback message, for example, "Great job! Try a few more things next time."

[0675] 5. Progress monitoring and rehabilitation adjustment

[0676] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[0677] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0678] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[0679] Example prompt sentence:

[0680] To help you move forward with your rehabilitation, please let us know your progress: How much rehabilitation have you done today?

[0681] With the above configuration, this system enables rehabilitation to be effectively carried out according to the individual needs of the elderly, achieving lasting rehabilitation effects.

[0682] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0683] Processing step details

[0684] Step 1: Registering the elderly person's information

[0685] Terminal: Displays an elderly person information registration form to the user, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0686] Input: Information about the elderly person (e.g., name "Yamada Taro", age "75 years old", health condition "currently undergoing rehabilitation for right knee", rehabilitation goal "playing in the park with grandchildren").

[0687] Output: The data in a format that sends the information entered by the user to the server.

[0688] User: Enters the required information into the form.

[0689] Terminal: Sends the entered information to the server.

[0690] Specific operation: When you click the submit button, the input content is encoded in JSON format or similar and sent to the server as an HTTP request.

[0691] Step 2: Save your information

[0692] Server: Stores the received information of the elderly in a database.

[0693] Input: Elderly person's information data sent from the terminal.

[0694] Output: The records stored in the database.

[0695] Specific behavior: Establishes a database connection and executes an INSERT statement to persist the information.

[0696] Step 3: Creating a rehabilitation menu

[0697] Server: Retrieves information about elderly people from the database and analyzes their motivation for rehabilitation.

[0698] Input: Elderly information obtained from the database.

[0699] Output: Individual rehabilitation menu.

[0700] Specific actions: The program automatically generates a menu based on the motivation. For example, if the motivation is "playing in the park with my grandchildren," the program will generate "10 knee bends, 3 sets."

[0701] Server: Sends the generated rehabilitation menu to the terminal.

[0702] Terminal: Displays the rehabilitation menu to the user.

[0703] Specific operation: Display the received data in HTML or in the app interface.

[0704] Step 4: Generative AI interaction

[0705] Server: Calls the generative AI model and generates questions to check daily rehabilitation progress.

[0706] Input: A specific prompt (e.g., Hello Yamada-san, how much progress have you made in your rehabilitation today?).

[0707] Output: Questions or messages generated by the generative AI.

[0708] Specific behavior: Calls the AI ​​API to send prompts, receives the generated results, and formats them.

[0709] Terminal: displays the generated question to the user.

[0710] User: Answers questions with text.

[0711] Input: User response (e.g., I completed 3 sets of 10 knee bends and straightens today).

[0712] Terminal: Sends the answer to the server.

[0713] Step 5: Record and analyze conversation logs

[0714] Server: Records the received user answers in a database.

[0715] Input: User response data.

[0716] Output: Interaction logs stored in a database.

[0717] Specific operation: Executes an INSERT statement in the database to persist the log.

[0718] Server: Analyzes the dialogue log data and evaluates the user's motivation and rehabilitation progress.

[0719] Input: Saved interaction log data.

[0720] Output: Analysis results (e.g. motivation rating, feedback messages).

[0721] What it does: Runs analytical algorithms to perform pattern recognition and statistical analysis.

[0722] On the device: Display feedback to the user based on the analysis results (e.g., "Great job! Try a few more things next time").

[0723] Output: Display of feedback message.

[0724] Step 6: Monitoring progress and adjusting rehabilitation

[0725] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[0726] Input: Historical rehabilitation progress data.

[0727] Output: Adjusted rehabilitation menu.

[0728] Specific actions: Perform weekly analysis and calculate new menus.

[0729] Terminal: Notifies the user of new rehabilitation menus and adjustments.

[0730] Specific behavior: Uses notifications to display new content to the user.

[0731] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[0732] Input: Feedback on rehabilitation content.

[0733] (Application example 1)

[0734] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0735] This invention relates to a rehabilitation support system for elderly workers. The challenge is to promote the continuation of elderly rehabilitation and to provide effective rehabilitation menus that correspond to the individual conditions and goals. In particular, to improve the rehabilitation effect of elderly workers in workplaces such as factories, it is important to maintain motivation and monitor progress, but conventional systems have not been able to adequately address these issues.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0737] In this invention, the server includes a means for registering and managing information about elderly people, a means for generating individual rehabilitation menus based on the registered information about elderly people, a means for generating and displaying rehabilitation conversations on a daily basis using generation AI, a means for recording and analyzing dialogue logs, a means for monitoring the elderly person's progress and adjusting the rehabilitation menu, and a display means using a wearable device for displaying the rehabilitation menu and progress information. This makes it possible to provide rehabilitation menus tailored to the individual conditions and goals of elderly workers, maintaining and improving their motivation, and providing optimal rehabilitation support according to their progress.

[0738] "Elderly person information" refers to personal data such as the name, age, health status, and rehabilitation goals of the elderly person being rehabilitated.

[0739] "Registration and management means" refers to systems and methods that allow elderly people's information to be entered, stored, and accessed as needed.

[0740] The "means for generating an individual rehabilitation menu" refers to an algorithm or program that proposes and creates optimal rehabilitation activities based on the individual information of the elderly person.

[0741] "Generative AI" refers to artificial intelligence technology that generates natural language and dialogue, and is a model used in particular to generate conversations related to rehabilitation.

[0742] "Means for recording and analyzing dialogue logs" refers to a method or system for registering generated conversations and user responses in a database and analyzing the data.

[0743] "Progress monitoring measures" are systems or methods for regularly checking the progress of elderly people's rehabilitation and evaluating the results.

[0744] "Means for adjusting the rehabilitation menu" refers to an algorithm or system that updates the rehabilitation menu according to progress and adjusts it to an appropriate level of difficulty and content.

[0745] "Display means using wearable devices" refers to a method of displaying rehabilitation menus and progress information using devices such as smart glasses and head-mounted displays.

[0746] We will now describe an example of how this invention can be implemented. This is a system designed to support the rehabilitation of elderly workers in factories. The system includes a sensor device, a data management system, an interactive generative AI model, and a wearable device that displays a rehabilitation menu.

[0747] First, a smartphone or tablet is used as a device to register and manage information about users (elderly workers). Users enter necessary information such as their name, age, health status, and rehabilitation goals into these devices, and send it to a server. The server stores the received information in a database. This database can use cloud data storage (for example, DynamoDB from Amazon Web Services).

[0748] The server generates an optimal rehabilitation menu for the elderly worker based on the registered information. For example, an algorithm creates a rehabilitation menu based on the registered health condition and rehabilitation goals, and the menu is sent from the server to a wearable device such as smart glasses. The wearable device visually displays the rehabilitation menu to the user, allowing the user to proceed with the work while checking the rehabilitation menu.

[0749] To check daily rehabilitation progress, the server utilizes a generative AI model (e.g., GPT-3). The server uses this AI model to generate conversations to check daily rehabilitation progress, generating questions such as, "How much rehabilitation progress have you made today?" These questions are displayed on the wearable device, and the user answers via text or voice input. The user's answers are sent back to the server, which records them in a database.

[0750] The server then analyzes the recorded dialogue logs to assess the user's progress and motivation level. For example, it uses data analysis libraries such as pandas and numpy to analyze the user's progress data. Based on the results, it automatically adjusts the rehabilitation menu and optimizes the next day's rehabilitation content. The new rehabilitation menu and adjustments are then sent to the wearable device, and the user is notified.

[0751] For example, you can generate a prompt like this:

[0752] "Hello, how far have you progressed in your rehabilitation today?"

[0753] This system supports the rehabilitation of elderly workers and can provide effective rehabilitation menus tailored to their individual conditions. Furthermore, it automatically monitors daily progress and continuously provides optimal rehabilitation support. This makes it possible to maintain the health of elderly workers and improve their work efficiency.

[0754] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0755] Step 1:

[0756] The user enters the information of the elderly worker into the terminal. The user enters the necessary information such as name, age, health condition, and rehabilitation goals into a smartphone or tablet. The input data is sent to the server by pressing the send button.

[0757] Step 2:

[0758] The server then stores the received information about the elderly workers in a database. The data is stored in cloud storage such as AWS DynamoDB, ensuring basic data that can be used to generate future rehabilitation menus and monitor progress.

[0759] Step 3:

[0760] The server generates an individual rehabilitation menu based on the elderly worker's information. For example, if a user needs knee rehabilitation, the algorithm generates "10 knee bending and straightening exercises, 3 sets." The generated menu is sent to the wearable device.

[0761] Step 4:

[0762] The wearable device visually displays the generated rehabilitation menu to the user, who can then check the menu through smart glasses or other devices and follow the instructions to perform the rehabilitation.

[0763] Step 5:

[0764] The server generates daily conversations using a generative AI model to check daily rehabilitation progress. For example, it uses GPT-3 to generate questions such as "How much rehabilitation progress have you made today?" and sends them to the wearable device.

[0765] Step 6:

[0766] The wearable device displays the generated conversational messages to the user, who then answers questions by voice or text, and the responses are sent to the server.

[0767] Step 7:

[0768] The server records the received user responses in a database, which is saved as a dialogue log and later used for progress analysis.

[0769] Step 8:

[0770] The server analyzes the recorded dialogue logs to evaluate the user's progress and motivation. It then analyzes the progress data using a data analysis library (e.g., pandas, numpy) and generates a new rehabilitation menu based on the results.

[0771] Step 9:

[0772] The server then generates new rehabilitation menus and adjustments based on the evaluation results and sends them to the wearable device, providing the user with an optimal menu for effective rehabilitation the next day.

[0773] Step 10:

[0774] The wearable device notifies the user of a new rehabilitation menu, and the user follows the instructions to carry out the next day's rehabilitation and inputs new progress.

[0775] These are the specific processing steps of this system. Each step is designed to seamlessly send and receive data between the user, terminal, and server, and to comprehensively support the rehabilitation of elderly workers.

[0776] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0777] As a specific embodiment of the present invention, we propose the following system. This system is designed primarily to effectively promote rehabilitation for the elderly, and incorporates an emotion engine that recognizes the user's emotions, providing more advanced rehabilitation support.

[0778] Program Overview

[0779] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[0780] 1. Registering and managing information on elderly people

[0781] 2. Creating rehabilitation menus based on individual motivations

[0782] 3. Generating daily conversations with interactive AI

[0783] 4. Recording and analyzing conversation logs

[0784] 5. Progress monitoring and rehabilitation adjustment

[0785] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[0786] A natural language description of the program's operation

[0787] 1. Registering and managing information on elderly people

[0788] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0789] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0790] Terminal: Sends the entered information to the server.

[0791] Server: Stores the received information in a database.

[0792] 2. Creating rehabilitation menus based on individual motivations

[0793] Server: Obtains information about the elderly from the database.

[0794] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[0795] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0796] 3. Generating daily conversations with interactive AI

[0797] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[0798] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0799] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0800] Terminal: Sends the entered answer to the server.

[0801] 4. Recording and analyzing conversation logs

[0802] Server: Records daily interaction logs in a database.

[0803] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[0804] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0805] 5. Progress monitoring and rehabilitation adjustment

[0806] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0807] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0808] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0809] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0810] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[0811] Server: Calls the emotion engine that analyzes the text and voice input from the user and recognizes the user's emotional state.

[0812] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[0813] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[0814] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[0815] The above is a description of the embodiment of the rehabilitation promotion system. This system can promote rehabilitation individually based on each elderly person's motivation, progress, and even emotional state. It is designed with the aim of realizing continuous and effective rehabilitation support.

[0816] The processing flow will be explained below.

[0817] Step 1:

[0818] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[0819] Step 2:

[0820] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0821] Step 3:

[0822] Terminal: Sends the entered information to the server.

[0823] Step 4:

[0824] Server: Stores the received information in a database.

[0825] Step 5:

[0826] Server: Obtains information about the elderly from the database.

[0827] Step 6:

[0828] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[0829] Step 7:

[0830] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0831] Step 8:

[0832] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[0833] Step 9:

[0834] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0835] Step 10:

[0836] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0837] Step 11:

[0838] Terminal: Sends the entered answer to the server.

[0839] Step 12:

[0840] Server: Records conversation logs in a database.

[0841] Step 13:

[0842] Server: Calls the emotion engine and analyzes the user's input text and voice data to recognize the user's emotional state.

[0843] Step 14:

[0844] Server: Records the user's emotional state (e.g., joy, sadness, anger, depression, etc.) recognized by the emotion engine in the dialogue log.

[0845] Step 15:

[0846] Server: Analyzes the dialogue log data and runs algorithms to assess the user's motivation, rehabilitation progress, and emotional state.

[0847] Step 16:

[0848] On the device: Display feedback based on the analysis results. For example, if the user is feeling down, display an encouraging message like, "Today was difficult, but you did well!"

[0849] Step 17:

[0850] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0851] Step 18:

[0852] Server: Adjust the rehabilitation menu based on the progress data and the user's emotional state. For example, if the achievement rate is low and the user is feeling depressed, reduce the rehabilitation menu.

[0853] Step 19:

[0854] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0855] Step 20:

[0856] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[0857] Step 21:

[0858] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[0859] The above are the specific processing steps for executing the program in this system. Taking into account the progress and emotional state of each elderly person, the system can automatically adjust appropriate rehabilitation programs and maintain motivation.

