System

Abstract

A system is provided.SOLUTION: This system includes a means for receiving and storing the basic information of a user, a means for generating and presenting a question to be a mental exercise on the basis of the basic information of the user, a means for receiving and analyzing the answer of the user, a means for evaluating the onset risk and progress condition of dementia on the basis of an analysis result, and a means for providing an evaluation result to the user.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] In today's aging society, the prevention and early detection of dementia are important issues, but there is a lack of easy ways to do this on a daily basis. Furthermore, there are limited means to objectively assess the risk and progression of dementia. Therefore, there is a need for a system that is easy for users to use, continuously monitors cognitive function, and provides appropriate mental exercises. [Means for solving the problem]

[0005] This invention provides a system that receives and stores basic information about a user, generates appropriate brain-training questions based on that basic information, and presents them to the user. It also includes a system that receives and analyzes the user's answers, and a system that evaluates the risk of developing dementia and the progression of dementia based on the analysis results. This system allows users to easily exercise their minds on a daily basis and objectively evaluates the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[0006] "Basic user information" refers to individual data about the user, such as gender, age, and preferences, and is information that the system uses to customize brain teasers and questions.

[0007] "Brain teasers" are questions and quiz-style tasks designed to stimulate cognitive function, and by users answering them, they promote brain activity.

[0008] The "means for generating questions" is a function that automatically creates appropriate brain teasers based on the user's basic information and presents them to the user.

[0009] "User's response" refers to the content that the user inputs or speaks in response to the question presented as a brain teaser.

[0010] "Means for analysis" refers to a function that analyzes the answers provided by users using technologies such as natural language processing, and evaluates their content, accuracy, response time, etc.

[0011] "Means for assessing the risk of developing dementia and the progression of the condition" refers to algorithms or systems that evaluate the user's cognitive function based on the analysis results and determine the risk and progression of dementia.

[0012] "Means for providing evaluation results to users" refers to a function that allows the system to notify the user of the results of analysis and evaluation by displaying or audibly notifying the user.

[0013] "Past conversation data" refers to historical data of answers previously provided by users in interactions with the system and the analysis results thereof, and is information used for continuous monitoring and evaluation. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention utilizes an AI voice chatbot that utilizes generative AI to provide a system that allows users to easily monitor the prevention and progression of dementia. Below, the processing of the system's program is explained in natural language, with specific examples included. The subjects used are server, terminal, and user.

[0036] First-time user registration

[0037] The user enters basic information

[0038] A user launches the application and enters basic information such as gender, age, and interests, which is important for generating brain teasers tailored to the user's needs.

[0039] The device sends the information to the server

[0040] The device formats the basic information entered and sends it to the server, which stores it in a database.

[0041] Start a conversation

[0042] A user starts a conversation

[0043] A conversation session begins when the user taps the "Start Conversation" button.

[0044] The server selects the brain teaser.

[0045] The server runs an algorithm based on the user's basic information to generate a brain teaser that's appropriate for that moment. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[0046] The server sends a question to the device

[0047] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[0048] User Answers

[0049] The user answers the question

[0050] The user answers the presented brain teasers by either text input or voice input.

[0051] The device sends the answer to the server

[0052] The device formats the user's response and sends it to the server.

[0053] Running the diagnostic algorithm

[0054] The server parses the answer

[0055] The server then analyzes the responses it receives using natural language processing (NLP) techniques, which evaluate accuracy, vocabulary diversity, response speed, and other factors.

[0056] The server assesses the risk of dementia

[0057] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, and makes a comprehensive judgment by also referencing past conversation data.

[0058] Providing feedback

[0059] The server generates and sends the evaluation results

[0060] The server generates feedback based on the evaluation results and sends it to the device, including the evaluation results and recommendations for the next brain exercise.

[0061] The device displays feedback to the user

[0062] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[0063] Save data and prepare for next time

[0064] The server stores the conversation history

[0065] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[0066] Specific examples

[0067] First-time registration and conversation flow

[0068] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[0069] The device formats this information and sends it to the server, which stores it in a database.

[0070] The user taps the "Start Conversation" button to begin the session.

[0071] The server generates a question based on the user's information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device.

[0072] The device displays the question to the user and plays it aloud.

[0073] The user answered, "The last crossword I solved was on the theme of 'zoo'."

[0074] The device formats the response and sends it to the server.

[0075] The server analyzes the responses, evaluates cognitive function, generates evaluation results, and sends them to the device.

[0076] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[0077] In this way, this invention allows users to easily perform daily mental exercises and evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user launches the app.

[0081] A user launches the application on a device such as a smartphone or PC. An input form for basic information (gender, age, interests, etc.) is displayed on the screen.

[0082] Step 2:

[0083] The user enters basic information.

[0084] The user enters information such as gender, age, and preferences, and then completes the entry by tapping the "Save" button.

[0085] Step 3:

[0086] The terminal sends the input data to the server.

[0087] The terminal formats the input data and generates a request to send to the server. The request is sent to the server.

[0088] Step 4:

[0089] The server stores user information.

[0090] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[0091] Step 5:

[0092] A user initiates a conversation.

[0093] When the user taps the "Start conversation" button, the device sends a conversation start request to the server.

[0094] Step 6:

[0095] The server selects a brain teaser.

[0096] The server runs an algorithm to generate appropriate brain teasers based on the user's basic information, and sends the selected content to the device.

[0097] Step 7:

[0098] The terminal presents a question to the user.

[0099] The device displays the question received from the server on the screen and plays it back aloud if necessary.

[0100] Step 8:

[0101] The user answers the question.

[0102] The user answers the questions presented to them by text input or voice input.

[0103] Step 9:

[0104] The device sends the response to the server.

[0105] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[0106] Step 10:

[0107] The server parses the response.

[0108] The server analyzes the responses it receives using natural language processing (NLP) technology and evaluates the accuracy of the responses and response time.

[0109] Step 11:

[0110] The server assesses the risk of dementia.

[0111] The server then runs an algorithm based on the analysis results to assess the risk and progression of dementia, taking into account past conversation data as well.

[0112] Step 12:

[0113] The server generates and transmits the evaluation results.

[0114] The server generates the evaluation results and feedback content and sends them to the terminal.

[0115] Step 13:

[0116] The device displays feedback to the user.

[0117] The device displays the feedback received from the server on the screen and plays it back as audio if necessary.

[0118] Step 14:

[0119] The server stores the conversation history.

[0120] The server stores the user's conversation history and evaluation results in a database and organizes the data to be used in the next conversation session.

[0121] The above is a specific processing flow of the system for carrying out the invention.

[0122] Example 1

[0123] 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."

[0124] In recent years, with the progress of the aging society, the prevention and early detection of dementia have become important issues. Conventional dementia prevention systems are insufficient in providing individualized feedback to users, and in many cases, they require specialized operation and knowledge, placing a heavy burden on users. Therefore, there is a need for a system that allows users to easily monitor the prevention and progression of dementia.

[0125] 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.

[0126] In this invention, the server includes means for generating brain teasing questions based on the user's basic information using a generative AI model, means for analyzing the answers received by the server using natural language processing technology, and means for assessing the risk of developing dementia and the progression of the disease based on the analysis results. This enables the user to receive continuous and personalized assessment and feedback of cognitive function without requiring specialized knowledge or operation.

[0127] "User" refers to an individual who uses this system to prevent dementia or monitor its progression.

[0128] "Basic information" refers to information necessary to generate personalized brain teasers, such as the user's gender, age, and preferences.

[0129] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized brain teasers based on basic user information.

[0130] "Brain teasers" refer to quizzes or questions used to assess a user's cognitive function.

[0131] "Questions" refer to specific tasks or quizzes related to brain teasers generated by the generative AI model.

[0132] "Answer" refers to the response or reaction a user provides to a brain teaser.

[0133] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[0134] "Analysis results" refers to the evaluation of user responses analyzed using natural language processing technology.

[0135] "Risk of developing dementia" refers to a measure that assesses the likelihood that a user will develop dementia in the future.

[0136] "Progression" refers to an indicator that shows the progress of dementia and its precursor, mild cognitive impairment.

[0137] "Evaluation results" refers to a comprehensive evaluation and feedback of cognitive function based on the analysis results.

[0138] "Feedback" refers to advice or information provided to users based on the evaluation results.

[0139] MODE FOR CARRYING OUT THE INVENTION

[0140] The purpose of this invention is to enable users to easily prevent dementia and monitor its progression using a system that utilizes generative AI. The configuration and operation of the system are described in detail below.

[0141] System configuration

[0142] This system mainly consists of a server, a terminal, and a user.

[0143] The server integrates a database, generative AI model, and natural language processing technology. Here, MySQL is used for the database, GPT-3 for the generative AI model, and spaCy and BERT for the natural language processing technology.

[0144] A terminal is a device operated by a user, such as a smartphone, tablet, or PC. This terminal is equipped with a communication module for network communication with the server, and also has input devices (keyboard, touch panel) and output devices (display, speaker).

[0145] Users are ordinary individuals who use the system to assess their own cognitive function and receive feedback.

[0146] System operating principle

[0147] Acquisition and storage of basic user information

[0148] When a user first uses the system, they launch the application and enter basic information such as gender, age, and interests. For example, if the user is female, 65 years old, and enjoys crossword puzzles, the device formats this information and sends it in JSON format to the server. The server receives this information and stores it in a MySQL database.

[0149] Brain teaser generation

[0150] When the user taps the "Start Conversation" button, the server uses the pre-stored basic information to create a prompt sentence that generates a brain teaser using a generative AI model (GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input into the generative AI model. An example of the generated brain teaser would be a question such as "Tell me about the crossword puzzle you solved recently."

[0151] Posting questions and submitting answers

[0152] The server sends the generated question to the device, which displays it to the user, and the user answers by text input or voice input, which the device then sends back to the server in JSON format.

[0153] Analysis of responses and assessment of dementia risk

[0154] The server analyzes the received responses using natural language processing technology (spaCy or BERT). Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression. This assessment also references past conversation data to make a comprehensive judgment.

[0155] Providing and storing feedback

[0156] The server generates feedback based on the evaluation results and sends it to the device. The device displays this feedback to the user and can also notify them by voice. For example, it may include something like, "Your cognitive function is good. Let's continue next time." Finally, the server stores this conversation history and the evaluation results in a database.

[0157] Specific examples

[0158] 1. The user starts the application and enters the following basic information: "Gender: Female," "Age: 65," and "Hobbies: Crossword puzzles."

[0159] 2. The device formats this information and sends it to the server, which stores it in a database.

[0160] 3. The user taps the "Start Conversation" button to begin the session.

[0161] 4. Based on the user information, the server uses the generative AI model to input the prompt sentence, "Generate a brain teaser for a 65-year-old female user who likes crossword puzzles."

[0162] 5. The generative AI model generates a question such as "Tell me about the crossword puzzle you solved recently," and the server sends this to the device.

[0163] 6. The device displays it to the user and plays it aloud.

[0164] 7. A user answers, "The last crossword puzzle I solved was about the zoo."

[0165] 8. The device formats the response and sends it to the server.

[0166] 9. The server analyzes the answers, evaluates cognitive function, generates evaluation results, and sends them to the device.

[0167] 10. The device displays feedback to the user saying, "Your cognitive function is good. Let's continue next time."

[0168] In this way, this invention allows users to exercise their minds on a daily basis and easily evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1:

[0171] A user starts the application and enters basic information (gender, age, and interests). For example, the user enters gender "female," age "65," and interest "crossword puzzles." This inputs data tailored to the individual user.

[0172] Input: Gender "Female", Age "65", Interest "Crossword puzzles"

[0173] Output: Screen display where basic information is entered

[0174] ---

[0175] Step 2:

[0176] The device formats this basic information and sends it to the server in JSON format. For example, it generates the following JSON data:

[0177] json

[0178] {

[0179] "gender": "female",

[0180] "age": 65,

[0181] "preferences": ["crossword puzzle"]

[0182] }

[0183] Input: Basic information entered by the user

[0184] Output: JSON format data is sent to the server

[0185] ---

[0186] Step 3:

[0187] The server stores the received basic information in a database, for example, a MySQL database.

[0188] Input: Basic information data in JSON format

[0189] Output: User information stored in the database

[0190] ---

[0191] Step 4:

[0192] The user taps the "Start Conversation" button to begin a conversation session. The app catches this event and proceeds to the next processing step.

[0193] Input: Tap the "Start Conversation" button

[0194] Output: Start trigger for a conversation session

[0195] ---

[0196] Step 5:

[0197] The server uses the pre-stored basic information to create a prompt sentence to generate a brain teaser using a generative AI model (e.g., GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input to the generative AI model.

[0198] Input: User basic information stored in the database

[0199] Output: The prompt sentence that is fed into the generative AI model

[0200] ---

[0201] Step 6:

[0202] A generative AI model generates brain teasers, such as questions like, "Tell me about the last crossword puzzle you solved."

[0203] Input: prompt statement

[0204] Output: Generated brain teaser (question)

[0205] ---

[0206] Step 7:

[0207] The server sends the generated question to the terminal, which then displays the question to the user. For example, the terminal displays the question "Tell me about the crossword puzzle you solved recently" on the screen.

[0208] Input: Generated brain teaser (question)

[0209] Output: The question that is displayed to the user

[0210] ---

[0211] Step 8:

[0212] The user answers by text or voice input. For example, the user might answer by text input, "The last crossword puzzle I solved was on the theme 'zoo.'"

[0213] Input: User's answer

[0214] Output: The input response data

[0215] ---

[0216] Step 9:

[0217] The device formats the user's answer and sends it to the server in JSON format, for example generating the following JSON data:

[0218] json

[0219] {

[0220] "answer": "The last crossword I solved was about a zoo."

[0221] }

[0222] Input: User's answer

[0223] Output: JSON formatted answer data is sent to the server

[0224] ---

[0225] Step 10:

[0226] The server analyzes the received answers using natural language processing techniques (e.g., spaCy and BERT), evaluating the accuracy of the answers, vocabulary diversity, and response speed.

[0227] Input: JSON formatted response data

[0228] Output: Analysis results

[0229] ---

[0230] Step 11:

[0231] The server then runs an algorithm based on the analysis results to assess the user's dementia risk and progression, taking into account past conversation data.

[0232] Input: Analysis results and past conversation data

[0233] Output: Assessment results of dementia risk and progression

[0234] ---

[0235] Step 12:

[0236] The server generates feedback based on the evaluation results and sends it to the device. For example, it generates feedback such as "Your cognitive function is good. Let's continue next time."

[0237] Input: Dementia risk and progression assessment results

[0238] Output: Feedback data

[0239] ---

[0240] Step 13:

[0241] The device will display feedback to the user, such as an on-screen message like "Your cognitive function is good. Let's continue next time." Audio notifications are also possible.

[0242] Input: Feedback data

[0243] Output: Feedback that is displayed to the user

[0244] ---

[0245] Step 14:

[0246] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[0247] Input: Conversation history and evaluation results

[0248] Output: History and evaluation results stored in a database

[0249] (Application example 1)

[0250] 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."

[0251] In modern society, the prevention and progression management of dementia have become important issues as the population ages. It is particularly difficult for elderly people who visit physical stores to easily check their cognitive function. There is also a need for fun, sustained mental exercises in daily life to maintain cognitive function. However, existing systems are unable to meet these needs, so it is necessary to develop a method that allows for easy checking and continuous monitoring of cognitive function.

[0252] 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.

[0253] In this invention, the server includes means for receiving and storing basic information of a user, means for generating and presenting brain teasers based on the user's basic information, means for receiving and analyzing the user's answers, means for evaluating the risk of developing dementia and the state of progression based on the analysis results, means for providing the evaluation results to the user, and means for the user to check their cognitive function when visiting a physical store. This enables elderly people visiting a physical store to easily check their cognitive function using their smartphone when they visit, making it possible to engage in enjoyable and sustainable dementia prevention efforts.

[0254] "Basic user information" refers to basic personal data such as gender, age, and preferences that are necessary for the system to function.

[0255] "Brain teasers" are questions or tasks presented to users with the purpose of activating or assessing cognitive function.

[0256] "Means for generating and presenting questions" refers to a method or function for generating optimal brain teasers based on the user's basic information and displaying them to the user.

[0257] The "means for receiving and analyzing user responses" refers to a method or function for receiving the content of a user's response and analyzing it to evaluate the content.

[0258] "Means for assessing the risk of developing dementia and the progression of dementia based on analysis results" refers to a method or function that uses the analysis results of a user's responses to determine and evaluate a person's risk of dementia and the progression of that person's dementia.

[0259] "Means for providing evaluation results to users" refers to the method or function for communicating the evaluation results to users.

[0260] "Means for checking cognitive function when a user visits a physical store" refers to a method or function for evaluating a user's cognitive function on the spot when the user visits a physical store.

[0261] This invention provides a system for easily assessing the cognitive function of elderly people visiting physical stores. The system generates brain-teasing questions based on the user's basic information, and assesses the user's risk of dementia based on their answers. Specific embodiments of the system are described below.

[0262] System configuration

[0263] The system consists of three elements: a server, a terminal, and a user.

[0264] Hardware and Software

[0265] The servers have high-performance computing power, store and analyze data, and use natural language processing (NLP) and generative AI models (e.g., GPT-3).

[0266] The devices used by users are smartphones or tablets, and the system uses Google Text-to-Speech (TTS) for voice input and Google Cloud Natural Language API for conversation analysis.

[0267] The users are elderly people who interact with the system using terminals when they visit a physical store.

[0268] User registration and basic information acquisition

[0269] When a user uses the system for the first time, they enter basic information (gender, age, preferences), which is sent from the terminal to the server and stored in the database.

[0270] Generating and presenting brain teasers

[0271] The server uses a generative AI model to generate brain teasing questions based on the stored basic information. The questions are dynamically changed according to the user's preferences. The generated questions are sent to the device and presented to the user.

[0272] Example prompt sentence:

[0273] "Tell me about the last crossword puzzle you solved."

[0274] User responses and analysis

[0275] Users answer the questions posed to them, and the answers are sent to the server via their device for analysis. The server uses natural language processing (NLP) technology to evaluate the accuracy of the answers, the diversity of vocabulary, and the speed of response.

[0276] Dementia risk assessment and feedback

[0277] Based on the analysis results, the server uses a generative AI model to assess dementia risk. The assessment results are sent to the device and presented to the user. Feedback includes recommendations for the next brain exercise.

[0278] Use in physical stores

[0279] Users visiting a physical store interact with the system using a terminal. For example, they can do mental exercises while waiting in a cafe or supermarket to check the state of their cognitive function. The system is intended for use in physical stores, and automatically starts the cognitive function check process when the user arrives at the store.

[0280] For example, an elderly person visiting a cafe can launch a "Shopping Support App" and answer questions about a crossword puzzle. Based on their answers, their cognitive function is evaluated and the results are fed back to them.

[0281] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0282] Step 1:

[0283] The user starts the application and enters basic information, such as gender, age, and preferences. This provides the user's basic information.

[0284] Step 2:

[0285] The device formats the basic information and sends it to the server. Specifically, it converts the user's input data into JSON format and sends it to the server using an HTTP request. At this stage, the server saves the basic information in a database.

[0286] Step 3:

[0287] A conversation session begins when the user taps the "Start conversation" button, which allows the user's actions to be obtained as input.

[0288] Step 4:

[0289] The server generates brain teasers appropriate for the user at that time based on the user's basic information. Specifically, it generates questions using a generative AI model. In this generation process, the server dynamically creates questions based on the user's preferences and past data.

[0290] Step 5:

[0291] The generated question is sent to the terminal and presented to the user. The terminal displays the question and, if necessary, outputs it aloud. The user confirms the question and answers it by voice or text.

[0292] Step 6:

[0293] The user's answer is sent from the device to the server. The device formats the user's answer and sends it to the server using an HTTP request, which causes the server to receive the user's answer as input.

[0294] Step 7:

[0295] The server analyzes the user's answers. Specifically, it uses natural language processing (NLP) technology to evaluate the accuracy of the answers, vocabulary diversity, response speed, etc. The analysis can be performed using the Google Cloud Natural Language API.

[0296] Step 8:

[0297] Based on the analysis results, the server performs a dementia risk assessment. A generative AI model is used to comprehensively evaluate the analysis results and past data. This assessment determines the risk of developing dementia and the progression of the condition.

[0298] Step 9:

[0299] The evaluation results and feedback are sent from the server to the device and presented to the user. The device displays the evaluation results and provides audio feedback, including recommendations for the next brain exercise.

[0300] Step 10:

[0301] The server saves the conversation history and evaluation results in a database, which enables continuous monitoring in subsequent sessions. This saving process includes inserting data into the database.

[0302] The above processing steps allow users to easily check their cognitive function in a physical store, enabling them to continuously work on preventing dementia.

[0303] 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.

[0304] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. The following explains the system's program processing in detail, including specific examples.

[0305] First-time user registration

[0306] The user enters basic information

[0307] Users launch the application on their smartphones, PCs, or other devices and enter basic information such as gender, age, and interests. This information is used to provide users with brain exercises tailored to their needs.

[0308] The device sends the information to the server

[0309] The device formats the user's basic information and sends it to the server, which then stores it in a database.

[0310] Start a conversation

[0311] A user starts a conversation

[0312] When the user taps the "Start Conversation" button, the device sends a conversation start request to the server.

[0313] The server selects the brain teaser.