[0860] Example 2

[0861] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0862] With conventional rehabilitation systems, it was difficult to customize rehabilitation according to the motivation and emotional state of each elderly person, making it difficult to maintain the elderly's motivation. Furthermore, when providing effective rehabilitation menus and managing progress, automatic adjustments to meet individual needs were not sufficiently performed. This led to the issue of elderly people losing motivation to continue rehabilitation.

[0863] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0864] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, and means for recognizing the elderly's emotional state using an emotion recognition engine and adjusting the rehabilitation conversation content based on the recognized emotional state. This makes it possible to provide an effective rehabilitation menu and manage progress according to the elderly's individual needs and emotional state while maintaining their motivation for rehabilitation.

[0865] "Elderly person information" refers to personal information such as the elderly person's name, age, health status, and rehabilitation goals.

[0866] A "rehabilitation menu" refers to a customized exercise and activity program based on an elderly person's health condition and rehabilitation goals.

[0867] "Generative AI" refers to an artificial intelligence model that performs natural language generation and is used to generate rehabilitation questions and conversations.

[0868] "Dialogue logs" refer to data that records daily conversations with the generating AI and responses from the elderly.

[0869] "Progress monitoring" refers to the process of regularly observing and evaluating the progress made by older adults through rehabilitation.

[0870] An "emotion recognition engine" refers to a software system that analyzes and recognizes the emotional state of elderly people from their input text or voice.

[0871] "Feedback" refers to the evaluation and encouraging messages given to elderly people based on the results and progress of their rehabilitation program.

[0872] As a specific embodiment of this invention, we propose a rehabilitation support system that effectively supports elderly rehabilitation and maintains their motivation by registering user information, generating daily conversations using a generative AI model, and monitoring progress.

[0873] Key components and their roles

[0874] 1. Registering and managing information on elderly people

[0875] Terminal: Display an input form for elderly person information registration. A software form containing input fields such as "name," "age," "health status," and "rehabilitation goals" is used.

[0876] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0877] Terminal: Sends the entered information to the server.

[0878] Server: Stores the received information in a database.

[0879] 2. Creating rehabilitation menus based on individual motivations

[0880] Server: Obtains information about the elderly from the database.

[0881] Server: Runs the algorithm that generates the rehabilitation menu. This algorithm includes the ability to automatically generate a customized menu based on the user's motivation (e.g., "playing in the park with my grandchildren").

[0882] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0883] 3. Generating daily conversations with interactive AI

[0884] Server: Uses a generative AI model (e.g., OpenAI GPT-4) to generate questions related to elderly rehabilitation.

[0885] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[0886] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0887] Terminal: Sends the entered answer to the server.

[0888] 4. Recording and analyzing conversation logs

[0889] Server: Records daily interaction logs in a database.

[0890] Server: Analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress, for example, using the Python Pandas library.

[0891] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0892] 5. Progress monitoring and rehabilitation adjustment

[0893] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0894] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0895] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0896] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[0897] Server: Calls an emotion engine (e.g., IBM Watson's Tone Analyzer) to analyze the text and voice input from the user and recognize the user's emotional state.

[0898] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[0899] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[0900] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[0901] This system will provide rehabilitation support tailored to the individual needs and emotional state of the elderly, thereby achieving sustainable and effective rehabilitation. As a concrete example, the following prompt sentences are provided:

[0902] Example prompt sentence:

[0903] "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[0904] This system makes it easier for elderly people to maintain motivation for daily rehabilitation, allowing them to proceed with rehabilitation more effectively.

[0905] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0906] System processing flow

[0907] Step 1: Enter and register the senior citizen's information

[0908] Input: The user enters their information into an input form.

[0909] Terminal: Displays an input form for registering elderly information. Input fields include "name," "age," "health status," and "rehabilitation goals."

[0910] User: Enter information in the above fields. For example, enter "Name: Taro Yamada", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[0911] Specific operation: When the user clicks the "Submit" button on the input form, the terminal sends this information to the server.

[0912] Output: The entered information is sent to the server and stored.

[0913] Server: Saves the received information in a database and generates a message confirming the save.

[0914] Specific operation: After the data is successfully saved to the database, the server returns a confirmation message to the terminal saying "The information has been saved successfully."

[0915] Step 2: Creating an individual rehabilitation menu

[0916] Input: Information about the elderly stored in a database.

[0917] Server: Obtains information about the elderly from the database.

[0918] Server: Runs the algorithm that generates the rehabilitation menu and generates a rehabilitation menu based on the acquired information and motivation (e.g., "playing in the park with my grandchildren").

[0919] Specific actions: Use Python scripts as algorithms to design rehabilitation menus.

[0920] Output: The generated rehabilitation menu.

[0921] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[0922] Specific operation: A rehabilitation menu will be displayed on the device screen, and the user can check it.

[0923] Step 3: Generate daily rehabilitation conversations

[0924] Input: Elderly information and rehabilitation menu.

[0925] Server: Calls a generative AI model (e.g., OpenAI GPT-4) and generates questions related to elderly rehabilitation.

[0926] An example of a specific prompt sentence: The server generates "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[0927] Output: Generated rehabilitation questions.

[0928] Terminal: Display the generated question.

[0929] Specific behavior: A screen is launched that displays a question to the user.

[0930] Step 4: User answers and submits

[0931] Input: The generated question and the user's text answer.

[0932] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[0933] Specific behavior: The user enters an answer and clicks the "Submit" button.

[0934] Output: The user's text response is sent to the server.

[0935] Terminal: Sends the user's answer to the server.

[0936] Step 5: Record and analyze conversation logs

[0937] Input: User response data.

[0938] Server: Records the user's answers in a database.

[0939] Server: Analyzes daily interaction log data and runs algorithms to evaluate user motivation and rehabilitation progress, using, for example, the Python Pandas library.

[0940] Specific operation: The server stores the dialogue log in a database and analyzes the progress data.

[0941] Output: Feedback based on the analysis results.

[0942] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[0943] Step 6: Progress monitoring and rehabilitation adjustment

[0944] Input: Accumulated progress data.

[0945] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[0946] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[0947] What it does: It periodically runs a progress check script and generates menu adjustment results.

[0948] Output: New rehab menu after adjustments.

[0949] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[0950] Specific operation: A new rehabilitation menu will be displayed on the device and the user will confirm it.

[0951] Step 7: Emotion-aware conversation adjustment

[0952] Input: User input text and voice data.

[0953] Server: Calls an emotion recognition engine (e.g., IBM Watson's Tone Analyzer) to analyze the user's emotional state.

[0954] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[0955] Specific operation: Uses the analysis results to provide prompts to the generative AI and adjust the conversation content.

[0956] Output: The adjusted conversation.

[0957] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[0958] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[0959] Specific behavior: The adjusted message is displayed on the screen and recorded as a conversation log for the user.

[0960] (Application example 2)

[0961] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0962] Improving the effectiveness of elderly rehabilitation requires personalized responses tailored to each individual's condition and emotions. However, current rehabilitation systems have difficulty understanding individual situations and emotions in real time and providing appropriate rehabilitation programs based on that understanding. Furthermore, due to a lack of rehabilitation support for elderly people using self-driving cars, there is no monitoring of rehabilitation status or real-time support while traveling. This poses a significant challenge that significantly impacts the rehabilitation progress and motivation of elderly people.

[0963] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for recognizing the elderly's emotional state using an emotion engine and optimizing the conversation content and rehabilitation menu, means for displaying the rehabilitation menu in real time on an in-vehicle display when the elderly uses a self-driving car, and means for collecting the elderly's rehabilitation status and impressions through voice input. This enables appropriate rehabilitation support based on the elderly's condition and emotions in real time.

[0964] The "Means for registering and managing information on elderly people" is part of a system that records and stores basic information about elderly people, their health status, rehabilitation goals, etc. in a database, and has the function of accessing and editing that information as needed.

[0965] The "means for generating an individual rehabilitation menu" is part of the system that executes an algorithm that automatically creates a rehabilitation plan tailored to the individual needs and goals of the registered elderly person based on their information.

[0966] The "means for generating rehabilitation conversations using generative AI" is part of a system that uses a generative AI model to automatically generate and display rehabilitation questions and conversations suitable for elderly people on a daily basis.

[0967] The "means for recording and analyzing dialogue logs" is part of a system for recording dialogues with elderly people and analyzing them to evaluate their rehabilitation progress and motivation.

[0968] The "means for monitoring the elderly person's progress and adjusting the rehabilitation menu" is part of a system that continuously monitors the elderly person's rehabilitation progress and dynamically changes and optimizes the rehabilitation menu according to that progress.

[0969] The "means for recognizing emotional states using an emotion engine" is part of a system that has the function of analyzing input text and voice data of elderly people, recognizing their emotional states, and reflecting this in rehabilitation support.

[0970] The "in-vehicle display means for self-driving vehicles" is part of a system that displays rehabilitation menus and related information in real time on the in-vehicle display when elderly people use self-driving vehicles.

[0971] The "means for collecting status through voice input" is part of a system that allows elderly people to input their rehabilitation status and impressions via voice in self-driving cars, etc., and collect that data.

[0972] As an embodiment of the present invention, we propose an elderly rehabilitation support system that is installed in an autonomous vehicle. This system has multiple functions for effectively carrying out rehabilitation within the autonomous vehicle.

[0973] Program Overview

[0974] The system includes the following main features:

[0975] 1. Registering and managing information on elderly people

[0976] 2. Creating an individual rehabilitation menu

[0977] 3. Generating Conversations About Rehabilitation Using Generative AI

[0978] 4. Recording and analyzing conversation logs

[0979] 5. Progress monitoring and rehabilitation adjustment

[0980] 6. Emotional state recognition and reflection using an emotion engine

[0981] 7. Displaying rehabilitation menus on an in-vehicle display

[0982] 8. Collecting rehabilitation status information through voice input

[0983] How the system is implemented

[0984] 1. Registering and managing information on elderly people

[0985] Device: An input form for elderly information is displayed on the in-car display or smartphone. Input fields include "name," "age," "health status," and "rehabilitation goals."

[0986] User: Enter the required information into the input form. Example: "Name: Hanako Suzuki", "Age: 76", "Health condition: Currently undergoing rehabilitation for lower back pain", "Rehabilitation goal: Enjoy traveling with grandchildren"

[0987] Terminal: Sends the entered information to the server.

[0988] Server: Stores the received information in a database.

[0989] 2. Creating an individual rehabilitation menu

[0990] Server: Obtains information about the elderly from the database.

[0991] Server: Executes the algorithm for generating a rehabilitation menu and creates a rehabilitation menu based on the elderly person's goals.

[0992] Terminal: Display the generated rehabilitation menu on the in-car display. Example: "Waist stretching exercises: 5 minutes, 2 sets."

[0993] 3. Generating Conversations About Rehabilitation Using Generative AI

[0994] Server: Calls a generative AI model (e.g., GPT-4) and generates questions related to elderly rehabilitation.

[0995] Terminal: Display the generated question on the display. Example: "Hello, Hanako. How much progress did you make in your rehabilitation today?"

[0996] User: Answers questions verbally.

[0997] Terminal: The input voice data is sent to the server and converted into text using voice recognition software.

[0998] 4. Recording and analyzing conversation logs

[0999] Server: Records daily interaction logs in a database.

[1000] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1001] 5. Progress monitoring and rehabilitation adjustment

[1002] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1003] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1004] Device: Notifies the new rehabilitation menu and adjustment details on the in-car display. Example: "Low back stretching exercises, 5 minutes, 3 sets."

[1005] 6. Emotional state recognition and reflection using an emotion engine

[1006] Server: Calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state.

[1007] Server: Based on the recognized emotional state, the generative AI adjusts the next rehabilitation conversation. For example, if the user is depressed, it will send more encouraging messages.

[1008] Device: Display the adjusted conversation on the in-car display. Example: "Today was tough, but you did well!"

[1009] 7. Displaying rehabilitation menus on an in-vehicle display

[1010] Terminal: When elderly people use self-driving cars, the device provides a function that displays rehabilitation menus and related information in real time on the in-car display.

[1011] 8. Collecting rehabilitation status information through voice input

[1012] Terminal: Collects elderly people's rehabilitation status and impressions through voice input on the in-vehicle display. The voice input data is sent to the server and converted into text format as needed.

[1013] Software and hardware used

[1014] Hardware: In-car display, voice recognition microphone

[1015] Software: Generative AI models (e.g., GPT-4), emotion recognition APIs, speech recognition software (e.g., speech recognition services)

[1016] Prompt Sentence Examples

[1017] "Hello, how much progress have you made in your rehabilitation today?"

[1018] "How are you feeling today? How is your rehabilitation going?"

[1019] This will enable appropriate rehabilitation support based on the elderly person's condition and emotions in real time.

[1020] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1021] Step 1:

[1022] Input: Elderly person's information (name, age, health status, rehabilitation goals)

[1023] Operation: The device displays an input form for elderly information on the in-car display or smartphone. The user enters the necessary information into this input form. For example, "Name: Suzuki Hanako," "Age: 76 years old," "Health condition: Currently undergoing rehabilitation for lower back pain," and "Rehabilitation goal: Enjoy traveling with grandchildren."

[1024] Output: The entered information is sent to the server, which stores the received information in a database.