[0314] The server runs an algorithm to generate the most suitable brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 and likes crossword puzzles, the server will select a word-based quiz.

[0315] The server sends a question to the device

[0316] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[0317] User responses and emotion recognition

[0318] The user answers the question

[0319] The user answers the questions presented to them, and can do so by text input or voice input.

[0320] The device sends the answer to the server

[0321] The terminal formats the user's response data and sends it to the server.

[0322] The server recognizes emotions using an emotion engine

[0323] When the server analyzes the received answer data, it uses an emotion engine to recognize the user's emotional state. For example, if the answer sounds nervous, it recognizes the emotion "nervous."

[0324] Running the diagnostic algorithm

[0325] The server analyzes the responses and emotional data

[0326] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of the answers, reaction time, emotional state, etc.

[0327] The server assesses the risk of dementia

[0328] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past conversation data and emotional data.

[0329] Providing feedback

[0330] The server generates and sends the evaluation results

[0331] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[0332] The device displays feedback to the user

[0333] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[0334] Save data and prepare for next time

[0335] The server stores conversation history and emotion data

[0336] The server stores the user's conversation history, evaluation results, and emotion data in a database, which allows for continuous monitoring in the next conversation session.

[0337] Specific examples

[0338] First-time registration and conversation flow

[0339] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[0340] The device formats this information and sends it to the server, which stores it in a database.

[0341] The user taps the "Start Conversation" button and the conversation session begins.

[0342] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the terminal.

[0343] The terminal displays the question to the user and plays it aloud.

[0344] A user answers, "The last crossword puzzle I solved was on the theme 'zoo'."

[0345] The device formats the response and sends it to the server.

[0346] The server analyzes the answers and uses an emotion engine to recognize the user's emotions (e.g., "fun").

[0347] The server evaluates cognitive function, generates feedback based on the evaluation results, and sends it to the device.

[0348] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[0349] The server stores the conversation history and emotion data in a database in preparation for the next conversation session.

[0350] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[0351] The processing flow will be explained below.

[0352] Step 1:

[0353] The user launches the app.

[0354] When a user launches the application on a device such as a smartphone or PC, an input form for basic information (gender, age, interests, etc.) appears on the screen.

[0355] Step 2:

[0356] The user enters basic information.

[0357] The user enters information such as gender, age, and preferences, and then taps the "Save" button to complete the entry.

[0358] Step 3:

[0359] The terminal sends the input data to the server.

[0360] The terminal formats the input data and generates a request to be sent to the server, and the request is sent.

[0361] Step 4:

[0362] The server stores user information.

[0363] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[0364] Step 5:

[0365] A user initiates a conversation.

[0366] The user taps the "Start conversation" button, and the device sends a conversation start request to the server.

[0367] Step 6:

[0368] The server selects a brain teaser.

[0369] The server runs an algorithm based on the user's basic information and past conversation data to generate the most appropriate brain teaser. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[0370] Step 7:

[0371] The server sends a question to the terminal.

[0372] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[0373] Step 8:

[0374] The user answers the question.

[0375] The user answers the questions posed, and the answers can be entered by text or voice.

[0376] Step 9:

[0377] The device sends the response to the server.

[0378] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[0379] Step 10:

[0380] The server recognizes emotions using an emotion engine.

[0381] The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state, such as "tension" or "joy" based on the tone of voice and the content of the text.

[0382] Step 11:

[0383] The server analyzes the responses and sentiment data.

[0384] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of responses, reaction time, perceived emotional state, and more.

[0385] Step 12:

[0386] The server assesses the risk of dementia.

[0387] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, taking into account past conversation data and emotion data to make a comprehensive assessment.

[0388] Step 13:

[0389] The server generates and transmits the evaluation results.

[0390] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[0391] Step 14:

[0392] The device displays feedback to the user.

[0393] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[0394] Step 15:

[0395] The server stores conversation history and emotion data.

[0396] The server stores the user's conversation history, evaluation results, and emotion data in a database, which organizes the data for use in the next conversation session.

[0397] The above is a specific processing flow of the system of the present invention that combines an emotion engine. This allows users to exercise their minds on a daily basis, objectively and specifically evaluate the state of their cognitive function, and receive more personalized feedback and improvement measures.

[0398] Example 2

[0399] 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."

[0400] Conventional dementia prevention and monitoring tools assess users' cognitive function primarily based on data entry accuracy and reaction time, without taking into account their emotional state. This makes it difficult to understand the user's condition in more detail or provide personalized feedback. This makes it difficult to detect the risk and progression of dementia early and develop appropriate countermeasures.

[0401] 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.

[0402] In this invention, the server includes means for receiving and storing basic information of the user, means for generating and presenting brain teasing questions based on the basic information of the user, and means for recognizing the emotional state of the user from the response data, thereby enabling the provision of personalized feedback that takes the emotional state into consideration.

[0403] "Basic user information" refers to basic attribute information about a user, such as the user's gender, age, and preferences.

[0404] "Means for storing" refers to means for storing received information in a storage system such as a database.

[0405] "Brain teasers" are tasks such as quizzes and questions designed to stimulate and assess the user's cognitive function.

[0406] A "means for generating questions" is an algorithm or system that generates optimal brain-teasing questions based on the user's basic information and past data.

[0407] The "presentation means" is a means for presenting the generated question to the user visually or audibly.

[0408] A "means for receiving a response" is a means for receiving the text or voice input by the user and formatting it into an analyzable format.

[0409] The "means of analysis" refers to the means of analyzing the received response data using natural language processing or an emotion recognition engine.

[0410] A "means for assessing the risk of developing dementia and the progression of the condition" is an algorithm or system that assesses a user's risk of dementia and its progression based on the results of the analysis.

[0411] "Means for providing evaluation results" refers to means for notifying users of the evaluation results visually or audibly.

[0412] "Means for recognizing emotional states" refers to means for identifying emotions from user response data using natural language processing and voice analysis.

[0413] The "means for comprehensively adjusting the evaluation results" is an algorithm or system that performs a comprehensive evaluation including the recognized emotional state and adjusts the final result.

[0414] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. Specific embodiments of the system are described below.

[0415] The system of the present invention is used by users via devices such as smartphones or PCs. The user launches the application and enters basic information such as gender, age, and preferences. The device formats this information and sends it to the server. The server stores the received information in a database and uses it for subsequent processing.

[0416] In this system, when the user starts a conversation, the server generates optimal brain teasing questions based on the user's basic information and past conversation data. The generated questions are sent to the device and presented to the user. The user answers the questions by text input or voice input.

[0417] The device formats the user's response data and sends it to the server. The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state. Based on the data obtained from this analysis, the server evaluates the user's cognitive function. This makes it possible to assess the risk of dementia and monitor its progression.

[0418] The evaluation results are sent from the server to the device, where they are displayed to the user. A voice notification is also possible. The server then stores the user's conversation history, evaluation results, and recognized emotion data in a database for future use in the next session.

[0419] Specific examples

[0420] For example, a 65-year-old woman who enjoys crossword puzzles launches the app and enters her gender, age, and interests. The device formats this information and sends it to the server. After the server stores the information in a database, the user can tap the "Start Conversation" button to begin a conversation.

[0421] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device. The device displays the question to the user and plays it back aloud. When the user answers, "The crossword puzzle I solved recently was on the theme of 'zoo,'" the device formats the answer and sends it to the server.

[0422] The server analyzes the response data and uses an emotion engine to recognize the user's emotion. For example, it recognizes "enjoyment." The server then evaluates cognitive function based on the analysis results, generates feedback based on the evaluation results, and sends it to the device. The device then displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[0423] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[0424] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0425] Step 1:

[0426] The user enters basic information

[0427] Input: The user enters basic information such as gender, age, and preferences into an application on a smartphone or PC.

[0428] What it does: The user fills out a form on the app's start screen, filling out their information, such as being a 65-year-old woman who likes crossword puzzles.

[0429] Output: The input basic information is prepared as data for further processing.

[0430] Step 2:

[0431] The device sends the information to the server

[0432] Input: Basic information entered by the user.

[0433] What happens: The device takes the input information, formats it into JSON, and sends the formatted data to the server over HTTPS.

[0434] Output: Formatted basic information data received by the server.

[0435] Step 3:

[0436] The server stores basic information in a database

[0437] Input: Basic information data sent from the device.

[0438] Specific operation: The server stores the received basic information in a database, for example, a NoSQL database.

[0439] Output: User basic information stored in the database.

[0440] Step 4:

[0441] A user starts a conversation

[0442] Input: The action a user takes to start a conversation, such as tapping a button.

[0443] Specific operation: The user taps the "Start conversation" button on the app screen, which causes the device to send a conversation start request to the server.

[0444] Output: A conversation initiation request sent to the server.

[0445] Step 5:

[0446] The server chooses the best brain teaser

[0447] Input: User basic information and past conversation data.

[0448] How it works: The server uses a generative AI model to select the most suitable brain exercise for a user based on their basic information and past conversation data. For example, if a 65-year-old woman who likes crossword puzzles is selected, the server will select a crossword-related quiz.

[0449] Output: Optimal brain teaser question data.

[0450] Step 6:

[0451] The server sends a question to the device

[0452] Input: Selected brain teaser question data.

[0453] Specific operation: The server sends the generated question data to the terminal, which then displays the received question to the user and plays it back aloud.

[0454] Output: The question that is presented to the user.

[0455] Step 7:

[0456] The user answers the question

[0457] Input: The question posed by the server.

[0458] What happens: The user answers the question by typing or speaking, for example, "The last crossword puzzle I solved was about the zoo."

[0459] Output: User response data.

[0460] Step 8:

[0461] The device sends the answer to the server

[0462] Input: User response data.

[0463] Specific operation: The device receives the user's response data, formats it into JSON format again, and sends this formatted data to the server using HTTPS.

[0464] Output: The formatted response data received by the server.

[0465] Step 9:

[0466] The server recognizes emotions using an emotion engine

[0467] Input: Response data sent from the device.

[0468] Specific operation: The server analyzes the received response data and uses the emotion engine to recognize the user's emotional state, for example, identifying emotions such as "joy" or "tension."

[0469] Output: Recognized emotion data.

[0470] Step 10:

[0471] The server analyzes the responses and emotional data

[0472] Input: User response data and sentiment data.

[0473] How it works: The server uses natural language processing technology to analyze the content of the answers, and simultaneously processes the response time and emotional state, thereby evaluating the accuracy of the answers, response time, and emotional state.

[0474] Output: User's cognitive function assessment data.

[0475] Step 11:

[0476] The server assesses the risk of dementia

[0477] Input: User's cognitive assessment data.

[0478] Specific operation: Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past data as well.

[0479] Output: Dementia risk assessment results.

[0480] Step 12:

[0481] The server generates and sends the evaluation results

[0482] Input: Dementia risk assessment results.

[0483] Specific operation: The server generates feedback on the cognitive function assessment results and recommended future brain exercises and sends them to the device.

[0484] Output: Feedback data sent to the device.

[0485] Step 13:

[0486] The device displays feedback to the user

[0487] Input: Feedback data sent by the server.

[0488] Specific operation: The device receives the feedback data and notifies the user by text or voice, so that the user receives the feedback.

[0489] Output: The feedback that is presented to the user.

[0490] Step 14:

[0491] The server stores conversation history and emotion data

[0492] Input: User conversation history, rating results, and emotion data.

[0493] What happens: The server stores this data in a database and keeps it available for the next session.

[0494] Output: User history and emotion data stored in a database.

[0495] In this way, the system of the present invention comprehensively analyzes the user's basic information, response data, and emotional state, and provides a detailed assessment of cognitive function and personalized feedback, thereby enabling the prevention of dementia risk and early detection.

[0496] (Application example 2)

[0497] 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."

[0498] Conventional dementia prevention systems simply provide mental exercises without considering the user's emotional state. This results in a lack of feedback that reflects the user's emotional changes, reducing the accuracy of long-term dementia prevention and progress monitoring. It is necessary to provide dementia prevention training for the elderly as a digital assistant in brick-and-mortar stores, aiming to provide detailed support tailored to each individual user.

[0499] The identification process by the identification 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 receiving and storing basic information about the user, means for generating and presenting brain teasing questions based on the user's basic information, means for receiving and analyzing the user's answers, means for analyzing the user's emotions using an emotion recognition engine, and means for generating feedback according to data acquired based on the user's emotional state. This makes it possible to provide dementia prevention measures according to the individual emotional state of the user.

[0500] A "user" is someone who uses the system to exercise their mind to prevent dementia.

[0501] "Basic information" refers to personal data such as gender, age, and preferences provided by the user.

[0502] "Means for storing" refers to functions or devices that allow users' basic information to be saved and reused.

[0503] "Generating means" refers to algorithms or devices that generate appropriate brain teasers or questions based on basic information about the user.

[0504] "Presentation means" refers to the function or device for displaying the generated brain teasers or questions to the user.

[0505] "Means for analysis" refers to the algorithms or devices used to evaluate user responses and analyze their content and accuracy.

[0506] An "emotion recognition engine" is an artificial intelligence technology or software that analyzes a user's voice or text and recognizes their emotional state.

[0507] "Means for generating feedback" refers to algorithms or devices that provide appropriate responses or advice to users based on analysis results and emotional data.

[0508] "Emotional state" refers to a state that indicates a user's changes in feelings or mood.

[0509] "Dementia risk assessment" refers to algorithms and devices used to assess a user's risk of developing dementia and the progression of the condition.

[0510] "Means for providing assessment results" refers to functions and devices for notifying users of the assessed dementia risk and progression.

[0511]

[0512] This invention builds a system that acts as a digital assistant in brick-and-mortar stores, providing brain exercise sessions aimed at preventing dementia, particularly for the elderly. The system operates as a smartphone application and provides individualized feedback based on the user's basic information and emotional recognition, enabling more effective dementia prevention and progression monitoring.

[0513] System configuration

[0514] The system consists of the following components:

[0515] User device: Smartphone or tablet used by the user

[0516] Server: A remote server that processes and analyzes data

[0517] Database: A database that stores basic information about users, past conversation data, and emotional data.

[0518] What the program does

[0519] When a user uses the app for the first time, they enter basic information such as gender, age, and interests on their device. The device sends this information to the server, which then stores it in a database.

[0520] When you tap the "Start Conversation" button in the application, your device sends a conversation start request to the server, which then generates the optimal brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 years old and enjoys crossword puzzles, the server will generate a question such as "Tell me about the crossword puzzle you solved recently."

[0521] The generated question is sent to the device and presented to the user. When the user answers, the answer data is sent to the server as text or voice. The server uses natural language processing technology and an emotion engine to analyze the answer and recognize the user's emotional state. For example, if the answer sounds fun, the emotion of "fun" is recognized.

[0522] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data, and generates feedback based on the results. The feedback is sent to the device and displayed to the user. For example, a positive message such as "Your cognitive function is good. Let's continue next time" is provided.

[0523] Technology used

[0524] Emotion Recognition Engine: Uses generative AI models and natural language processing techniques to recognize emotional states. Examples include the spacy and transformers libraries.

[0525] Database: A database management system for storing user basic information and past conversation data. For example, an SQL or NoSQL database.

[0526] Server technology: Back-end servers for data processing and analysis. Examples include Python-based servers and cloud services.

[0527] Specific examples

[0528] Example 1:

[0529] The user launches the app and enters their gender (female), age (70), and preference (puzzles). The app begins asking questions such as, "Tell me about the puzzle you solved recently." The user answers, "I solved a number puzzle recently," and the answer is sent to the server. The emotion engine recognizes "fun," and provides feedback such as, "Cognitive function is good. Please continue to enjoy the app."

[0530] Example 2:

[0531] prompt:

[0532] User ID = "id1"

[0533] Question = Start conversation session (user ID)

[0534] Brain teaser presentation (user ID, question)

[0535] Answer = "The puzzle I solved recently was about flowers."

[0536] Emotion result = Answer and Emotion Recognition(User ID, Answer)

[0537] Evaluation result = Cognitive function evaluation (user ID)

[0538] print(f"Feedback: {Evaluation result}")

[0539]

[0540] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0541]

[0542] Step 1:

[0543] The user starts the application on the device and enters basic information (gender, age, interests).

[0544] Input: The user enters basic information such as gender, age, and preferences.

[0545] Data processing: The terminal formats the basic information entered and sends it to the server.

[0546] Output: Basic formatted information is sent to the server.

[0547] Step 2:

[0548] The server stores the basic information in a database.

[0549] Input: Basic information sent from the device.

[0550] Data processing: The server records the basic information received in a database.

[0551] Output: Basic information stored in the database.

[0552] Step 3:

[0553] The user taps the Start Conversation button.

[0554] Input: The user taps the "Start Conversation" button on their device.

[0555] Data processing: The terminal sends a conversation start request to the server.

[0556] Output: A conversation initiation request is sent to the server.

[0557] Step 4:

[0558] The server generates optimal brain exercises based on the user's basic information and past conversation data.

[0559] Input: Basic information about the user and past conversation data.

[0560] Data processing: The server uses the generated AI model to generate the optimal brain teaser for the user.

[0561] Output: The generated brain teaser.

[0562] Step 5:

[0563] The server sends the generated brain teasers to the terminal, which then presents them to the user.

[0564] Input: A server-generated brain teaser.

[0565] Data processing: The device formats the brain teasers received from the server for display.

[0566] Output: The brain teaser displayed on the terminal.

[0567] Step 6:

[0568] The user answers the questions presented.

[0569] Input: The user inputs the answer into the terminal by voice or text.

[0570] Data processing: The terminal formats the entered answers and sends them to the server.

[0571] Output: The formatted response data is sent to the server.

[0572] Step 7:

[0573] The server analyzes the response data and recognizes the user's emotions using an emotion recognition engine.

[0574] Input: Response data sent from the device.

[0575] Data processing: The server analyzes the responses using natural language processing technology and an emotion recognition engine to recognize the emotional state.

[0576] Output: Analysis results and emotion data.

[0577] Step 8:

[0578] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data.

[0579] Input: Analysis results and emotion data.

[0580] Data processing: The server evaluates the dementia risk using an evaluation algorithm.

[0581] Output: Dementia risk assessment results.

[0582] Step 9:

[0583] The server generates feedback based on the evaluation results and sends it to the device.

[0584] Input: Dementia risk assessment results.

[0585] Data processing: The server generates feedback based on the evaluation results.

[0586] Output: The generated feedback is sent to the device.

[0587] Step 10:

[0588] The device displays the feedback to the user.

[0589] Input: Feedback sent by the server.

[0590] Data processing: The device formats the feedback for display.

[0591] Output: Feedback displayed on the device.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Second embodiment]

[0596] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] In the smart glasses 214, 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.

[0607] 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."

[0608] This invention utilizes an AI voice chatbot that utilizes generative AI to provide a system that allows users to easily monitor the prevention and progression of dementia. Below, the processing of the system's program is explained in natural language, with specific examples included. The subjects used are server, terminal, and user.

[0609] First-time user registration

[0610] The user enters basic information

[0611] A user launches the application and enters basic information such as gender, age, and interests, which is important for generating brain teasers tailored to the user's needs.

[0612] The device sends the information to the server

[0613] The device formats the basic information entered and sends it to the server, which stores it in a database.

[0614] Start a conversation

[0615] A user starts a conversation

[0616] A conversation session begins when the user taps the "Start Conversation" button.

[0617] The server selects the brain teaser.

[0618] The server runs an algorithm that uses basic information about the user to generate a brain teaser that's appropriate for that moment. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[0619] The server sends a question to the device

[0620] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[0621] User Answers

[0622] The user answers the question

[0623] The user answers the presented brain teasers by either text input or voice input.

[0624] The device sends the answer to the server

[0625] The device formats the user's response and sends it to the server.

[0626] Running the diagnostic algorithm

[0627] The server parses the answer

[0628] The server then analyzes the responses it receives using natural language processing (NLP) techniques, which evaluate accuracy, vocabulary diversity, response speed, and other factors.

[0629] The server assesses the risk of dementia

[0630] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, and makes a comprehensive judgment by also referencing past conversation data.

[0631] Providing feedback

[0632] The server generates and sends the evaluation results

[0633] The server generates feedback based on the evaluation results and sends it to the device, including the evaluation results and recommendations for the next brain exercise.

[0634] The device displays feedback to the user

[0635] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[0636] Save data and prepare for next time

[0637] The server stores the conversation history

[0638] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[0639] Specific examples

[0640] First-time registration and conversation flow

[0641] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[0642] The device formats this information and sends it to the server, which stores it in a database.

[0643] The user taps the "Start Conversation" button to begin the session.

[0644] The server generates a question based on the user's information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device.

[0645] The device displays the question to the user and plays it aloud.

[0646] The user answered, "The last crossword I solved was on the theme of 'zoo'."

[0647] The device formats the response and sends it to the server.

[0648] The server analyzes the responses, evaluates cognitive function, generates evaluation results, and sends them to the device.

[0649] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[0650] In this way, this invention allows users to easily perform daily mental exercises and evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[0651] The processing flow will be explained below.

[0652] Step 1:

[0653] The user launches the app.

[0654] A user launches the application on a device such as a smartphone or PC. An input form for basic information (gender, age, interests, etc.) is displayed on the screen.

[0655] Step 2:

[0656] The user enters basic information.

[0657] The user enters information such as gender, age, and preferences, and then completes the entry by tapping the "Save" button.

[0658] Step 3:

[0659] The terminal sends the input data to the server.