[1025] Step 2:

[1026] Input: Elderly information retrieved from the database

[1027] Operation: The server retrieves information about the elderly person from the database and runs an algorithm to generate a rehabilitation menu based on that information. An individual rehabilitation menu is generated.

[1028] Output: The generated rehabilitation menu is displayed on the terminal (vehicle display). For example, it may say, "Low back stretching exercises, 5 minutes, 2 sets."

[1029] Step 3:

[1030] Input: Elderly rehabilitation menu progress data

[1031] How it works: The server invokes a generative AI model (e.g., GPT-4) to generate questions related to elderly rehabilitation. The device displays the generated questions on the in-car display. For example, it displays "Hello, Hanako. How much progress have you made in your rehabilitation today?"

[1032] Output: The user responds verbally and the audio data is sent to the server.

[1033] Step 4:

[1034] Input: User's voice data

[1035] How it works: The server uses speech recognition software to convert the voice data into text, which is then stored in a database as a conversation log.

[1036] Output: The conversation log is saved and used for the next conversation generation.

[1037] Step 5:

[1038] Input: Interaction log data

[1039] How it works: The server analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress. It generates feedback based on the analysis results.

[1040] Output: The device displays feedback, for example, "Great progress today! Good luck next time."

[1041] Step 6:

[1042] Input: Rehabilitation progress data

[1043] How it works: The server periodically checks the rehabilitation progress data and evaluates the progress on a weekly basis. It adjusts the rehabilitation menu based on the progress data. For example, if the achievement rate is low, the rehabilitation menu is reduced, and if it is high, the menu is strengthened.

[1044] Output: The new rehabilitation menu will be displayed on the device. For example, "Low back stretching exercises: 5 minutes, 3 sets."

[1045] Step 7:

[1046] Input: Elderly voice input data

[1047] How it works: The server calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state. Based on the recognized emotional state, the generative AI adjusts the content of the next rehabilitation conversation.

[1048] Output: The conversational content corresponding to the emotion will be displayed on the device. For example, "Today was tough, but you did well!"

[1049] Step 8:

[1050] Input: Elderly voice input data

[1051] Operation: When an elderly person uses an autonomous vehicle, the device displays rehabilitation menus and related information in real time on the vehicle's display. It also collects the elderly person's rehabilitation status and impressions through voice input.

[1052] Output: The collected data is sent to the server and used to generate the next rehabilitation menu and adjust the conversation content.

[1053] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1054] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1055] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1056] [Third embodiment]

[1057] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1058] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1059] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1060] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1061] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1062] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1063] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1064] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1065] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1066] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1067] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1068] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1069] The following system is proposed as a specific embodiment for carrying out the present invention.

[1070] Program Overview

[1071] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[1072] 1. Registering and managing information on elderly people

[1073] 2. Creating rehabilitation menus based on individual motivations

[1074] 3. Generating daily conversations with interactive AI

[1075] 4. Recording and analyzing conversation logs

[1076] 5. Progress monitoring and rehabilitation adjustment

[1077] A natural language description of the program's operation

[1078] 1. Registering and managing information on elderly people

[1079] Terminal: Displays an input form for registering elderly information (including, for example, "name," "age," "health status," and "rehabilitation goals").

[1080] User: Enter the necessary information into the input form. For example, the name is "Yamada Taro," the age is "75 years old," the health condition is "currently undergoing rehabilitation for right knee," and the rehabilitation goal is "playing in the park with grandchildren."

[1081] Terminal: Sends the entered information to the server.

[1082] Server: The received information is stored in a database and used for subsequent dialogue and rehabilitation menu generation.

[1083] 2. Creating rehabilitation menus based on individual motivations

[1084] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[1085] Server: Runs the algorithm that generates the rehabilitation menu and creates a specific rehabilitation menu based on the elderly person's motivation (e.g., "playing in the park with my grandchildren").

[1086] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[1087] 3. Generating daily conversations with interactive AI

[1088] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[1089] Device: Display the generated question or encouraging message. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[1090] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1091] Terminal: Sends the entered answer to the server.

[1092] 4. Recording and analyzing conversation logs

[1093] Server: Records daily interaction logs in a database.

[1094] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1095] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1096] 5. Progress monitoring and rehabilitation adjustment

[1097] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1098] Server: Based on progress data, review the rehabilitation menu and adjust as necessary.

[1099] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1100] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1101] The above is a description of the embodiment of the rehabilitation promotion system. This system is designed to enable individualized rehabilitation promotion according to the motivation and progress of each elderly person, and to achieve sustainable rehabilitation effects.

[1102] The processing flow will be explained below.

[1103] Step 1:

[1104] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1105] Step 2:

[1106] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1107] Step 3:

[1108] Terminal: Sends the entered information to the server.

[1109] Step 4:

[1110] Server: Stores the received information in a database.

[1111] Step 5:

[1112] Server: Obtains information about the elderly from the database.

[1113] Step 6:

[1114] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[1115] Step 7:

[1116] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1117] Step 8:

[1118] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[1119] Step 9:

[1120] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1121] Step 10:

[1122] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1123] Step 11:

[1124] Terminal: Sends the entered answer to the server.

[1125] Step 12:

[1126] Server: Records conversation logs in a database.

[1127] Step 13:

[1128] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1129] Step 14:

[1130] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1131] Step 15:

[1132] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1133] Step 16:

[1134] Server: Review rehabilitation menu based on progress data and adjust as necessary.

[1135] Step 17:

[1136] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1137] Step 18:

[1138] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1139] Step 19:

[1140] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[1141] The above are the specific processing steps for executing the program in this system. The system can automatically adjust the rehabilitation menu to suit the progress of each elderly person and maintain their motivation.

[1142] Example 1

[1143] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1144] In elderly rehabilitation, providing appropriate rehabilitation menus for each elderly person and maintaining their motivation are difficult challenges. It is also necessary to properly monitor rehabilitation progress and adjust rehabilitation menus in real time. Conventional methods lacked methods for solving these challenges efficiently and effectively.

[1145] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1146] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for inputting elderly information into an input form, means for displaying the generated rehabilitation menu on a terminal, and means for calling a generation AI model to generate questions. This makes it possible to provide an appropriate rehabilitation menu for each elderly person, maintain their motivation, and adjust the rehabilitation menu in real time according to their progress.

[1147] "Elderly information" is personal profile data including the elderly person's name, age, health status, rehabilitation goals, etc.

[1148] "Information registration and management means" refers to a combination of hardware and software for collecting information about seniors, storing it in a database, and accessing and updating it as needed.

[1149] The "means for generating a rehabilitation menu" refers to an algorithm that creates an individually appropriate rehabilitation program based on information about the elderly person, and the environment in which it is executed.

[1150] "Generative AI" is an artificial intelligence (AI) model that automatically generates dialogue and questions, and generally utilizes machine learning and natural language processing technologies.

[1151] "Daily generation method" refers to the process of creating new rehabilitation conversations using generative AI on a regular basis every day.

[1152] The "means for recording and analyzing dialogue logs" refers to software and a database for saving the content of conversations with elderly people and analyzing that data.

[1153] "Means for monitoring progress and adjusting rehabilitation menus" refers to a system for regularly checking the rehabilitation status of elderly people and appropriately adjusting the rehabilitation program based on the results.

[1154] An "input form" is a screen or interface that runs on a computer or mobile device and allows seniors to enter their information.

[1155] "Devices" refer to computers and mobile devices used by seniors and their caregivers.

[1156] "Means for calling a generative AI model to generate questions" refers to the process of inputting the rehabilitation progress of an elderly person into a generative AI model and generating questions based on that information.

[1157] MODE FOR CARRYING OUT THE INVENTION

[1158] This invention is a system for efficiently and effectively supporting elderly rehabilitation. This system has the function of registering and managing elderly information, generating individual rehabilitation menus, conducting daily dialogues using AI, analyzing the logs, and adjusting the rehabilitation menu according to the elderly's progress.

[1159] Hardware and software used

[1160] Hardware

[1161] Devices: Includes computers and mobile devices (e.g., tablets and smartphones) used by seniors and their caregivers.

[1162] Server: Includes a high-performance server (e.g., a cloud-based server) for managing and processing the entire system.

[1163] software

[1164] Database management system: RDBMS such as MySQL or MongoDB is used to store and manage information and conversation logs of the elderly.

[1165] Rehabilitation menu generation algorithm: An algorithm that generates an individual rehabilitation menu based on the elderly person's motivation and health condition.

[1166] Generative AI models: Includes natural language generation models (e.g., GPT-3) to generate rehabilitation questions and conversations on a daily basis.

[1167] Interaction log analysis algorithm: A machine learning algorithm to analyze collected interaction logs and evaluate motivation and progress.

[1168] Example of a system

[1169] 1. Registering and managing information on elderly people

[1170] Terminal: Displays an input interface for the user to enter information into a form, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1171] User: For example, enter "Yamada Taro" as the name, "75 years old" as the age, "currently undergoing rehabilitation for right knee" as the health condition, and "playing in the park with grandchildren" as the rehabilitation goal.

[1172] Terminal: Sends the entered data to the server.

[1173] Server: Stores the received data in a database.

[1174] 2. Creating rehabilitation menus based on individual motivations

[1175] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[1176] Server: Uses a rehabilitation menu generation algorithm to create a rehabilitation menu based on the motivations of specific elderly people.

[1177] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[1178] 3. Generating daily conversations with interactive AI

[1179] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[1180] Terminal: Displays the generated questions and encouraging messages to the user. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[1181] User: For example, "Today I completed 3 sets of 10 knee bends and straightens."

[1182] Terminal: Sends the user's answer to the server.

[1183] 4. Recording and analyzing conversation logs

[1184] Server: Records daily interaction logs in a database.

[1185] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1186] Terminal: Display a feedback message, for example, "Great job! Try a few more things next time."

[1187] 5. Progress monitoring and rehabilitation adjustment

[1188] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[1189] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1190] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[1191] Example prompt sentence:

[1192] To help you move forward with your rehabilitation, please let us know your progress: How much rehabilitation have you done today?

[1193] With the above configuration, this system enables rehabilitation to be effectively carried out according to the individual needs of the elderly, achieving lasting rehabilitation effects.

[1194] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1195] Processing step details

[1196] Step 1: Registering the elderly person's information

[1197] Terminal: Displays an elderly person information registration form to the user, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1198] Input: Information about the elderly person (e.g., name "Yamada Taro", age "75 years old", health condition "currently undergoing rehabilitation for right knee", rehabilitation goal "playing in the park with grandchildren").

[1199] Output: The data in a format that sends the information entered by the user to the server.

[1200] User: Enters the required information into the form.

[1201] Terminal: Sends the entered information to the server.

[1202] Specific operation: When you click the submit button, the input content is encoded in JSON format or similar and sent to the server as an HTTP request.

[1203] Step 2: Save your information

[1204] Server: Stores the received information of the elderly in a database.

[1205] Input: Elderly person's information data sent from the terminal.

[1206] Output: The records stored in the database.

[1207] Specific behavior: Establishes a database connection and executes an INSERT statement to persist the information.

[1208] Step 3: Creating a rehabilitation menu

[1209] Server: Retrieves information about elderly people from the database and analyzes their motivation for rehabilitation.

[1210] Input: Elderly information obtained from the database.

[1211] Output: Individual rehabilitation menu.

[1212] Specific actions: The program automatically generates a menu based on the motivation. For example, if the motivation is "playing in the park with my grandchildren," the program will generate "10 knee bends, 3 sets."

[1213] Server: Sends the generated rehabilitation menu to the terminal.

[1214] Terminal: Displays the rehabilitation menu to the user.

[1215] Specific operation: Display the received data in HTML or in the app interface.

[1216] Step 4: Generative AI interaction

[1217] Server: Calls the generative AI model and generates questions to check daily rehabilitation progress.

[1218] Input: A specific prompt (e.g., Hello Yamada-san, how much progress have you made in your rehabilitation today?).

[1219] Output: Questions or messages generated by the generative AI.

[1220] Specific behavior: Calls the AI ​​API to send prompts, receives the generated results, and formats them.

[1221] Terminal: displays the generated question to the user.

[1222] User: Answers questions with text.

[1223] Input: User response (e.g., I completed 3 sets of 10 knee bends and straightens today).

[1224] Terminal: Sends the answer to the server.

[1225] Step 5: Record and analyze conversation logs

[1226] Server: Records the received user answers in a database.

[1227] Input: User response data.

[1228] Output: Interaction logs stored in a database.

[1229] Specific operation: Executes an INSERT statement in the database to persist the log.

[1230] Server: Analyzes the dialogue log data and evaluates the user's motivation and rehabilitation progress.

[1231] Input: Saved interaction log data.

[1232] Output: Analysis results (e.g. motivation rating, feedback messages).

[1233] What it does: Runs analytical algorithms to perform pattern recognition and statistical analysis.

[1234] On the device: Display feedback to the user based on the analysis results (e.g., "Great job! Try a few more things next time").

[1235] Output: Display of feedback message.

[1236] Step 6: Monitoring progress and adjusting rehabilitation

[1237] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[1238] Input: Historical rehabilitation progress data.

[1239] Output: Adjusted rehabilitation menu.

[1240] Specific actions: Perform weekly analysis and calculate new menus.

[1241] Terminal: Notifies the user of new rehabilitation menus and adjustments.