[0660] The terminal formats the input data and generates a request to send to the server. The request is sent to the server.

[0661] Step 4:

[0662] The server stores user information.

[0663] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[0664] Step 5:

[0665] A user initiates a conversation.

[0666] When the user taps the "Start conversation" button, the device sends a conversation start request to the server.

[0667] Step 6:

[0668] The server selects a brain teaser.

[0669] The server runs an algorithm to generate appropriate brain teasers based on the user's basic information, and sends the selected content to the device.

[0670] Step 7:

[0671] The terminal presents a question to the user.

[0672] The device displays the question received from the server on the screen and plays it back aloud if necessary.

[0673] Step 8:

[0674] The user answers the question.

[0675] The user answers the questions presented to them by text input or voice input.

[0676] Step 9:

[0677] The device sends the response to the server.

[0678] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[0679] Step 10:

[0680] The server parses the response.

[0681] The server analyzes the responses it receives using natural language processing (NLP) technology and evaluates the accuracy of the responses and response time.

[0682] Step 11:

[0683] The server assesses the risk of dementia.

[0684] The server then runs an algorithm based on the analysis results to assess the risk and progression of dementia, taking into account past conversation data as well.

[0685] Step 12:

[0686] The server generates and transmits the evaluation results.

[0687] The server generates the evaluation results and feedback content and sends them to the terminal.

[0688] Step 13:

[0689] The device displays feedback to the user.

[0690] The device displays the feedback received from the server on the screen and plays it back as audio if necessary.

[0691] Step 14:

[0692] The server stores the conversation history.

[0693] The server stores the user's conversation history and evaluation results in a database and organizes the data to be used in the next conversation session.

[0694] The above is a specific processing flow of the system for carrying out the invention.

[0695] Example 1

[0696] 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."

[0697] In recent years, with the progress of the aging society, the prevention and early detection of dementia have become important issues. Conventional dementia prevention systems are insufficient in providing individualized feedback to users, and in many cases, they require specialized operation and knowledge, placing a heavy burden on users. Therefore, there is a need for a system that allows users to easily monitor the prevention and progression of dementia.

[0698] 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.

[0699] In this invention, the server includes means for generating brain teasing questions based on the user's basic information using a generative AI model, means for analyzing the answers received by the server using natural language processing technology, and means for assessing the risk of developing dementia and the progression of the disease based on the analysis results. This enables the user to receive continuous and personalized assessment and feedback of cognitive function without requiring specialized knowledge or operation.

[0700] "User" refers to an individual who uses this system to prevent dementia or monitor its progression.

[0701] "Basic information" refers to information necessary to generate personalized brain teasers, such as the user's gender, age, and preferences.

[0702] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized brain teasers based on basic user information.

[0703] "Brain teasers" refer to quizzes or questions used to assess a user's cognitive function.

[0704] "Questions" refer to specific tasks or quizzes related to brain teasers generated by the generative AI model.

[0705] "Answer" refers to the response or reaction a user provides to a brain teaser.

[0706] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[0707] "Analysis results" refers to the evaluation of user responses analyzed using natural language processing technology.

[0708] "Risk of developing dementia" refers to a measure that assesses the likelihood that a user will develop dementia in the future.

[0709] "Progression" refers to an indicator that shows the progress of dementia and its precursor, mild cognitive impairment.

[0710] "Evaluation results" refers to a comprehensive evaluation and feedback of cognitive function based on the analysis results.

[0711] "Feedback" refers to advice or information provided to users based on the evaluation results.

[0712] MODE FOR CARRYING OUT THE INVENTION

[0713] The purpose of this invention is to enable users to easily prevent dementia and monitor its progression using a system that utilizes generative AI. The configuration and operation of the system are described in detail below.

[0714] System configuration

[0715] This system mainly consists of a server, a terminal, and a user.

[0716] The server integrates a database, generative AI model, and natural language processing technology. Here, MySQL is used for the database, GPT-3 for the generative AI model, and spaCy and BERT for the natural language processing technology.

[0717] A terminal is a device operated by a user, such as a smartphone, tablet, or PC. This terminal is equipped with a communication module for network communication with the server, and also has input devices (keyboard, touch panel) and output devices (display, speaker).

[0718] Users are ordinary individuals who use the system to assess their own cognitive function and receive feedback.

[0719] System operating principle

[0720] Acquisition and storage of basic user information

[0721] When a user first uses the system, they launch the application and enter basic information such as gender, age, and interests. For example, if the user is female, 65 years old, and enjoys crossword puzzles, the device formats this information and sends it in JSON format to the server. The server receives this information and stores it in a MySQL database.

[0722] Brain teaser generation

[0723] When the user taps the "Start Conversation" button, the server uses the pre-stored basic information to create a prompt sentence that generates a brain teaser using a generative AI model (GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input into the generative AI model. An example of the generated brain teaser would be a question such as "Tell me about the crossword puzzle you solved recently."

[0724] Posting questions and submitting answers

[0725] The server sends the generated question to the device, which displays it to the user, and the user answers by text input or voice input, which the device then sends back to the server in JSON format.

[0726] Analysis of responses and assessment of dementia risk

[0727] The server analyzes the received responses using natural language processing technology (spaCy or BERT). Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression. This assessment also references past conversation data to make a comprehensive judgment.

[0728] Providing and storing feedback

[0729] The server generates feedback based on the evaluation results and sends it to the device. The device displays this feedback to the user and can also notify them by voice. For example, it may include something like, "Your cognitive function is good. Let's continue next time." Finally, the server stores this conversation history and the evaluation results in a database.

[0730] Specific examples

[0731] 1. The user starts the application and enters the following basic information: "Gender: Female," "Age: 65," and "Hobbies: Crossword puzzles."

[0732] 2. The device formats this information and sends it to the server, which stores it in a database.

[0733] 3. The user taps the "Start Conversation" button to begin the session.

[0734] 4. Based on the user information, the server uses the generative AI model to input the prompt sentence, "Generate a brain teaser for a 65-year-old female user who likes crossword puzzles."

[0735] 5. The generative AI model generates a question such as "Tell me about the crossword puzzle you solved recently," and the server sends this to the device.

[0736] 6. The device displays it to the user and plays it aloud.

[0737] 7. A user answers, "The last crossword puzzle I solved was about the zoo."

[0738] 8. The device formats the response and sends it to the server.

[0739] 9. The server analyzes the answers, evaluates cognitive function, generates evaluation results, and sends them to the device.

[0740] 10. The device displays feedback to the user saying, "Your cognitive function is good. Let's continue next time."

[0741] In this way, this invention allows users to exercise their minds on a daily basis and easily evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[0742] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0743] Step 1:

[0744] A user starts the application and enters basic information (gender, age, and interests). For example, the user enters gender "female," age "65," and interest "crossword puzzles." This inputs data tailored to the individual user.

[0745] Input: Gender "Female", Age "65", Interest "Crossword puzzles"

[0746] Output: Screen display where basic information is entered

[0747] ---

[0748] Step 2:

[0749] The device formats this basic information and sends it to the server in JSON format. For example, it generates the following JSON data:

[0750] json

[0751] {

[0752] "gender": "female",

[0753] "age": 65,

[0754] "preferences": ["crossword puzzle"]

[0755] }

[0756] Input: Basic information entered by the user

[0757] Output: JSON format data is sent to the server

[0758] ---

[0759] Step 3:

[0760] The server stores the received basic information in a database, for example, a MySQL database.

[0761] Input: Basic information data in JSON format

[0762] Output: User information stored in the database

[0763] ---

[0764] Step 4:

[0765] The user taps the "Start Conversation" button to begin a conversation session. The app catches this event and proceeds to the next processing step.

[0766] Input: Tap the "Start Conversation" button

[0767] Output: Start trigger for a conversation session

[0768] ---

[0769] Step 5:

[0770] The server uses the pre-stored basic information to create a prompt sentence to generate a brain teaser using a generative AI model (e.g., GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input to the generative AI model.

[0771] Input: User basic information stored in the database

[0772] Output: The prompt sentence that is fed into the generative AI model

[0773] ---

[0774] Step 6:

[0775] A generative AI model generates brain teasers, such as questions like, "Tell me about the last crossword puzzle you solved."

[0776] Input: prompt statement

[0777] Output: Generated brain teaser (question)

[0778] ---

[0779] Step 7:

[0780] The server sends the generated question to the terminal, which then displays the question to the user. For example, the terminal displays the question "Tell me about the crossword puzzle you solved recently" on the screen.

[0781] Input: Generated brain teaser (question)

[0782] Output: The question that is displayed to the user

[0783] ---

[0784] Step 8:

[0785] The user answers by text or voice input. For example, the user might answer by text input, "The last crossword puzzle I solved was on the theme 'zoo.'"

[0786] Input: User's answer

[0787] Output: The input response data

[0788] ---

[0789] Step 9:

[0790] The device formats the user's answer and sends it to the server in JSON format, for example generating the following JSON data:

[0791] json

[0792] {

[0793] "answer": "The last crossword I solved was about a zoo."

[0794] }

[0795] Input: User's answer

[0796] Output: JSON formatted answer data is sent to the server

[0797] ---

[0798] Step 10:

[0799] The server analyzes the received answers using natural language processing techniques (e.g., spaCy and BERT), evaluating the accuracy of the answers, vocabulary diversity, and response speed.

[0800] Input: JSON formatted response data

[0801] Output: Analysis results

[0802] ---

[0803] Step 11:

[0804] The server then runs an algorithm based on the analysis results to assess the user's dementia risk and progression, taking into account past conversation data.

[0805] Input: Analysis results and past conversation data

[0806] Output: Assessment results of dementia risk and progression

[0807] ---

[0808] Step 12:

[0809] The server generates feedback based on the evaluation results and sends it to the device. For example, it generates feedback such as "Your cognitive function is good. Let's continue next time."

[0810] Input: Dementia risk and progression assessment results

[0811] Output: Feedback data

[0812] ---

[0813] Step 13:

[0814] The device will display feedback to the user, such as an on-screen message like "Your cognitive function is good. Let's continue next time." Audio notifications are also possible.

[0815] Input: Feedback data

[0816] Output: Feedback that is displayed to the user

[0817] ---

[0818] Step 14:

[0819] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[0820] Input: Conversation history and evaluation results

[0821] Output: History and evaluation results stored in a database

[0822] (Application example 1)

[0823] 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."

[0824] In modern society, the prevention and progression management of dementia have become important issues as the population ages. It is particularly difficult for elderly people who visit physical stores to easily check their cognitive function. There is also a need for fun, sustained mental exercises in daily life to maintain cognitive function. However, existing systems are unable to meet these needs, so it is necessary to develop a method that allows for easy checking and continuous monitoring of cognitive function.

[0825] 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.

[0826] In this invention, the server includes means for receiving and storing basic information of a user, means for generating and presenting brain teasers based on the user's basic information, means for receiving and analyzing the user's answers, means for evaluating the risk of developing dementia and the state of progression based on the analysis results, means for providing the evaluation results to the user, and means for the user to check their cognitive function when visiting a physical store. This enables elderly people visiting a physical store to easily check their cognitive function using their smartphone when they visit, making it possible to engage in enjoyable and sustainable dementia prevention efforts.

[0827] "Basic user information" refers to basic personal data such as gender, age, and preferences that are necessary for the system to function.

[0828] "Brain teasers" are questions or tasks presented to users with the purpose of activating or assessing cognitive function.

[0829] "Means for generating and presenting questions" refers to a method or function for generating optimal brain teasers based on the user's basic information and displaying them to the user.

[0830] The "means for receiving and analyzing user responses" refers to a method or function for receiving the content of a user's response and analyzing it to evaluate the content.

[0831] "Means for assessing the risk of developing dementia and the progression of dementia based on analysis results" refers to a method or function that uses the analysis results of a user's responses to determine and evaluate a person's risk of dementia and the progression of that person's dementia.

[0832] "Means for providing evaluation results to users" refers to the method or function for communicating the evaluation results to users.

[0833] "Means for checking cognitive function when a user visits a physical store" refers to a method or function for evaluating a user's cognitive function on the spot when the user visits a physical store.

[0834] This invention provides a system for easily assessing the cognitive function of elderly people visiting physical stores. The system generates brain-teasing questions based on the user's basic information, and assesses the user's risk of dementia based on their answers. Specific embodiments of the system are described below.

[0835] System configuration

[0836] The system consists of three elements: a server, a terminal, and a user.

[0837] Hardware and Software

[0838] The servers have high-performance computing power, store and analyze data, and use natural language processing (NLP) and generative AI models (e.g., GPT-3).

[0839] The devices used by users are smartphones or tablets, and the system uses Google Text-to-Speech (TTS) for voice input and Google Cloud Natural Language API for conversation analysis.

[0840] The users are elderly people who interact with the system using terminals when they visit a physical store.

[0841] User registration and basic information acquisition

[0842] When a user uses the system for the first time, they enter basic information (gender, age, preferences), which is sent from the terminal to the server and stored in the database.

[0843] Generating and presenting brain teasers

[0844] The server uses a generative AI model to generate brain teasing questions based on the stored basic information. The questions are dynamically changed according to the user's preferences. The generated questions are sent to the device and presented to the user.

[0845] Example prompt sentence:

[0846] "Tell me about the last crossword puzzle you solved."

[0847] User responses and analysis

[0848] Users answer the questions posed to them, and the answers are sent to the server via their device for analysis. The server uses natural language processing (NLP) technology to evaluate the accuracy of the answers, the diversity of vocabulary, and the speed of response.

[0849] Dementia risk assessment and feedback

[0850] Based on the analysis results, the server uses a generative AI model to assess dementia risk. The assessment results are sent to the device and presented to the user. Feedback includes recommendations for the next brain exercise.

[0851] Use in physical stores

[0852] Users visiting a physical store interact with the system using a terminal. For example, they can do mental exercises while waiting in a cafe or supermarket to check the state of their cognitive function. The system is intended for use in physical stores, and automatically starts the cognitive function check process when the user arrives at the store.

[0853] For example, an elderly person visiting a cafe can launch a "Shopping Support App" and answer questions about a crossword puzzle. Based on their answers, their cognitive function is evaluated and the results are fed back to them.

[0854] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0855] Step 1:

[0856] The user starts the application and enters basic information, such as gender, age, and preferences. This provides the user's basic information.

[0857] Step 2:

[0858] The device formats the basic information and sends it to the server. Specifically, it converts the user's input data into JSON format and sends it to the server using an HTTP request. At this stage, the server saves the basic information in a database.

[0859] Step 3:

[0860] A conversation session begins when the user taps the "Start conversation" button, which allows the user's actions to be obtained as input.

[0861] Step 4:

[0862] The server generates brain teasers appropriate for the user at that time based on the user's basic information. Specifically, it generates questions using a generative AI model. In this generation process, the server dynamically creates questions based on the user's preferences and past data.

[0863] Step 5:

[0864] The generated question is sent to the terminal and presented to the user. The terminal displays the question and, if necessary, outputs it aloud. The user confirms the question and answers it by voice or text.

[0865] Step 6:

[0866] The user's answer is sent from the device to the server. The device formats the user's answer and sends it to the server using an HTTP request, which causes the server to receive the user's answer as input.

[0867] Step 7:

[0868] The server analyzes the user's answers. Specifically, it uses natural language processing (NLP) technology to evaluate the accuracy of the answers, vocabulary diversity, response speed, etc. The analysis can be performed using the Google Cloud Natural Language API.

[0869] Step 8:

[0870] Based on the analysis results, the server performs a dementia risk assessment. A generative AI model is used to comprehensively evaluate the analysis results and past data. This assessment determines the risk of developing dementia and the progression of the condition.

[0871] Step 9:

[0872] The evaluation results and feedback are sent from the server to the device and presented to the user. The device displays the evaluation results and provides audio feedback, including recommendations for the next brain exercise.

[0873] Step 10:

[0874] The server saves the conversation history and evaluation results in a database, which enables continuous monitoring in subsequent sessions. This saving process includes inserting data into the database.

[0875] The above processing steps allow users to easily check their cognitive function in a physical store, enabling them to continuously work on preventing dementia.

[0876] 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.

[0877] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. The following explains the system's program processing in detail, including specific examples.

[0878] First-time user registration

[0879] The user enters basic information

[0880] Users launch the application on their smartphones, PCs, or other devices and enter basic information such as gender, age, and interests. This information is used to provide users with brain exercises tailored to their needs.

[0881] The device sends the information to the server

[0882] The device formats the user's basic information and sends it to the server, which then stores it in a database.

[0883] Start a conversation

[0884] A user starts a conversation

[0885] When the user taps the "Start Conversation" button, the device sends a conversation start request to the server.

[0886] The server selects the brain teaser.

[0887] The server runs an algorithm to generate the most suitable brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 and likes crossword puzzles, the server will select a word-based quiz.

[0888] The server sends a question to the device

[0889] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[0890] User responses and emotion recognition

[0891] The user answers the question

[0892] The user answers the questions presented to them, and can do so by text input or voice input.

[0893] The device sends the answer to the server

[0894] The terminal formats the user's response data and sends it to the server.

[0895] The server recognizes emotions using an emotion engine

[0896] When the server analyzes the received answer data, it uses an emotion engine to recognize the user's emotional state. For example, if the answer sounds nervous, it recognizes the emotion "nervous."

[0897] Running the diagnostic algorithm

[0898] The server analyzes the responses and emotional data

[0899] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of the answers, reaction time, emotional state, etc.

[0900] The server assesses the risk of dementia

[0901] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past conversation data and emotional data.

[0902] Providing feedback

[0903] The server generates and sends the evaluation results

[0904] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[0905] The device displays feedback to the user

[0906] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[0907] Save data and prepare for next time

[0908] The server stores conversation history and emotion data

[0909] The server stores the user's conversation history, evaluation results, and emotion data in a database, which allows for continuous monitoring in the next conversation session.

[0910] Specific examples

[0911] First-time registration and conversation flow

[0912] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[0913] The device formats this information and sends it to the server, which stores it in a database.

[0914] The user taps the "Start Conversation" button and the conversation session begins.

[0915] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the terminal.

[0916] The terminal displays the question to the user and plays it aloud.

[0917] A user answers, "The last crossword puzzle I solved was on the theme 'zoo'."

[0918] The device formats the response and sends it to the server.

[0919] The server analyzes the answers and uses an emotion engine to recognize the user's emotions (e.g., "fun").

[0920] The server evaluates cognitive function, generates feedback based on the evaluation results, and sends it to the device.

[0921] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[0922] The server stores the conversation history and emotion data in a database in preparation for the next conversation session.

[0923] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[0924] The processing flow will be explained below.

[0925] Step 1:

[0926] The user launches the app.

[0927] When a user launches the application on a device such as a smartphone or PC, an input form for basic information (gender, age, interests, etc.) appears on the screen.

[0928] Step 2:

[0929] The user enters basic information.

[0930] The user enters information such as gender, age, and preferences, and then taps the "Save" button to complete the entry.

[0931] Step 3:

[0932] The terminal sends the input data to the server.

[0933] The terminal formats the input data and generates a request to be sent to the server, and the request is sent.

[0934] Step 4:

[0935] The server stores user information.

[0936] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[0937] Step 5:

[0938] A user initiates a conversation.

[0939] The user taps the "Start conversation" button, and the device sends a conversation start request to the server.

[0940] Step 6:

[0941] The server selects a brain teaser.

[0942] The server runs an algorithm based on the user's basic information and past conversation data to generate the most appropriate brain teaser. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[0943] Step 7:

[0944] The server sends a question to the terminal.

[0945] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[0946] Step 8:

[0947] The user answers the question.

[0948] The user answers the questions posed, and the answers can be entered by text or voice.

[0949] Step 9:

[0950] The device sends the response to the server.

[0951] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[0952] Step 10:

[0953] The server recognizes emotions using an emotion engine.

[0954] The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state, such as "tension" or "joy" based on the tone of voice and the content of the text.

[0955] Step 11:

[0956] The server analyzes the responses and sentiment data.

[0957] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of responses, reaction time, perceived emotional state, and more.

[0958] Step 12:

[0959] The server assesses the risk of dementia.

[0960] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, taking into account past conversation data and emotion data to make a comprehensive assessment.

[0961] Step 13:

[0962] The server generates and transmits the evaluation results.

[0963] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[0964] Step 14:

[0965] The device displays feedback to the user.

[0966] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[0967] Step 15:

[0968] The server stores conversation history and emotion data.

[0969] The server stores the user's conversation history, evaluation results, and emotion data in a database, which organizes the data for use in the next conversation session.

[0970] The above is a specific processing flow of the system of the present invention that combines an emotion engine. This allows users to exercise their minds on a daily basis, objectively and specifically evaluate the state of their cognitive function, and receive more personalized feedback and improvement measures.

[0971] Example 2

[0972] 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."

[0973] Conventional dementia prevention and monitoring tools assess users' cognitive function primarily based on data entry accuracy and reaction time, without taking into account their emotional state. This makes it difficult to understand the user's condition in more detail or provide personalized feedback. This makes it difficult to detect the risk and progression of dementia early and develop appropriate countermeasures.

[0974] 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.

[0975] In this invention, the server includes means for receiving and storing basic information of the user, means for generating and presenting brain teasing questions based on the basic information of the user, and means for recognizing the emotional state of the user from the response data, thereby enabling the provision of personalized feedback that takes the emotional state into consideration.

[0976] "Basic user information" refers to basic attribute information about a user, such as the user's gender, age, and preferences.