[1242] Specific behavior: Uses notifications to display new content to the user.

[1243] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[1244] Input: Feedback on rehabilitation content.

[1245] (Application example 1)

[1246] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1247] This invention relates to a rehabilitation support system for elderly workers. The challenge is to promote the continuation of elderly rehabilitation and to provide effective rehabilitation menus that correspond to the individual conditions and goals. In particular, to improve the rehabilitation effect of elderly workers in workplaces such as factories, it is important to maintain motivation and monitor progress, but conventional systems have not been able to adequately address these issues.

[1248] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1249] In this invention, the server includes a means for registering and managing information about elderly people, a means for generating individual rehabilitation menus based on the registered information about elderly people, a means for generating and displaying rehabilitation conversations on a daily basis using generation AI, a means for recording and analyzing dialogue logs, a means for monitoring the elderly person's progress and adjusting the rehabilitation menu, and a display means using a wearable device for displaying the rehabilitation menu and progress information. This makes it possible to provide rehabilitation menus tailored to the individual conditions and goals of elderly workers, maintaining and improving their motivation, and providing optimal rehabilitation support according to their progress.

[1250] "Elderly person information" refers to personal data such as the name, age, health status, and rehabilitation goals of the elderly person being rehabilitated.

[1251] "Registration and management means" refers to systems and methods that allow elderly people's information to be entered, stored, and accessed as needed.

[1252] The "means for generating an individual rehabilitation menu" refers to an algorithm or program that proposes and creates optimal rehabilitation activities based on the individual information of the elderly person.

[1253] "Generative AI" refers to artificial intelligence technology that generates natural language and dialogue, and is a model used in particular to generate conversations related to rehabilitation.

[1254] "Means for recording and analyzing dialogue logs" refers to a method or system for registering generated conversations and user responses in a database and analyzing the data.

[1255] "Progress monitoring measures" are systems or methods for regularly checking the progress of elderly people's rehabilitation and evaluating the results.

[1256] "Means for adjusting the rehabilitation menu" refers to an algorithm or system that updates the rehabilitation menu according to progress and adjusts it to an appropriate level of difficulty and content.

[1257] "Display means using wearable devices" refers to a method of displaying rehabilitation menus and progress information using devices such as smart glasses and head-mounted displays.

[1258] We will now describe an example of how this invention can be implemented. This is a system designed to support the rehabilitation of elderly workers in factories. The system includes a sensor device, a data management system, an interactive generative AI model, and a wearable device that displays a rehabilitation menu.

[1259] First, a smartphone or tablet is used as a device to register and manage information about users (elderly workers). Users enter necessary information such as their name, age, health status, and rehabilitation goals into these devices, and send it to a server. The server stores the received information in a database. This database can use cloud data storage (for example, DynamoDB from Amazon Web Services).

[1260] The server generates an optimal rehabilitation menu for the elderly worker based on the registered information. For example, an algorithm creates a rehabilitation menu based on the registered health condition and rehabilitation goals, and the menu is sent from the server to a wearable device such as smart glasses. The wearable device visually displays the rehabilitation menu to the user, allowing the user to proceed with the work while checking the rehabilitation menu.

[1261] To check daily rehabilitation progress, the server utilizes a generative AI model (e.g., GPT-3). The server uses this AI model to generate conversations to check daily rehabilitation progress, generating questions such as, "How much rehabilitation progress have you made today?" These questions are displayed on the wearable device, and the user answers via text or voice input. The user's answers are sent back to the server, which records them in a database.

[1262] The server then analyzes the recorded dialogue logs to assess the user's progress and motivation level. For example, it uses data analysis libraries such as pandas and numpy to analyze the user's progress data. Based on the results, it automatically adjusts the rehabilitation menu and optimizes the next day's rehabilitation content. The new rehabilitation menu and adjustments are then sent to the wearable device, and the user is notified.

[1263] For example, you can generate a prompt like this:

[1264] "Hello, how far have you progressed in your rehabilitation today?"

[1265] This system supports the rehabilitation of elderly workers and can provide effective rehabilitation menus tailored to their individual conditions. Furthermore, it automatically monitors daily progress and continuously provides optimal rehabilitation support. This makes it possible to maintain the health of elderly workers and improve their work efficiency.

[1266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1267] Step 1:

[1268] The user enters the information of the elderly worker into the terminal. The user enters the necessary information such as name, age, health condition, and rehabilitation goals into a smartphone or tablet. The input data is sent to the server by pressing the send button.

[1269] Step 2:

[1270] The server then stores the received information about the elderly workers in a database. The data is stored in cloud storage such as AWS DynamoDB, ensuring basic data that can be used to generate future rehabilitation menus and monitor progress.

[1271] Step 3:

[1272] The server generates an individual rehabilitation menu based on the elderly worker's information. For example, if a user needs knee rehabilitation, the algorithm generates "10 knee bending and straightening exercises, 3 sets." The generated menu is sent to the wearable device.

[1273] Step 4:

[1274] The wearable device visually displays the generated rehabilitation menu to the user, who can then check the menu through smart glasses or other devices and follow the instructions to perform the rehabilitation.

[1275] Step 5:

[1276] The server generates daily conversations using a generative AI model to check daily rehabilitation progress. For example, it uses GPT-3 to generate questions such as "How much rehabilitation progress have you made today?" and sends them to the wearable device.

[1277] Step 6:

[1278] The wearable device displays the generated conversational messages to the user, who then answers questions by voice or text, and the responses are sent to the server.

[1279] Step 7:

[1280] The server records the received user responses in a database, which is saved as a dialogue log and later used for progress analysis.

[1281] Step 8:

[1282] The server analyzes the recorded dialogue logs to evaluate the user's progress and motivation. It then analyzes the progress data using a data analysis library (e.g., pandas, numpy) and generates a new rehabilitation menu based on the results.

[1283] Step 9:

[1284] The server then generates new rehabilitation menus and adjustments based on the evaluation results and sends them to the wearable device, providing the user with an optimal menu for effective rehabilitation the next day.

[1285] Step 10:

[1286] The wearable device notifies the user of a new rehabilitation menu, and the user follows the instructions to carry out the next day's rehabilitation and inputs new progress.

[1287] These are the specific processing steps of this system. Each step is designed to seamlessly send and receive data between the user, terminal, and server, and to comprehensively support the rehabilitation of elderly workers.

[1288] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1289] As a specific embodiment of the present invention, we propose the following system. This system is designed primarily to effectively promote rehabilitation for the elderly, and incorporates an emotion engine that recognizes the user's emotions, providing more advanced rehabilitation support.

[1290] Program Overview

[1291] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[1292] 1. Registering and managing information on elderly people

[1293] 2. Creating rehabilitation menus based on individual motivations

[1294] 3. Generating daily conversations with interactive AI

[1295] 4. Recording and analyzing conversation logs

[1296] 5. Progress monitoring and rehabilitation adjustment

[1297] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[1298] A natural language description of the program's operation

[1299] 1. Registering and managing information on elderly people

[1300] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1301] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1302] Terminal: Sends the entered information to the server.

[1303] Server: Stores the received information in a database.

[1304] 2. Creating rehabilitation menus based on individual motivations

[1305] Server: Obtains information about the elderly from the database.

[1306] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[1307] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1308] 3. Generating daily conversations with interactive AI

[1309] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[1310] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1311] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1312] Terminal: Sends the entered answer to the server.

[1313] 4. Recording and analyzing conversation logs

[1314] Server: Records daily interaction logs in a database.

[1315] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1316] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1317] 5. Progress monitoring and rehabilitation adjustment

[1318] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1319] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1320] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1321] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1322] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[1323] Server: Calls the emotion engine that analyzes the text and voice input from the user and recognizes the user's emotional state.

[1324] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[1325] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[1326] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[1327] The above is a description of the embodiment of the rehabilitation promotion system. This system can promote rehabilitation individually based on each elderly person's motivation, progress, and even emotional state. It is designed with the aim of realizing continuous and effective rehabilitation support.

[1328] The processing flow will be explained below.

[1329] Step 1:

[1330] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1331] Step 2:

[1332] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1333] Step 3:

[1334] Terminal: Sends the entered information to the server.

[1335] Step 4:

[1336] Server: Stores the received information in a database.

[1337] Step 5:

[1338] Server: Obtains information about the elderly from the database.

[1339] Step 6:

[1340] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[1341] Step 7:

[1342] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1343] Step 8:

[1344] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[1345] Step 9:

[1346] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1347] Step 10:

[1348] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1349] Step 11:

[1350] Terminal: Sends the entered answer to the server.

[1351] Step 12:

[1352] Server: Records conversation logs in a database.

[1353] Step 13:

[1354] Server: Calls the emotion engine and analyzes the user's input text and voice data to recognize the user's emotional state.

[1355] Step 14:

[1356] Server: Records the user's emotional state (e.g., joy, sadness, anger, depression, etc.) recognized by the emotion engine in the dialogue log.

[1357] Step 15:

[1358] Server: Analyzes the dialogue log data and runs algorithms to assess the user's motivation, rehabilitation progress, and emotional state.

[1359] Step 16:

[1360] On the device: Display feedback based on the analysis results. For example, if the user is feeling down, display an encouraging message like, "Today was difficult, but you did well!"

[1361] Step 17:

[1362] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1363] Step 18:

[1364] Server: Adjust the rehabilitation menu based on the progress data and the user's emotional state. For example, if the achievement rate is low and the user is feeling depressed, reduce the rehabilitation menu.

[1365] Step 19:

[1366] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1367] Step 20:

[1368] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1369] Step 21:

[1370] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[1371] The above are the specific processing steps for executing the program in this system. Taking into account the progress and emotional state of each elderly person, the system can automatically adjust appropriate rehabilitation programs and maintain motivation.

[1372] Example 2

[1373] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1374] With conventional rehabilitation systems, it was difficult to customize rehabilitation according to the motivation and emotional state of each elderly person, making it difficult to maintain the elderly's motivation. Furthermore, when providing effective rehabilitation menus and managing progress, automatic adjustments to meet individual needs were not sufficiently performed. This led to the issue of elderly people losing motivation to continue rehabilitation.

[1375] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1376] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, and means for recognizing the elderly's emotional state using an emotion recognition engine and adjusting the rehabilitation conversation content based on the recognized emotional state. This makes it possible to provide an effective rehabilitation menu and manage progress according to the elderly's individual needs and emotional state while maintaining their motivation for rehabilitation.

[1377] "Elderly person information" refers to personal information such as the elderly person's name, age, health status, and rehabilitation goals.

[1378] A "rehabilitation menu" refers to a customized exercise and activity program based on an elderly person's health condition and rehabilitation goals.

[1379] "Generative AI" refers to an artificial intelligence model that performs natural language generation and is used to generate rehabilitation questions and conversations.

[1380] "Dialogue logs" refer to data that records daily conversations with the generating AI and responses from the elderly.

[1381] "Progress monitoring" refers to the process of regularly observing and evaluating the progress made by older adults through rehabilitation.

[1382] An "emotion recognition engine" refers to a software system that analyzes and recognizes the emotional state of elderly people from their input text or voice.

[1383] "Feedback" refers to the evaluation and encouraging messages given to elderly people based on the results and progress of their rehabilitation program.

[1384] As a specific embodiment of this invention, we propose a rehabilitation support system that effectively supports elderly rehabilitation and maintains their motivation by registering user information, generating daily conversations using a generative AI model, and monitoring progress.

[1385] Key components and their roles

[1386] 1. Registering and managing information on elderly people

[1387] Terminal: Display an input form for elderly person information registration. A software form containing input fields such as "name," "age," "health status," and "rehabilitation goals" is used.

[1388] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1389] Terminal: Sends the entered information to the server.

[1390] Server: Stores the received information in a database.

[1391] 2. Creating rehabilitation menus based on individual motivations

[1392] Server: Obtains information about the elderly from the database.

[1393] Server: Runs the algorithm that generates the rehabilitation menu. This algorithm includes the ability to automatically generate a customized menu based on the user's motivation (e.g., "playing in the park with my grandchildren").

[1394] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1395] 3. Generating daily conversations with interactive AI

[1396] Server: Uses a generative AI model (e.g., OpenAI GPT-4) to generate questions related to elderly rehabilitation.

[1397] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1398] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1399] Terminal: Sends the entered answer to the server.

[1400] 4. Recording and analyzing conversation logs

[1401] Server: Records daily interaction logs in a database.

[1402] Server: Analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress, for example, using the Python Pandas library.

[1403] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1404] 5. Progress monitoring and rehabilitation adjustment

[1405] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1406] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1407] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1408] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[1409] Server: Calls an emotion engine (e.g., IBM Watson's Tone Analyzer) to analyze the text and voice input from the user and recognize the user's emotional state.

[1410] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[1411] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[1412] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[1413] This system will provide rehabilitation support tailored to the individual needs and emotional state of the elderly, thereby achieving sustainable and effective rehabilitation. As a concrete example, the following prompt sentences are provided:

[1414] Example prompt sentence:

[1415] "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[1416] This system makes it easier for elderly people to maintain motivation for daily rehabilitation, allowing them to proceed with rehabilitation more effectively.

[1417] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1418] System processing flow

[1419] Step 1: Enter and register the senior citizen's information

[1420] Input: The user enters their information into an input form.