[0977] "Means for storing" refers to means for storing received information in a storage system such as a database.

[0978] "Brain teasers" are tasks such as quizzes and questions designed to stimulate and assess the user's cognitive function.

[0979] A "means for generating questions" is an algorithm or system that generates optimal brain-teasing questions based on the user's basic information and past data.

[0980] The "presentation means" is a means for presenting the generated question to the user visually or audibly.

[0981] A "means for receiving a response" is a means for receiving the text or voice input by the user and formatting it into an analyzable format.

[0982] The "means of analysis" refers to the means of analyzing the received response data using natural language processing or an emotion recognition engine.

[0983] A "means for assessing the risk of developing dementia and the progression of the condition" is an algorithm or system that assesses a user's risk of dementia and its progression based on the results of the analysis.

[0984] "Means for providing evaluation results" refers to means for notifying users of the evaluation results visually or audibly.

[0985] "Means for recognizing emotional states" refers to means for identifying emotions from user response data using natural language processing and voice analysis.

[0986] The "means for comprehensively adjusting the evaluation results" is an algorithm or system that performs a comprehensive evaluation including the recognized emotional state and adjusts the final result.

[0987] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. Specific embodiments of the system are described below.

[0988] The system of the present invention is used by users via devices such as smartphones or PCs. The user launches the application and enters basic information such as gender, age, and preferences. The device formats this information and sends it to the server. The server stores the received information in a database and uses it for subsequent processing.

[0989] In this system, when the user starts a conversation, the server generates optimal brain teasing questions based on the user's basic information and past conversation data. The generated questions are sent to the device and presented to the user. The user answers the questions by text input or voice input.

[0990] The device formats the user's response data and sends it to the server. The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state. Based on the data obtained from this analysis, the server evaluates the user's cognitive function. This makes it possible to assess the risk of dementia and monitor its progression.

[0991] The evaluation results are sent from the server to the device, where they are displayed to the user. A voice notification is also possible. The server then stores the user's conversation history, evaluation results, and recognized emotion data in a database for future use in the next session.

[0992] Specific examples

[0993] For example, a 65-year-old woman who enjoys crossword puzzles launches the app and enters her gender, age, and interests. The device formats this information and sends it to the server. After the server stores the information in a database, the user can tap the "Start Conversation" button to begin a conversation.

[0994] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device. The device displays the question to the user and plays it back aloud. When the user answers, "The crossword puzzle I solved recently was on the theme of 'zoo,'" the device formats the answer and sends it to the server.

[0995] The server analyzes the response data and uses an emotion engine to recognize the user's emotion. For example, it recognizes "enjoyment." The server then evaluates cognitive function based on the analysis results, generates feedback based on the evaluation results, and sends it to the device. The device then displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[0996] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[0997] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] The user enters basic information

[1000] Input: The user enters basic information such as gender, age, and preferences into an application on a smartphone or PC.

[1001] What it does: The user fills out a form on the app's start screen, filling out their information, such as being a 65-year-old woman who likes crossword puzzles.

[1002] Output: The input basic information is prepared as data for further processing.

[1003] Step 2:

[1004] The device sends the information to the server

[1005] Input: Basic information entered by the user.

[1006] What happens: The device takes the input information, formats it into JSON, and sends the formatted data to the server over HTTPS.

[1007] Output: Formatted basic information data received by the server.

[1008] Step 3:

[1009] The server stores basic information in a database

[1010] Input: Basic information data sent from the device.

[1011] Specific operation: The server stores the received basic information in a database, for example, a NoSQL database.

[1012] Output: User basic information stored in the database.

[1013] Step 4:

[1014] A user starts a conversation

[1015] Input: The action a user takes to start a conversation, such as tapping a button.

[1016] Specific operation: The user taps the "Start conversation" button on the app screen, which causes the device to send a conversation start request to the server.

[1017] Output: A conversation initiation request sent to the server.

[1018] Step 5:

[1019] The server chooses the best brain teaser

[1020] Input: User basic information and past conversation data.

[1021] How it works: The server uses a generative AI model to select the most suitable brain exercise for a user based on their basic information and past conversation data. For example, if a 65-year-old woman who likes crossword puzzles is selected, the server will select a crossword-related quiz.

[1022] Output: Optimal brain teaser question data.

[1023] Step 6:

[1024] The server sends a question to the device

[1025] Input: Selected brain teaser question data.

[1026] Specific operation: The server sends the generated question data to the terminal, which then displays the received question to the user and plays it back aloud.

[1027] Output: The question that is presented to the user.

[1028] Step 7:

[1029] The user answers the question

[1030] Input: The question posed by the server.

[1031] What happens: The user answers the question by typing or speaking, for example, "The last crossword puzzle I solved was about the zoo."

[1032] Output: User response data.

[1033] Step 8:

[1034] The device sends the answer to the server

[1035] Input: User response data.

[1036] Specific operation: The device receives the user's response data, formats it into JSON format again, and sends this formatted data to the server using HTTPS.

[1037] Output: The formatted response data received by the server.

[1038] Step 9:

[1039] The server recognizes emotions using an emotion engine

[1040] Input: Response data sent from the device.

[1041] Specific operation: The server analyzes the received response data and uses the emotion engine to recognize the user's emotional state, for example, identifying emotions such as "joy" or "tension."

[1042] Output: Recognized emotion data.

[1043] Step 10:

[1044] The server analyzes the responses and emotional data

[1045] Input: User response data and sentiment data.

[1046] How it works: The server uses natural language processing technology to analyze the content of the answers, and simultaneously processes the response time and emotional state, thereby evaluating the accuracy of the answers, response time, and emotional state.

[1047] Output: User's cognitive function assessment data.

[1048] Step 11:

[1049] The server assesses the risk of dementia

[1050] Input: User's cognitive assessment data.

[1051] Specific operation: Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past data as well.

[1052] Output: Dementia risk assessment results.

[1053] Step 12:

[1054] The server generates and sends the evaluation results

[1055] Input: Dementia risk assessment results.

[1056] Specific operation: The server generates feedback on the cognitive function assessment results and recommended future brain exercises and sends them to the device.

[1057] Output: Feedback data sent to the device.

[1058] Step 13:

[1059] The device displays feedback to the user

[1060] Input: Feedback data sent by the server.

[1061] Specific operation: The device receives the feedback data and notifies the user by text or voice, so that the user receives the feedback.

[1062] Output: The feedback that is presented to the user.

[1063] Step 14:

[1064] The server stores conversation history and emotion data

[1065] Input: User conversation history, rating results, and emotion data.

[1066] What happens: The server stores this data in a database and keeps it available for the next session.

[1067] Output: User history and emotion data stored in a database.

[1068] In this way, the system of the present invention comprehensively analyzes the user's basic information, response data, and emotional state, and provides a detailed assessment of cognitive function and personalized feedback, thereby enabling the prevention of dementia risk and early detection.

[1069] (Application example 2)

[1070] 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."

[1071] Conventional dementia prevention systems simply provide mental exercises without considering the user's emotional state. This results in a lack of feedback that reflects the user's emotional changes, reducing the accuracy of long-term dementia prevention and progress monitoring. It is necessary to provide dementia prevention training for the elderly as a digital assistant in brick-and-mortar stores, aiming to provide detailed support tailored to each individual user.

[1072] The identification process by the identification 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 receiving and storing basic information about the user, means for generating and presenting brain teasing questions based on the user's basic information, means for receiving and analyzing the user's answers, means for analyzing the user's emotions using an emotion recognition engine, and means for generating feedback according to data acquired based on the user's emotional state. This makes it possible to provide dementia prevention measures according to the individual emotional state of the user.

[1073] A "user" is someone who uses the system to exercise their mind to prevent dementia.

[1074] "Basic information" refers to personal data such as gender, age, and preferences provided by the user.

[1075] "Means for storing" refers to functions or devices that allow users' basic information to be saved and reused.

[1076] "Generating means" refers to algorithms or devices that generate appropriate brain teasers or questions based on basic information about the user.

[1077] "Presentation means" refers to the function or device for displaying the generated brain teasers or questions to the user.

[1078] "Means for analysis" refers to the algorithms or devices used to evaluate user responses and analyze their content and accuracy.

[1079] An "emotion recognition engine" is an artificial intelligence technology or software that analyzes a user's voice or text and recognizes their emotional state.

[1080] "Means for generating feedback" refers to algorithms or devices that provide appropriate responses or advice to users based on analysis results and emotional data.

[1081] "Emotional state" refers to a state that indicates a user's changes in feelings or mood.

[1082] "Dementia risk assessment" refers to algorithms and devices used to assess a user's risk of developing dementia and the progression of the condition.

[1083] "Means for providing assessment results" refers to functions and devices for notifying users of the assessed dementia risk and progression.

[1084]

[1085] This invention builds a system that acts as a digital assistant in brick-and-mortar stores, providing brain exercise sessions aimed at preventing dementia, particularly for the elderly. The system operates as a smartphone application and provides individualized feedback based on the user's basic information and emotional recognition, enabling more effective dementia prevention and progression monitoring.

[1086] System configuration

[1087] The system consists of the following components:

[1088] User device: Smartphone or tablet used by the user

[1089] Server: A remote server that processes and analyzes data

[1090] Database: A database that stores basic information about users, past conversation data, and emotional data.

[1091] What the program does

[1092] When a user uses the app for the first time, they enter basic information such as gender, age, and interests on their device. The device sends this information to the server, which then stores it in a database.

[1093] When you tap the "Start Conversation" button in the application, your device sends a conversation start request to the server, which then generates the optimal brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 years old and enjoys crossword puzzles, the server will generate a question such as "Tell me about the crossword puzzle you solved recently."

[1094] The generated question is sent to the device and presented to the user. When the user answers, the answer data is sent to the server as text or voice. The server uses natural language processing technology and an emotion engine to analyze the answer and recognize the user's emotional state. For example, if the answer sounds fun, the emotion of "fun" is recognized.

[1095] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data, and generates feedback based on the results. The feedback is sent to the device and displayed to the user. For example, a positive message such as "Your cognitive function is good. Let's continue next time" is provided.

[1096] Technology used

[1097] Emotion Recognition Engine: Uses generative AI models and natural language processing techniques to recognize emotional states. Examples include the spacy and transformers libraries.

[1098] Database: A database management system for storing user basic information and past conversation data. For example, an SQL or NoSQL database.

[1099] Server technology: Back-end servers for data processing and analysis. Examples include Python-based servers and cloud services.

[1100] Specific examples

[1101] Example 1:

[1102] The user launches the app and enters their gender (female), age (70), and preference (puzzles). The app begins asking questions such as, "Tell me about the puzzle you solved recently." The user answers, "I solved a number puzzle recently," and the answer is sent to the server. The emotion engine recognizes "fun," and provides feedback such as, "Cognitive function is good. Please continue to enjoy the app."