[1421] Terminal: Displays an input form for registering elderly information. Input fields include "name," "age," "health status," and "rehabilitation goals."

[1422] User: Enter information in the above fields. For example, enter "Name: Taro Yamada", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1423] Specific operation: When the user clicks the "Submit" button on the input form, the terminal sends this information to the server.

[1424] Output: The entered information is sent to the server and stored.

[1425] Server: Saves the received information in a database and generates a message confirming the save.

[1426] Specific operation: After the data is successfully saved to the database, the server returns a confirmation message to the terminal saying "The information has been saved successfully."

[1427] Step 2: Creating an individual rehabilitation menu

[1428] Input: Information about the elderly stored in a database.

[1429] Server: Obtains information about the elderly from the database.

[1430] Server: Runs the algorithm that generates the rehabilitation menu and generates a rehabilitation menu based on the acquired information and motivation (e.g., "playing in the park with my grandchildren").

[1431] Specific actions: Use Python scripts as algorithms to design rehabilitation menus.

[1432] Output: The generated rehabilitation menu.

[1433] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1434] Specific operation: A rehabilitation menu will be displayed on the device screen, and the user can check it.

[1435] Step 3: Generate daily rehabilitation conversations

[1436] Input: Elderly information and rehabilitation menu.

[1437] Server: Calls a generative AI model (e.g., OpenAI GPT-4) and generates questions related to elderly rehabilitation.

[1438] An example of a specific prompt sentence: The server generates "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[1439] Output: Generated rehabilitation questions.

[1440] Terminal: Display the generated question.

[1441] Specific behavior: A screen is launched that displays a question to the user.

[1442] Step 4: User answers and submits

[1443] Input: The generated question and the user's text answer.

[1444] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1445] Specific behavior: The user enters an answer and clicks the "Submit" button.

[1446] Output: The user's text response is sent to the server.

[1447] Terminal: Sends the user's answer to the server.

[1448] Step 5: Record and analyze conversation logs

[1449] Input: User response data.

[1450] Server: Records the user's answers in a database.

[1451] Server: Analyzes daily interaction log data and runs algorithms to evaluate user motivation and rehabilitation progress, using, for example, the Python Pandas library.

[1452] Specific operation: The server stores the dialogue log in a database and analyzes the progress data.

[1453] Output: Feedback based on the analysis results.

[1454] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1455] Step 6: Progress monitoring and rehabilitation adjustment

[1456] Input: Accumulated progress data.

[1457] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1458] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1459] What it does: It periodically runs a progress check script and generates menu adjustment results.

[1460] Output: New rehab menu after adjustments.

[1461] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1462] Specific operation: A new rehabilitation menu will be displayed on the device and the user will confirm it.

[1463] Step 7: Emotion-aware conversation adjustment

[1464] Input: User input text and voice data.

[1465] Server: Calls an emotion recognition engine (e.g., IBM Watson's Tone Analyzer) to analyze the user's emotional state.

[1466] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[1467] Specific operation: Uses the analysis results to provide prompts to the generative AI and adjust the conversation content.

[1468] Output: The adjusted conversation.

[1469] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[1470] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[1471] Specific behavior: The adjusted message is displayed on the screen and recorded as a conversation log for the user.

[1472] (Application example 2)

[1473] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1474] Improving the effectiveness of elderly rehabilitation requires personalized responses tailored to each individual's condition and emotions. However, current rehabilitation systems have difficulty understanding individual situations and emotions in real time and providing appropriate rehabilitation programs based on that understanding. Furthermore, due to a lack of rehabilitation support for elderly people using self-driving cars, there is no monitoring of rehabilitation status or real-time support while traveling. This poses a significant challenge that significantly impacts the rehabilitation progress and motivation of elderly people.

[1475] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for recognizing the elderly's emotional state using an emotion engine and optimizing the conversation content and rehabilitation menu, means for displaying the rehabilitation menu in real time on an in-vehicle display when the elderly uses a self-driving car, and means for collecting the elderly's rehabilitation status and impressions through voice input. This enables appropriate rehabilitation support based on the elderly's condition and emotions in real time.

[1476] The "Means for registering and managing information on elderly people" is part of a system that records and stores basic information about elderly people, their health status, rehabilitation goals, etc. in a database, and has the function of accessing and editing that information as needed.

[1477] The "means for generating an individual rehabilitation menu" is part of the system that executes an algorithm that automatically creates a rehabilitation plan tailored to the individual needs and goals of the registered elderly person based on their information.

[1478] The "means for generating rehabilitation conversations using generative AI" is part of a system that uses a generative AI model to automatically generate and display rehabilitation questions and conversations suitable for elderly people on a daily basis.

[1479] The "means for recording and analyzing dialogue logs" is part of a system for recording dialogues with elderly people and analyzing them to evaluate their rehabilitation progress and motivation.

[1480] The "means for monitoring the elderly person's progress and adjusting the rehabilitation menu" is part of a system that continuously monitors the elderly person's rehabilitation progress and dynamically changes and optimizes the rehabilitation menu according to that progress.

[1481] The "means for recognizing emotional states using an emotion engine" is part of a system that has the function of analyzing input text and voice data of elderly people, recognizing their emotional states, and reflecting this in rehabilitation support.

[1482] The "in-vehicle display means for self-driving vehicles" is part of a system that displays rehabilitation menus and related information in real time on the in-vehicle display when elderly people use self-driving vehicles.

[1483] The "means for collecting status through voice input" is part of a system that allows elderly people to input their rehabilitation status and impressions via voice in self-driving cars, etc., and collect that data.

[1484] As an embodiment of the present invention, we propose an elderly rehabilitation support system that is installed in an autonomous vehicle. This system has multiple functions for effectively carrying out rehabilitation within the autonomous vehicle.

[1485] Program Overview

[1486] The system includes the following main features:

[1487] 1. Registering and managing information on elderly people

[1488] 2. Creating an individual rehabilitation menu

[1489] 3. Generating Conversations About Rehabilitation Using Generative AI

[1490] 4. Recording and analyzing conversation logs

[1491] 5. Progress monitoring and rehabilitation adjustment

[1492] 6. Emotional state recognition and reflection using an emotion engine

[1493] 7. Displaying rehabilitation menus on an in-vehicle display

[1494] 8. Collecting rehabilitation status information through voice input

[1495] How the system is implemented

[1496] 1. Registering and managing information on elderly people

[1497] Device: An input form for elderly information is displayed on the in-car display or smartphone. Input fields include "name," "age," "health status," and "rehabilitation goals."

[1498] User: Enter the required information into the input form. Example: "Name: Hanako Suzuki", "Age: 76", "Health condition: Currently undergoing rehabilitation for lower back pain", "Rehabilitation goal: Enjoy traveling with grandchildren"

[1499] Terminal: Sends the entered information to the server.

[1500] Server: Stores the received information in a database.

[1501] 2. Creating an individual rehabilitation menu

[1502] Server: Obtains information about the elderly from the database.

[1503] Server: Executes the algorithm for generating a rehabilitation menu and creates a rehabilitation menu based on the elderly person's goals.

[1504] Terminal: Display the generated rehabilitation menu on the in-car display. Example: "Waist stretching exercises: 5 minutes, 2 sets."

[1505] 3. Generating Conversations About Rehabilitation Using Generative AI

[1506] Server: Calls a generative AI model (e.g., GPT-4) and generates questions related to elderly rehabilitation.

[1507] Terminal: Display the generated question on the display. Example: "Hello, Hanako. How much progress did you make in your rehabilitation today?"

[1508] User: Answers questions verbally.

[1509] Terminal: The input voice data is sent to the server and converted into text using voice recognition software.

[1510] 4. Recording and analyzing conversation logs

[1511] Server: Records daily interaction logs in a database.

[1512] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1513] 5. Progress monitoring and rehabilitation adjustment

[1514] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1515] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1516] Device: Notifies the new rehabilitation menu and adjustment details on the in-car display. Example: "Low back stretching exercises, 5 minutes, 3 sets."

[1517] 6. Emotional state recognition and reflection using an emotion engine

[1518] Server: Calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state.

[1519] Server: Based on the recognized emotional state, the generative AI adjusts the next rehabilitation conversation. For example, if the user is depressed, it will send more encouraging messages.

[1520] Device: Display the adjusted conversation on the in-car display. Example: "Today was tough, but you did well!"

[1521] 7. Displaying rehabilitation menus on an in-vehicle display

[1522] Terminal: When elderly people use self-driving cars, the device provides a function that displays rehabilitation menus and related information in real time on the in-car display.

[1523] 8. Collecting rehabilitation status information through voice input

[1524] Terminal: Collects elderly people's rehabilitation status and impressions through voice input on the in-vehicle display. The voice input data is sent to the server and converted into text format as needed.

[1525] Software and hardware used

[1526] Hardware: In-car display, voice recognition microphone

[1527] Software: Generative AI models (e.g., GPT-4), emotion recognition APIs, speech recognition software (e.g., speech recognition services)

[1528] Prompt Sentence Examples

[1529] "Hello, how much progress have you made in your rehabilitation today?"

[1530] "How are you feeling today? How is your rehabilitation going?"

[1531] This will enable appropriate rehabilitation support based on the elderly person's condition and emotions in real time.

[1532] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1533] Step 1:

[1534] Input: Elderly person's information (name, age, health status, rehabilitation goals)

[1535] Operation: The device displays an input form for elderly information on the in-car display or smartphone. The user enters the necessary information into this input form. For example, "Name: Suzuki Hanako," "Age: 76 years old," "Health condition: Currently undergoing rehabilitation for lower back pain," and "Rehabilitation goal: Enjoy traveling with grandchildren."

[1536] Output: The entered information is sent to the server, which stores the received information in a database.

[1537] Step 2:

[1538] Input: Elderly information retrieved from the database

[1539] Operation: The server retrieves information about the elderly person from the database and runs an algorithm to generate a rehabilitation menu based on that information. An individual rehabilitation menu is generated.

[1540] Output: The generated rehabilitation menu is displayed on the terminal (vehicle display). For example, it may say, "Low back stretching exercises, 5 minutes, 2 sets."

[1541] Step 3:

[1542] Input: Elderly rehabilitation menu progress data

[1543] How it works: The server invokes a generative AI model (e.g., GPT-4) to generate questions related to elderly rehabilitation. The device displays the generated questions on the in-car display. For example, it displays "Hello, Hanako. How much progress have you made in your rehabilitation today?"

[1544] Output: The user responds verbally and the audio data is sent to the server.

[1545] Step 4:

[1546] Input: User's voice data

[1547] How it works: The server uses speech recognition software to convert the voice data into text, which is then stored in a database as a conversation log.

[1548] Output: The conversation log is saved and used for the next conversation generation.

[1549] Step 5:

[1550] Input: Interaction log data

[1551] How it works: The server analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress. It generates feedback based on the analysis results.

[1552] Output: The device displays feedback, for example, "Great progress today! Good luck next time."

[1553] Step 6:

[1554] Input: Rehabilitation progress data

[1555] How it works: The server periodically checks the rehabilitation progress data and evaluates the progress on a weekly basis. It adjusts the rehabilitation menu based on the progress data. For example, if the achievement rate is low, the rehabilitation menu is reduced, and if it is high, the menu is strengthened.

[1556] Output: The new rehabilitation menu will be displayed on the device. For example, "Low back stretching exercises: 5 minutes, 3 sets."

[1557] Step 7:

[1558] Input: Elderly voice input data

[1559] How it works: The server calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state. Based on the recognized emotional state, the generative AI adjusts the content of the next rehabilitation conversation.

[1560] Output: The conversational content corresponding to the emotion will be displayed on the device. For example, "Today was tough, but you did well!"

[1561] Step 8:

[1562] Input: Elderly voice input data

[1563] Operation: When an elderly person uses an autonomous vehicle, the device displays rehabilitation menus and related information in real time on the vehicle's display. It also collects the elderly person's rehabilitation status and impressions through voice input.

[1564] Output: The collected data is sent to the server and used to generate the next rehabilitation menu and adjust the conversation content.

[1565] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1566] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1567] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1568] [Fourth embodiment]

[1569] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1570] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1571] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1572] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1573] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1574] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1575] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1576] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1577] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1578] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1579] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1580] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1581] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1582] The following system is proposed as a specific embodiment for carrying out the present invention.

[1583] Program Overview

[1584] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[1585] 1. Registering and managing information on elderly people

[1586] 2. Creating rehabilitation menus based on individual motivations

[1587] 3. Generating daily conversations with interactive AI

[1588] 4. Recording and analyzing conversation logs

[1589] 5. Progress monitoring and rehabilitation adjustment

[1590] A natural language description of the program's operation

[1591] 1. Registering and managing information on elderly people

[1592] Terminal: Displays an input form for registering elderly information (including, for example, "name," "age," "health status," and "rehabilitation goals").

[1593] User: Enter the necessary information into the input form. For example, the name is "Yamada Taro," the age is "75 years old," the health condition is "currently undergoing rehabilitation for right knee," and the rehabilitation goal is "playing in the park with grandchildren."

[1594] Terminal: Sends the entered information to the server.

[1595] Server: The received information is stored in a database and used for subsequent dialogue and rehabilitation menu generation.

[1596] 2. Creating rehabilitation menus based on individual motivations

[1597] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[1598] Server: Runs the algorithm that generates the rehabilitation menu and creates a specific rehabilitation menu based on the elderly person's motivation (e.g., "playing in the park with my grandchildren").

[1599] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[1600] 3. Generating daily conversations with interactive AI

[1601] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[1602] Device: Display the generated question or encouraging message. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[1603] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1604] Terminal: Sends the entered answer to the server.

[1605] 4. Recording and analyzing conversation logs

[1606] Server: Records daily interaction logs in a database.

[1607] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1608] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1609] 5. Progress monitoring and rehabilitation adjustment

[1610] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1611] Server: Based on progress data, review the rehabilitation menu and adjust as necessary.

[1612] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1613] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1614] The above is a description of the embodiment of the rehabilitation promotion system. This system is designed to enable individualized rehabilitation promotion according to the motivation and progress of each elderly person, and to achieve sustainable rehabilitation effects.

[1615] The processing flow will be explained below.

[1616] Step 1:

[1617] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1618] Step 2:

[1619] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1620] Step 3:

[1621] Terminal: Sends the entered information to the server.

[1622] Step 4:

[1623] Server: Stores the received information in a database.

[1624] Step 5:

[1625] Server: Obtains information about the elderly from the database.

[1626] Step 6:

[1627] Server: Runs the algorithm that generates the rehabilitation menu and creates a rehabilitation menu based on the elderly person's motivation (e.g., "playing in the park with my grandchildren").

[1628] Step 7:

[1629] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1630] Step 8:

[1631] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[1632] Step 9:

[1633] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1634] Step 10:

[1635] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1636] Step 11:

[1637] Terminal: Sends the entered answer to the server.

[1638] Step 12:

[1639] Server: Records conversation logs in a database.

[1640] Step 13:

[1641] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1642] Step 14:

[1643] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1644] Step 15:

[1645] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1646] Step 16:

[1647] Server: Review rehabilitation menu based on progress data and adjust as necessary.

[1648] Step 17:

[1649] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1650] Step 18:

[1651] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1652] Step 19:

[1653] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[1654] The above are the specific processing steps for executing the program in this system. It can automatically adjust the rehabilitation menu to suit the progress of each elderly person and maintain their motivation.

[1655] Example 1

[1656] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1657] In elderly rehabilitation, providing appropriate rehabilitation menus for each elderly person and maintaining their motivation are difficult challenges. It is also necessary to properly monitor rehabilitation progress and adjust rehabilitation menus in real time. Conventional methods lacked methods for solving these challenges efficiently and effectively.

[1658] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1659] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for inputting elderly information into an input form, means for displaying the generated rehabilitation menu on a terminal, and means for calling a generation AI model to generate questions. This makes it possible to provide an appropriate rehabilitation menu for each elderly person, maintain their motivation, and adjust the rehabilitation menu in real time according to their progress.

[1660] "Elderly information" is personal profile data including the elderly person's name, age, health status, rehabilitation goals, etc.

[1661] "Information registration and management means" refers to a combination of hardware and software for collecting information about seniors, storing it in a database, and accessing and updating it as needed.

[1662] The "means for generating a rehabilitation menu" refers to an algorithm that creates an individually appropriate rehabilitation program based on information about the elderly person, and the environment in which it is executed.

[1663] "Generative AI" is an artificial intelligence (AI) model that automatically generates dialogue and questions, and generally utilizes machine learning and natural language processing technologies.

[1664] "Daily generation method" refers to the process of creating new rehabilitation conversations using generative AI on a regular basis every day.

[1665] The "means for recording and analyzing dialogue logs" refers to software and a database for saving the content of conversations with elderly people and analyzing that data.

[1666] "Means for monitoring progress and adjusting rehabilitation menus" refers to a system for regularly checking the rehabilitation status of elderly people and appropriately adjusting the rehabilitation program based on the results.

[1667] An "input form" is a screen or interface that runs on a computer or mobile device and allows seniors to enter their information.

[1668] "Devices" refer to computers and mobile devices used by seniors and their caregivers.

[1669] "Means for calling a generative AI model to generate questions" refers to the process of inputting the rehabilitation progress of an elderly person into a generative AI model and generating questions based on that information.

[1670] MODE FOR CARRYING OUT THE INVENTION

[1671] This invention is a system for efficiently and effectively supporting elderly rehabilitation. This system has the function of registering and managing elderly information, generating individual rehabilitation menus, conducting daily dialogues using AI, analyzing the logs, and adjusting the rehabilitation menu according to the elderly's progress.

[1672] Hardware and software used

[1673] Hardware

[1674] Devices: Includes computers and mobile devices (e.g., tablets and smartphones) used by seniors and their caregivers.

[1675] Server: Includes a high-performance server (e.g., a cloud-based server) for managing and processing the entire system.

[1676] software

[1677] Database management system: RDBMS such as MySQL or MongoDB is used to store and manage information and conversation logs of the elderly.

[1678] Rehabilitation menu generation algorithm: An algorithm that generates an individual rehabilitation menu based on the elderly person's motivation and health condition.

[1679] Generative AI models: Includes natural language generation models (e.g., GPT-3) to generate rehabilitation questions and conversations on a daily basis.

[1680] Interaction log analysis algorithm: A machine learning algorithm to analyze collected interaction logs and evaluate motivation and progress.

[1681] Example of a system

[1682] 1. Registering and managing information on elderly people

[1683] Terminal: Displays an input interface for the user to enter information into a form, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1684] User: For example, enter "Yamada Taro" as the name, "75 years old" as the age, "currently undergoing rehabilitation for right knee" as the health condition, and "playing in the park with grandchildren" as the rehabilitation goal.

[1685] Terminal: Sends the entered data to the server.

[1686] Server: Stores the received data in a database.

[1687] 2. Creating rehabilitation menus based on individual motivations

[1688] Server: Obtains information about the elderly person from the database and analyzes their rehabilitation goals.

[1689] Server: Uses a rehabilitation menu generation algorithm to create a rehabilitation menu based on the motivations of specific elderly people.

[1690] Terminal: Displays the generated rehabilitation menu to the user. For example, it suggests "10 knee bending and straightening exercises, 3 sets."

[1691] 3. Generating daily conversations with interactive AI

[1692] Server: Calls the generative AI model and generates questions to check the elderly person's rehabilitation progress and maintain their motivation.

[1693] Terminal: Displays the generated questions and encouraging messages to the user. For example, "Hello, Yamada-san, how much progress did you make in your rehabilitation today?"

[1694] User: For example, "Today I completed 3 sets of 10 knee bends and straightens."

[1695] Terminal: Sends the user's answer to the server.

[1696] 4. Recording and analyzing conversation logs

[1697] Server: Records daily interaction logs in a database.

[1698] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1699] Terminal: Display a feedback message, for example, "Great job! Try a few more things next time."

[1700] 5. Progress monitoring and rehabilitation adjustment

[1701] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[1702] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1703] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[1704] Example prompt sentence:

[1705] To help you move forward with your rehabilitation, please let us know your progress: How much rehabilitation have you done today?

[1706] With the above configuration, this system enables rehabilitation to be effectively carried out according to the individual needs of the elderly, achieving lasting rehabilitation effects.

[1707] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1708] Processing step details

[1709] Step 1: Registering the elderly person's information

[1710] Terminal: Displays an elderly person information registration form to the user, including fields such as "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1711] Input: Information about the elderly person (e.g., name "Yamada Taro", age "75 years old", health condition "currently undergoing rehabilitation for right knee", rehabilitation goal "playing in the park with grandchildren").

[1712] Output: The data in a format that sends the information entered by the user to the server.

[1713] User: Enters the required information into the form.

[1714] Terminal: Sends the entered information to the server.

[1715] Specific operation: When you click the submit button, the input content is encoded in JSON format or similar and sent to the server as an HTTP request.

[1716] Step 2: Save your information

[1717] Server: Stores the received information of the elderly in a database.

[1718] Input: Elderly person's information data sent from the terminal.

[1719] Output: The records stored in the database.

[1720] Specific behavior: Establishes a database connection and executes an INSERT statement to persist the information.

[1721] Step 3: Creating a rehabilitation menu

[1722] Server: Retrieves information about elderly people from the database and analyzes their motivation for rehabilitation.

[1723] Input: Elderly information obtained from the database.

[1724] Output: Individual rehabilitation menu.

[1725] Specific actions: The program automatically generates a menu based on the motivation. For example, if the motivation is "playing in the park with my grandchildren," the program will generate "10 knee bends, 3 sets."

[1726] Server: Sends the generated rehabilitation menu to the terminal.

[1727] Terminal: Displays the rehabilitation menu to the user.

[1728] Specific operation: Display the received data in HTML or in the app interface.

[1729] Step 4: Generative AI interaction

[1730] Server: Calls the generative AI model and generates questions to check daily rehabilitation progress.

[1731] Input: A specific prompt (e.g., Hello Yamada-san, how much progress have you made in your rehabilitation today?).

[1732] Output: Questions or messages generated by the generative AI.

[1733] Specific behavior: Calls the AI ​​API to send prompts, receives the generated results, and formats them.

[1734] Terminal: displays the generated question to the user.

[1735] User: Answers questions with text.

[1736] Input: User response (e.g., I completed 3 sets of 10 knee bends and straightens today).

[1737] Terminal: Sends the answer to the server.

[1738] Step 5: Record and analyze conversation logs

[1739] Server: Records the received user answers in a database.

[1740] Input: User response data.

[1741] Output: Interaction logs stored in a database.

[1742] Specific operation: Executes an INSERT statement in the database to persist the log.

[1743] Server: Analyzes the dialogue log data and evaluates the user's motivation and rehabilitation progress.

[1744] Input: Saved interaction log data.

[1745] Output: Analysis results (e.g. motivation rating, feedback messages).

[1746] What it does: Runs analytical algorithms to perform pattern recognition and statistical analysis.

[1747] On the device: Display feedback to the user based on the analysis results (e.g., "Great job! Try a few more things next time").

[1748] Output: Display of feedback message.

[1749] Step 6: Monitoring progress and adjusting rehabilitation

[1750] Server: Regularly checks rehabilitation progress data and adjusts rehabilitation menu based on progress.

[1751] Input: Historical rehabilitation progress data.

[1752] Output: Adjusted rehabilitation menu.

[1753] Specific actions: Perform weekly analysis and calculate new menus.

[1754] Terminal: Notifies the user of new rehabilitation menus and adjustments.

[1755] Specific behavior: Uses notifications to display new content to the user.

[1756] User: Enters thoughts and feedback about the new rehabilitation content and sends it to the server.

[1757] Input: Feedback on rehabilitation content.

[1758] (Application example 1)

[1759] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1760] This invention relates to a rehabilitation support system for elderly workers. The challenge is to promote the continuation of elderly rehabilitation and to provide effective rehabilitation menus that correspond to the individual conditions and goals. In particular, to improve the rehabilitation effect of elderly workers in workplaces such as factories, it is important to maintain motivation and monitor progress, but conventional systems have not been able to adequately address these issues.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1762] In this invention, the server includes a means for registering and managing information about elderly people, a means for generating individual rehabilitation menus based on the registered information about elderly people, a means for generating and displaying rehabilitation conversations on a daily basis using generation AI, a means for recording and analyzing dialogue logs, a means for monitoring the elderly person's progress and adjusting the rehabilitation menu, and a display means using a wearable device for displaying the rehabilitation menu and progress information. This makes it possible to provide rehabilitation menus tailored to the individual conditions and goals of elderly workers, maintaining and improving their motivation, and providing optimal rehabilitation support according to their progress.

[1763] "Elderly person information" refers to personal data such as the name, age, health status, and rehabilitation goals of the elderly person being rehabilitated.

[1764] "Registration and management means" refers to systems and methods that allow elderly people's information to be entered, stored, and accessed as needed.

[1765] The "means for generating an individual rehabilitation menu" refers to an algorithm or program that proposes and creates optimal rehabilitation activities based on the individual information of the elderly person.

[1766] "Generative AI" refers to artificial intelligence technology that generates natural language and dialogue, and is a model used in particular to generate conversations related to rehabilitation.

[1767] "Means for recording and analyzing dialogue logs" refers to a method or system for registering generated conversations and user responses in a database and analyzing the data.

[1768] "Progress monitoring measures" are systems or methods for regularly checking the progress of elderly people's rehabilitation and evaluating the results.

[1769] "Means for adjusting the rehabilitation menu" refers to an algorithm or system that updates the rehabilitation menu according to progress and adjusts it to an appropriate level of difficulty and content.

[1770] "Display means using wearable devices" refers to a method of displaying rehabilitation menus and progress information using devices such as smart glasses and head-mounted displays.

[1771] We will now describe an example of how this invention can be implemented. This is a system designed to support the rehabilitation of elderly workers in factories. The system includes a sensor device, a data management system, an interactive generative AI model, and a wearable device that displays a rehabilitation menu.

[1772] First, a smartphone or tablet is used as a device to register and manage information about users (elderly workers). Users enter necessary information such as their name, age, health status, and rehabilitation goals into these devices, and send it to a server. The server stores the received information in a database. This database can use cloud data storage (for example, DynamoDB from Amazon Web Services).

[1773] The server generates an optimal rehabilitation menu for the elderly worker based on the registered information. For example, an algorithm creates a rehabilitation menu based on the registered health condition and rehabilitation goals, and the menu is sent from the server to a wearable device such as smart glasses. The wearable device visually displays the rehabilitation menu to the user, allowing the user to proceed with the work while checking the rehabilitation menu.

[1774] To check daily rehabilitation progress, the server utilizes a generative AI model (e.g., GPT-3). The server uses this AI model to generate conversations to check daily rehabilitation progress, generating questions such as, "How much rehabilitation progress have you made today?" These questions are displayed on the wearable device, and the user answers via text or voice input. The user's answers are sent back to the server, which records them in a database.

[1775] The server then analyzes the recorded dialogue logs to assess the user's progress and motivation level. For example, it uses data analysis libraries such as pandas and numpy to analyze the user's progress data. Based on the results, it automatically adjusts the rehabilitation menu and optimizes the next day's rehabilitation content. The new rehabilitation menu and adjustments are then sent to the wearable device, and the user is notified.

[1776] For example, you can generate a prompt like this:

[1777] "Hello, how far have you progressed in your rehabilitation today?"

[1778] This system supports the rehabilitation of elderly workers and can provide effective rehabilitation menus tailored to their individual conditions. Furthermore, it automatically monitors daily progress and continuously provides optimal rehabilitation support. This makes it possible to maintain the health of elderly workers and improve their work efficiency.

[1779] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1780] Step 1:

[1781] The user enters the information of the elderly worker into the terminal. The user enters the necessary information such as name, age, health condition, and rehabilitation goals into a smartphone or tablet. The input data is sent to the server by pressing the send button.

[1782] Step 2:

[1783] The server then stores the received information about the elderly workers in a database. The data is stored in cloud storage such as AWS DynamoDB, ensuring basic data that can be used to generate future rehabilitation menus and monitor progress.

[1784] Step 3:

[1785] The server generates an individual rehabilitation menu based on the elderly worker's information. For example, if a user needs knee rehabilitation, the algorithm generates "10 knee bending and straightening exercises, 3 sets." The generated menu is sent to the wearable device.

[1786] Step 4:

[1787] The wearable device visually displays the generated rehabilitation menu to the user, who can then check the menu through smart glasses or other devices and follow the instructions to perform the rehabilitation.

[1788] Step 5:

[1789] The server generates daily conversations using a generative AI model to check daily rehabilitation progress. For example, it uses GPT-3 to generate questions such as "How much rehabilitation progress have you made today?" and sends them to the wearable device.

[1790] Step 6:

[1791] The wearable device displays the generated conversational messages to the user, who then answers questions by voice or text, and the responses are sent to the server.

[1792] Step 7:

[1793] The server records the received user responses in a database, which is saved as a dialogue log and later used for progress analysis.

[1794] Step 8:

[1795] The server analyzes the recorded dialogue logs to evaluate the user's progress and motivation. It then analyzes the progress data using a data analysis library (e.g., pandas, numpy) and generates a new rehabilitation menu based on the results.

[1796] Step 9:

[1797] The server then generates new rehabilitation menus and adjustments based on the evaluation results and sends them to the wearable device, providing the user with an optimal menu for effective rehabilitation the next day.

[1798] Step 10:

[1799] The wearable device notifies the user of a new rehabilitation menu, and the user follows the instructions to carry out the next day's rehabilitation and inputs new progress.

[1800] These are the specific processing steps of this system. Each step is designed to seamlessly send and receive data between the user, terminal, and server, and to comprehensively support the rehabilitation of elderly workers.

[1801] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1802] As a specific embodiment of the present invention, we propose the following system. This system is designed primarily to effectively promote rehabilitation for the elderly, and incorporates an emotion engine that recognizes the user's emotions, providing more advanced rehabilitation support.

[1803] Program Overview

[1804] This system is designed to improve motivation and provide effective rehabilitation for elderly people who require assistance and care. It includes the following features:

[1805] 1. Registering and managing information on elderly people

[1806] 2. Creating rehabilitation menus based on individual motivations

[1807] 3. Generating daily conversations with interactive AI

[1808] 4. Recording and analyzing conversation logs

[1809] 5. Progress monitoring and rehabilitation adjustment

[1810] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[1811] A natural language description of the program's operation

[1812] 1. Registering and managing information on elderly people

[1813] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1814] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1815] Terminal: Sends the entered information to the server.

[1816] Server: Stores the received information in a database.

[1817] 2. Creating rehabilitation menus based on individual motivations

[1818] Server: Obtains information about the elderly from the database.

[1819] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[1820] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1821] 3. Generating daily conversations with interactive AI

[1822] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[1823] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1824] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1825] Terminal: Sends the entered answer to the server.

[1826] 4. Recording and analyzing conversation logs

[1827] Server: Records daily interaction logs in a database.

[1828] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[1829] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1830] 5. Progress monitoring and rehabilitation adjustment

[1831] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1832] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1833] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1834] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1835] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[1836] Server: Calls the emotion engine that analyzes the text and voice input from the user and recognizes the user's emotional state.

[1837] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[1838] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[1839] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[1840] The above is a description of the embodiment of the rehabilitation promotion system. This system can promote rehabilitation individually based on each elderly person's motivation, progress, and even emotional state. It is designed with the aim of realizing continuous and effective rehabilitation support.

[1841] The processing flow will be explained below.

[1842] Step 1:

[1843] Terminal: Displays an input form for elderly person information registration. Input fields include "Name," "Age," "Health Status," and "Rehabilitation Goals."

[1844] Step 2:

[1845] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1846] Step 3:

[1847] Terminal: Sends the entered information to the server.

[1848] Step 4:

[1849] Server: Stores the received information in a database.

[1850] Step 5:

[1851] Server: Obtains information about the elderly from the database.

[1852] Step 6:

[1853] Server: Runs the algorithm that generates rehabilitation menus and creates rehabilitation menus based on the elderly person's motivations (e.g., "playing in the park with my grandchildren").

[1854] Step 7:

[1855] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1856] Step 8:

[1857] Server: Calls the generative AI model and generates questions related to elderly rehabilitation.

[1858] Step 9:

[1859] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1860] Step 10:

[1861] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1862] Step 11:

[1863] Terminal: Sends the entered answer to the server.

[1864] Step 12:

[1865] Server: Records conversation logs in a database.

[1866] Step 13:

[1867] Server: Calls the emotion engine and analyzes the user's input text and voice data to recognize the user's emotional state.

[1868] Step 14:

[1869] Server: Records the user's emotional state (e.g., joy, sadness, anger, depression, etc.) recognized by the emotion engine in the dialogue log.

[1870] Step 15:

[1871] Server: Analyzes the dialogue log data and runs algorithms to assess the user's motivation, rehabilitation progress, and emotional state.

[1872] Step 16:

[1873] On the device: Display feedback based on the analysis results. For example, if the user is feeling down, display an encouraging message like, "Today was difficult, but you did well!"

[1874] Step 17:

[1875] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1876] Step 18:

[1877] Server: Adjust the rehabilitation menu based on the progress data and the user's emotional state. For example, if the achievement rate is low and the user is feeling depressed, reduce the rehabilitation menu.

[1878] Step 19:

[1879] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1880] Step 20:

[1881] User: Enter and submit your thoughts and feedback on the new rehabilitation content.

[1882] Step 21:

[1883] Server: Analyzes the collected feedback and generates the differences to be reflected in the rehabilitation menu.

[1884] The above are the specific processing steps for executing the program in this system. Taking into account the progress and emotional state of each elderly person, the system can automatically adjust appropriate rehabilitation programs and maintain motivation.

[1885] Example 2

[1886] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1887] With conventional rehabilitation systems, it was difficult to customize rehabilitation according to the motivation and emotional state of each elderly person, making it difficult to maintain the elderly's motivation. Furthermore, when providing effective rehabilitation menus and managing progress, automatic adjustments to meet individual needs were not sufficiently performed. This led to the issue of elderly people losing motivation to continue rehabilitation.

[1888] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1889] In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, and means for recognizing the elderly's emotional state using an emotion recognition engine and adjusting the rehabilitation conversation content based on the recognized emotional state. This makes it possible to provide an effective rehabilitation menu and manage progress according to the elderly's individual needs and emotional state while maintaining their motivation for rehabilitation.

[1890] "Elderly person information" refers to personal information such as the elderly person's name, age, health status, and rehabilitation goals.

[1891] A "rehabilitation menu" refers to a customized exercise and activity program based on an elderly person's health condition and rehabilitation goals.

[1892] "Generative AI" refers to an artificial intelligence model that performs natural language generation and is used to generate rehabilitation questions and conversations.

[1893] "Dialogue logs" refer to data that records daily conversations with the generating AI and responses from the elderly.

[1894] "Progress monitoring" refers to the process of regularly observing and evaluating the progress made by older adults through rehabilitation.

[1895] An "emotion recognition engine" refers to a software system that analyzes and recognizes the emotional state of elderly people from their input text or voice.

[1896] "Feedback" refers to the evaluation and encouraging messages given to elderly people based on the results and progress of their rehabilitation program.

[1897] As a specific embodiment of this invention, we propose a rehabilitation support system that effectively supports elderly rehabilitation and maintains their motivation by registering user information, generating daily conversations using a generative AI model, and monitoring progress.

[1898] Key components and their roles

[1899] 1. Registering and managing information on elderly people

[1900] Terminal: Display an input form for elderly person information registration. A software form containing input fields such as "name," "age," "health status," and "rehabilitation goals" is used.

[1901] User: Enter the necessary information into the input form. For example, enter "Name: Yamada Taro", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1902] Terminal: Sends the entered information to the server.

[1903] Server: Stores the received information in a database.

[1904] 2. Creating rehabilitation menus based on individual motivations

[1905] Server: Obtains information about the elderly from the database.

[1906] Server: Runs the algorithm that generates the rehabilitation menu. This algorithm includes the ability to automatically generate a customized menu based on the user's motivation (e.g., "playing in the park with my grandchildren").

[1907] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1908] 3. Generating daily conversations with interactive AI

[1909] Server: Uses a generative AI model (e.g., OpenAI GPT-4) to generate questions related to elderly rehabilitation.

[1910] Terminal: Display the generated question. For example, "Hello, Yamada-san, how much progress have you made in rehabilitation today?"

[1911] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1912] Terminal: Sends the entered answer to the server.

[1913] 4. Recording and analyzing conversation logs

[1914] Server: Records daily interaction logs in a database.

[1915] Server: Analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress, for example, using the Python Pandas library.

[1916] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1917] 5. Progress monitoring and rehabilitation adjustment

[1918] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1919] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1920] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1921] 6. Recognizing and Reflecting User Emotions Using an Emotion Engine

[1922] Server: Calls an emotion engine (e.g., IBM Watson's Tone Analyzer) to analyze the text and voice input from the user and recognize the user's emotional state.

[1923] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[1924] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[1925] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[1926] This system will provide rehabilitation support tailored to the individual needs and emotional state of the elderly, thereby achieving sustainable and effective rehabilitation. As a concrete example, the following prompt sentences are provided:

[1927] Example prompt sentence:

[1928] "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[1929] This system makes it easier for elderly people to maintain motivation for daily rehabilitation, allowing them to proceed with rehabilitation more effectively.

[1930] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1931] System processing flow

[1932] Step 1: Enter and register the senior citizen's information

[1933] Input: The user enters their information into an input form.

[1934] Terminal: Displays an input form for registering elderly information. Input fields include "name," "age," "health status," and "rehabilitation goals."

[1935] User: Enter information in the above fields. For example, enter "Name: Taro Yamada", "Age: 75", "Health condition: Currently undergoing rehabilitation for right knee", and "Rehabilitation goal: Playing in the park with grandchildren".

[1936] Specific operation: When the user clicks the "Submit" button on the input form, the terminal sends this information to the server.

[1937] Output: The entered information is sent to the server and stored.

[1938] Server: Saves the received information in a database and generates a message confirming the save.

[1939] Specific operation: After the data is successfully saved to the database, the server returns a confirmation message to the terminal saying "The information has been saved successfully."

[1940] Step 2: Creating an individual rehabilitation menu

[1941] Input: Information about the elderly stored in a database.

[1942] Server: Obtains information about the elderly from the database.

[1943] Server: Runs the algorithm that generates the rehabilitation menu and generates a rehabilitation menu based on the acquired information and motivation (e.g., "playing in the park with my grandchildren").

[1944] Specific actions: Use Python scripts as algorithms to design rehabilitation menus.

[1945] Output: The generated rehabilitation menu.

[1946] Terminal: Displays the generated rehabilitation menu to the user. For example, "Knee bending and straightening exercises 10 times, 3 sets."

[1947] Specific operation: A rehabilitation menu will be displayed on the device screen, and the user can check it.

[1948] Step 3: Generate daily rehabilitation conversations

[1949] Input: Elderly information and rehabilitation menu.

[1950] Server: Calls a generative AI model (e.g., OpenAI GPT-4) and generates questions related to elderly rehabilitation.

[1951] An example of a specific prompt sentence: The server generates "Hello Yamada-san, how much progress have you made in your rehabilitation today?"

[1952] Output: Generated rehabilitation questions.

[1953] Terminal: Display the generated question.

[1954] Specific behavior: A screen is launched that displays a question to the user.

[1955] Step 4: User answers and submits

[1956] Input: The generated question and the user's text answer.

[1957] User: Responds to the question with text, for example, "Today I completed 3 sets of 10 knee bends and straightens."

[1958] Specific behavior: The user enters an answer and clicks the "Submit" button.

[1959] Output: The user's text response is sent to the server.

[1960] Terminal: Sends the user's answer to the server.

[1961] Step 5: Record and analyze conversation logs

[1962] Input: User response data.

[1963] Server: Records the user's answers in a database.

[1964] Server: Analyzes daily interaction log data and runs algorithms to evaluate user motivation and rehabilitation progress, using, for example, the Python Pandas library.

[1965] Specific operation: The server stores the dialogue log in a database and analyzes the progress data.

[1966] Output: Feedback based on the analysis results.

[1967] On your device: Display feedback based on the analysis, such as a message like "Great job! Try a bit harder next time."

[1968] Step 6: Progress monitoring and rehabilitation adjustment

[1969] Input: Accumulated progress data.

[1970] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[1971] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[1972] What it does: It periodically runs a progress check script and generates menu adjustment results.

[1973] Output: New rehab menu after adjustments.

[1974] Device: Notifies the user of new rehabilitation menus and adjustments. For example, it displays "Knee bending and straightening exercises 8 times, 3 sets."

[1975] Specific operation: A new rehabilitation menu will be displayed on the device and the user will confirm it.

[1976] Step 7: Emotion-aware conversation adjustment

[1977] Input: User input text and voice data.

[1978] Server: Calls an emotion recognition engine (e.g., IBM Watson's Tone Analyzer) to analyze the user's emotional state.

[1979] Server: Based on the recognized emotional state, the generative AI tailors the next rehabilitation conversation. For example, if the user is depressed, it will provide more encouraging messages.

[1980] Specific operation: Uses the analysis results to provide prompts to the generative AI and adjust the conversation content.

[1981] Output: The adjusted conversation.

[1982] Device: Display tailored dialogue, for example, "Today was difficult, but you did well!"

[1983] Server: The user's emotional state is also recorded in the dialogue log, and used as further reference for rehabilitation progress and menu adjustments.

[1984] Specific behavior: The adjusted message is displayed on the screen and recorded as a conversation log for the user.

[1985] (Application example 2)

[1986] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1987] Improving the effectiveness of elderly rehabilitation requires personalized responses tailored to each individual's condition and emotions. However, current rehabilitation systems have difficulty understanding individual situations and emotions in real time and providing appropriate rehabilitation programs based on that understanding. Furthermore, due to a lack of rehabilitation support for elderly people using self-driving cars, there is no monitoring of rehabilitation status or real-time support while traveling. This poses a significant challenge that significantly impacts the rehabilitation progress and motivation of elderly people.

[1988] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering and managing information about the elderly, means for generating an individual rehabilitation menu based on the registered information about the elderly, means for generating and displaying rehabilitation conversations on a daily basis using a generation AI, means for recording and analyzing a dialogue log, means for monitoring the elderly's progress and adjusting the rehabilitation menu, means for recognizing the elderly's emotional state using an emotion engine and optimizing the conversation content and rehabilitation menu, means for displaying the rehabilitation menu in real time on an in-vehicle display when the elderly uses a self-driving car, and means for collecting the elderly's rehabilitation status and impressions through voice input. This enables appropriate rehabilitation support based on the elderly's condition and emotions in real time.

[1989] The "Means for registering and managing information on elderly people" is part of a system that records and stores basic information about elderly people, their health status, rehabilitation goals, etc. in a database, and has the function of accessing and editing that information as needed.

[1990] The "means for generating an individual rehabilitation menu" is part of the system that executes an algorithm that automatically creates a rehabilitation plan tailored to the individual needs and goals of the registered elderly person based on their information.

[1991] The "means for generating rehabilitation conversations using generative AI" is part of a system that uses a generative AI model to automatically generate and display rehabilitation questions and conversations suitable for elderly people on a daily basis.

[1992] The "means for recording and analyzing dialogue logs" is part of a system for recording dialogues with elderly people and analyzing them to evaluate their rehabilitation progress and motivation.

[1993] The "means for monitoring the elderly person's progress and adjusting the rehabilitation menu" is part of a system that continuously monitors the elderly person's rehabilitation progress and dynamically changes and optimizes the rehabilitation menu according to that progress.

[1994] The "means for recognizing emotional states using an emotion engine" is part of a system that has the function of analyzing input text and voice data of elderly people, recognizing their emotional states, and reflecting this in rehabilitation support.

[1995] The "in-vehicle display means for self-driving vehicles" is part of a system that displays rehabilitation menus and related information in real time on the in-vehicle display when elderly people use self-driving vehicles.

[1996] The "means for collecting status through voice input" is part of a system that allows elderly people to input their rehabilitation status and impressions via voice in self-driving cars, etc., and collect that data.

[1997] As an embodiment of the present invention, we propose an elderly rehabilitation support system that is installed in an autonomous vehicle. This system has multiple functions for effectively carrying out rehabilitation within the autonomous vehicle.

[1998] Program Overview

[1999] The system includes the following main features:

[2000] 1. Registering and managing information on elderly people

[2001] 2. Creating an individual rehabilitation menu

[2002] 3. Generating Conversations About Rehabilitation Using Generative AI

[2003] 4. Recording and analyzing conversation logs

[2004] 5. Progress monitoring and rehabilitation adjustment

[2005] 6. Emotional state recognition and reflection using an emotion engine

[2006] 7. Displaying rehabilitation menus on an in-vehicle display

[2007] 8. Collecting rehabilitation status information through voice input

[2008] How the system is implemented

[2009] 1. Registering and managing information on elderly people

[2010] Device: An input form for elderly information is displayed on the in-car display or smartphone. Input fields include "name," "age," "health status," and "rehabilitation goals."

[2011] User: Enter the required information into the input form. Example: "Name: Hanako Suzuki", "Age: 76", "Health condition: Currently undergoing rehabilitation for lower back pain", "Rehabilitation goal: Enjoy traveling with grandchildren"

[2012] Terminal: Sends the entered information to the server.

[2013] Server: Stores the received information in a database.

[2014] 2. Creating an individual rehabilitation menu

[2015] Server: Obtains information about the elderly from the database.

[2016] Server: Executes the algorithm for generating a rehabilitation menu and creates a rehabilitation menu based on the elderly person's goals.

[2017] Terminal: Display the generated rehabilitation menu on the in-car display. Example: "Waist stretching exercises: 5 minutes, 2 sets."

[2018] 3. Generating Conversations About Rehabilitation Using Generative AI

[2019] Server: Calls a generative AI model (e.g., GPT-4) and generates questions related to elderly rehabilitation.

[2020] Terminal: Display the generated question on the display. Example: "Hello, Hanako. How much progress did you make in your rehabilitation today?"

[2021] User: Answers questions verbally.

[2022] Terminal: The input voice data is sent to the server and converted into text using voice recognition software.

[2023] 4. Recording and analyzing conversation logs

[2024] Server: Records daily interaction logs in a database.

[2025] Server: Analyzes the dialogue log data and runs algorithms to evaluate the user's motivation and rehabilitation progress.

[2026] 5. Progress monitoring and rehabilitation adjustment

[2027] Server: Regularly checks rehabilitation progress data and evaluates progress on a weekly basis.

[2028] Server: Adjust the rehabilitation menu based on progress data. For example, if the achievement rate is low, reduce the rehabilitation menu, and if it is high, strengthen the menu.

[2029] Device: Notifies the new rehabilitation menu and adjustment details on the in-car display. Example: "Low back stretching exercises, 5 minutes, 3 sets."

[2030] 6. Emotional state recognition and reflection using an emotion engine

[2031] Server: Calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state.

[2032] Server: Based on the recognized emotional state, the generative AI adjusts the next rehabilitation conversation. For example, if the user is depressed, it will send more encouraging messages.

[2033] Device: Display the adjusted conversation on the in-car display. Example: "Today was tough, but you did well!"

[2034] 7. Displaying rehabilitation menus on an in-vehicle display

[2035] Terminal: When elderly people use self-driving cars, the device provides a function that displays rehabilitation menus and related information in real time on the in-car display.

[2036] 8. Collecting rehabilitation status information through voice input

[2037] Terminal: Collects elderly people's rehabilitation status and impressions through voice input on the in-vehicle display. The voice input data is sent to the server and converted into text format as needed.

[2038] Software and hardware used

[2039] Hardware: In-car display, voice recognition microphone

[2040] Software: Generative AI models (e.g., GPT-4), emotion recognition APIs, speech recognition software (e.g., speech recognition services)

[2041] Prompt Sentence Examples

[2042] "Hello, how much progress have you made in your rehabilitation today?"

[2043] "How are you feeling today? How is your rehabilitation going?"

[2044] This will enable appropriate rehabilitation support based on the elderly person's condition and emotions in real time.

[2045] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2046] Step 1:

[2047] Input: Elderly person's information (name, age, health status, rehabilitation goals)

[2048] Operation: The device displays an input form for elderly information on the in-car display or smartphone. The user enters the necessary information into this input form. For example, "Name: Suzuki Hanako," "Age: 76 years old," "Health condition: Currently undergoing rehabilitation for lower back pain," and "Rehabilitation goal: Enjoy traveling with grandchildren."

[2049] Output: The entered information is sent to the server, which stores the received information in a database.

[2050] Step 2:

[2051] Input: Elderly information retrieved from the database

[2052] Operation: The server retrieves information about the elderly person from the database and runs an algorithm to generate a rehabilitation menu based on that information. An individual rehabilitation menu is generated.

[2053] Output: The generated rehabilitation menu is displayed on the terminal (vehicle display). For example, it may say, "Low back stretching exercises, 5 minutes, 2 sets."

[2054] Step 3:

[2055] Input: Elderly rehabilitation menu progress data

[2056] How it works: The server invokes a generative AI model (e.g., GPT-4) to generate questions related to elderly rehabilitation. The device displays the generated questions on the in-car display. For example, it displays "Hello, Hanako. How much progress have you made in your rehabilitation today?"

[2057] Output: The user responds verbally and the audio data is sent to the server.

[2058] Step 4:

[2059] Input: User's voice data

[2060] How it works: The server uses speech recognition software to convert the voice data into text, which is then stored in a database as a conversation log.

[2061] Output: The conversation log is saved and used for the next conversation generation.

[2062] Step 5:

[2063] Input: Interaction log data

[2064] How it works: The server analyzes the interaction log data and runs algorithms to evaluate the user's motivation and rehabilitation progress. It generates feedback based on the analysis results.

[2065] Output: The device displays feedback, for example, "Great progress today! Good luck next time."

[2066] Step 6:

[2067] Input: Rehabilitation progress data

[2068] How it works: The server periodically checks the rehabilitation progress data and evaluates the progress on a weekly basis. It adjusts the rehabilitation menu based on the progress data. For example, if the achievement rate is low, the rehabilitation menu is reduced, and if it is high, the menu is strengthened.

[2069] Output: The new rehabilitation menu will be displayed on the device. For example, "Low back stretching exercises: 5 minutes, 3 sets."

[2070] Step 7:

[2071] Input: Elderly voice input data

[2072] How it works: The server calls an emotion engine (e.g., emotion recognition API) that analyzes the voice data entered by the user and recognizes the user's emotional state. Based on the recognized emotional state, the generative AI adjusts the content of the next rehabilitation conversation.

[2073] Output: The conversational content corresponding to the emotion will be displayed on the device. For example, "Today was tough, but you did well!"

[2074] Step 8:

[2075] Input: Elderly voice input data

[2076] Operation: When an elderly person uses an autonomous vehicle, the device displays rehabilitation menus and related information in real time on the vehicle's display. It also collects the elderly person's rehabilitation status and impressions through voice input.

[2077] Output: The collected data is sent to the server and used to generate the next rehabilitation menu and adjust the conversation content.

[2078] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2080] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2081] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2082] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2083] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2084] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2085] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2086] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2087] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2088] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2089] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2090] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2091] 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.

[2092] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2093] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2094] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2095] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2096] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2097] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2098] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2099] The following is further disclosed regarding the above embodiment.

[2100] (Claim 1)

[2101] A means for registering and managing information on elderly people;

[2102] A means for generating an individual rehabilitation menu based on information of the registered elderly person;

[2103] A means for generating and displaying rehabilitation conversations on a daily basis using generation AI;

[2104] a means for recording and analyzing a log of the interaction;

[2105] A system that includes a means of monitoring the progress of elderly people and adjusting their rehabilitation menu.

[2106] (Claim 2)

[2107] 10. The system according to claim 1, further comprising means for displaying the generated rehabilitation menu on a terminal and receiving feedback from the elderly person or a caregiver.

[2108] (Claim 3)

[2109] 2. The system according to claim 1, further compr...

Claims

1. A means for registering and managing information on elderly people; A means for generating an individual rehabilitation menu based on information of the registered elderly person; A means for generating and displaying rehabilitation conversations on a daily basis using generation AI; a means for recording and analyzing a log of the interaction; A system that includes a means of monitoring the progress of elderly people and adjusting their rehabilitation menu.

2. The system according to claim 1 , further comprising means for displaying the generated rehabilitation menu on a terminal and receiving feedback from the elderly person or a caregiver.

3. The system according to claim 1, further comprising means for adjusting and displaying the next daily conversation content based on the analysis result of the dialogue log.

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