[1103] Example 2:

[1104] prompt:

[1105] User ID = "id1"

[1106] Question = Start conversation session (user ID)

[1107] Brain teaser presentation (user ID, question)

[1108] Answer = "The puzzle I solved recently was about flowers."

[1109] Emotion result = Answer and Emotion Recognition(User ID, Answer)

[1110] Evaluation result = Cognitive function evaluation (user ID)

[1111] print(f"Feedback: {Evaluation result}")

[1112]

[1113] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1114]

[1115] Step 1:

[1116] The user starts the application on the device and enters basic information (gender, age, interests).

[1117] Input: The user enters basic information such as gender, age, and preferences.

[1118] Data processing: The terminal formats the basic information entered and sends it to the server.

[1119] Output: Basic formatted information is sent to the server.

[1120] Step 2:

[1121] The server stores the basic information in a database.

[1122] Input: Basic information sent from the device.

[1123] Data processing: The server records the basic information received in a database.

[1124] Output: Basic information stored in the database.

[1125] Step 3:

[1126] The user taps the Start Conversation button.

[1127] Input: The user taps the "Start Conversation" button on their device.

[1128] Data processing: The terminal sends a conversation start request to the server.

[1129] Output: A conversation initiation request is sent to the server.

[1130] Step 4:

[1131] The server generates optimal brain exercises based on the user's basic information and past conversation data.

[1132] Input: Basic information about the user and past conversation data.

[1133] Data processing: The server uses the generated AI model to generate the optimal brain teaser for the user.

[1134] Output: The generated brain teaser.

[1135] Step 5:

[1136] The server sends the generated brain teasers to the terminal, which then presents them to the user.

[1137] Input: A server-generated brain teaser.

[1138] Data processing: The device formats the brain teasers received from the server for display.

[1139] Output: The brain teaser displayed on the terminal.

[1140] Step 6:

[1141] The user answers the questions presented.

[1142] Input: The user inputs the answer into the terminal by voice or text.

[1143] Data processing: The terminal formats the entered answers and sends them to the server.

[1144] Output: The formatted response data is sent to the server.

[1145] Step 7:

[1146] The server analyzes the response data and recognizes the user's emotions using an emotion recognition engine.

[1147] Input: Response data sent from the device.

[1148] Data processing: The server analyzes the responses using natural language processing technology and an emotion recognition engine to recognize the emotional state.

[1149] Output: Analysis results and emotion data.

[1150] Step 8:

[1151] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data.

[1152] Input: Analysis results and emotion data.

[1153] Data processing: The server evaluates the dementia risk using an evaluation algorithm.

[1154] Output: Dementia risk assessment results.

[1155] Step 9:

[1156] The server generates feedback based on the evaluation results and sends it to the device.

[1157] Input: Dementia risk assessment results.

[1158] Data processing: The server generates feedback based on the evaluation results.

[1159] Output: The generated feedback is sent to the device.

[1160] Step 10:

[1161] The device displays the feedback to the user.

[1162] Input: Feedback sent by the server.

[1163] Data processing: The device formats the feedback for display.

[1164] Output: Feedback displayed on the device.

[1165] 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.

[1166] 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.

[1167] 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.

[1168] [Third embodiment]

[1169] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1170] 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.

[1171] 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).

[1172] 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.

[1173] 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.

[1174] 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).

[1175] 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.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] 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.

[1180] 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."

[1181] This invention utilizes an AI voice chatbot that utilizes generative AI to provide a system that allows users to easily monitor the prevention and progression of dementia. Below, the processing of the system's program is explained in natural language, with specific examples included. The subjects used are server, terminal, and user.

[1182] First-time user registration

[1183] The user enters basic information

[1184] A user launches the application and enters basic information such as gender, age, and interests, which is important for generating brain teasers tailored to the user's needs.

[1185] The device sends the information to the server

[1186] The device formats the basic information entered and sends it to the server, which stores it in a database.

[1187] Start a conversation

[1188] A user starts a conversation

[1189] A conversation session begins when the user taps the "Start Conversation" button.

[1190] The server selects the brain teaser.

[1191] The server runs an algorithm that uses basic information about the user to generate a brain teaser that's appropriate for that moment. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[1192] The server sends a question to the device

[1193] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[1194] User Answers

[1195] The user answers the question

[1196] The user answers the presented brain teasers by either text input or voice input.

[1197] The device sends the answer to the server

[1198] The device formats the user's response and sends it to the server.

[1199] Running the diagnostic algorithm

[1200] The server parses the answer

[1201] The server then analyzes the responses it receives using natural language processing (NLP) techniques, which evaluate accuracy, vocabulary diversity, response speed, and other factors.

[1202] The server assesses the risk of dementia

[1203] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, and makes a comprehensive judgment by also referencing past conversation data.

[1204] Providing feedback

[1205] The server generates and sends the evaluation results

[1206] The server generates feedback based on the evaluation results and sends it to the device, including the evaluation results and recommendations for the next brain exercise.

[1207] The device displays feedback to the user

[1208] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[1209] Save data and prepare for next time

[1210] The server stores the conversation history

[1211] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[1212] Specific examples

[1213] First-time registration and conversation flow

[1214] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[1215] The device formats this information and sends it to the server, which stores it in a database.

[1216] The user taps the "Start Conversation" button to begin the session.

[1217] The server generates a question based on the user's information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device.

[1218] The device displays the question to the user and plays it aloud.

[1219] The user answered, "The last crossword I solved was on the theme of 'zoo'."

[1220] The device formats the response and sends it to the server.

[1221] The server analyzes the responses, evaluates cognitive function, generates evaluation results, and sends them to the device.

[1222] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[1223] In this way, this invention allows users to easily perform daily mental exercises and evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[1224] The processing flow will be explained below.

[1225] Step 1:

[1226] The user launches the app.

[1227] A user launches the application on a device such as a smartphone or PC. An input form for basic information (gender, age, interests, etc.) is displayed on the screen.

[1228] Step 2:

[1229] The user enters basic information.

[1230] The user enters information such as gender, age, and preferences, and then completes the entry by tapping the "Save" button.

[1231] Step 3:

[1232] The terminal sends the input data to the server.

[1233] The terminal formats the input data and generates a request to send to the server. The request is sent to the server.

[1234] Step 4:

[1235] The server stores user information.

[1236] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[1237] Step 5:

[1238] A user initiates a conversation.

[1239] When the user taps the "Start conversation" button, the device sends a conversation start request to the server.

[1240] Step 6:

[1241] The server selects a brain teaser.

[1242] The server runs an algorithm to generate appropriate brain teasers based on the user's basic information, and sends the selected content to the device.

[1243] Step 7:

[1244] The terminal presents a question to the user.

[1245] The device displays the question received from the server on the screen and plays it back aloud if necessary.

[1246] Step 8:

[1247] The user answers the question.

[1248] The user answers the questions presented to them by text input or voice input.

[1249] Step 9:

[1250] The device sends the response to the server.

[1251] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[1252] Step 10:

[1253] The server parses the response.

[1254] The server analyzes the responses it receives using natural language processing (NLP) technology and evaluates the accuracy of the responses and response time.

[1255] Step 11:

[1256] The server assesses the risk of dementia.

[1257] The server then runs an algorithm based on the analysis results to assess the risk and progression of dementia, taking into account past conversation data as well.

[1258] Step 12:

[1259] The server generates and transmits the evaluation results.

[1260] The server generates the evaluation results and feedback content and sends them to the terminal.

[1261] Step 13:

[1262] The device displays feedback to the user.

[1263] The device displays the feedback received from the server on the screen and plays it back as audio if necessary.

[1264] Step 14:

[1265] The server stores the conversation history.

[1266] The server stores the user's conversation history and evaluation results in a database and organizes the data to be used in the next conversation session.

[1267] The above is a specific processing flow of the system for carrying out the invention.

[1268] Example 1

[1269] 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."

[1270] In recent years, with the progress of the aging society, the prevention and early detection of dementia have become important issues. Conventional dementia prevention systems are insufficient in providing individualized feedback to users, and in many cases, they require specialized operation and knowledge, placing a heavy burden on users. Therefore, there is a need for a system that allows users to easily monitor the prevention and progression of dementia.

[1271] 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.

[1272] In this invention, the server includes means for generating brain teasing questions based on the user's basic information using a generative AI model, means for analyzing the answers received by the server using natural language processing technology, and means for assessing the risk of developing dementia and the progression of the disease based on the analysis results. This enables the user to receive continuous and personalized assessment and feedback of cognitive function without requiring specialized knowledge or operation.

[1273] "User" refers to an individual who uses this system to prevent dementia or monitor its progression.

[1274] "Basic information" refers to information necessary to generate personalized brain teasers, such as the user's gender, age, and preferences.

[1275] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized brain teasers based on basic user information.

[1276] "Brain teasers" refer to quizzes or questions used to assess a user's cognitive function.

[1277] "Questions" refer to specific tasks or quizzes related to brain teasers generated by the generative AI model.

[1278] "Answer" refers to the response or reaction a user provides to a brain teaser.

[1279] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[1280] "Analysis results" refers to the evaluation of user responses analyzed using natural language processing technology.

[1281] "Risk of developing dementia" refers to a measure that assesses the likelihood that a user will develop dementia in the future.

[1282] "Progression" refers to an indicator that shows the progress of dementia and its precursor, mild cognitive impairment.

[1283] "Evaluation results" refers to a comprehensive evaluation and feedback of cognitive function based on the analysis results.

[1284] "Feedback" refers to advice or information provided to users based on the evaluation results.

[1285] MODE FOR CARRYING OUT THE INVENTION

[1286] The purpose of this invention is to enable users to easily prevent dementia and monitor its progression using a system that utilizes generative AI. The configuration and operation of the system are described in detail below.

[1287] System configuration

[1288] This system mainly consists of a server, a terminal, and a user.

[1289] The server integrates a database, generative AI model, and natural language processing technology. Here, MySQL is used for the database, GPT-3 for the generative AI model, and spaCy and BERT for the natural language processing technology.

[1290] A terminal is a device operated by a user, such as a smartphone, tablet, or PC. This terminal is equipped with a communication module for network communication with the server, and also has input devices (keyboard, touch panel) and output devices (display, speaker).

[1291] Users are ordinary individuals who use the system to assess their own cognitive function and receive feedback.

[1292] System operating principle

[1293] Acquisition and storage of basic user information

[1294] When a user first uses the system, they launch the application and enter basic information such as gender, age, and interests. For example, if the user is female, 65 years old, and enjoys crossword puzzles, the device formats this information and sends it in JSON format to the server. The server receives this information and stores it in a MySQL database.

[1295] Brain teaser generation

[1296] When the user taps the "Start Conversation" button, the server uses the pre-stored basic information to create a prompt sentence that generates a brain teaser using a generative AI model (GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input into the generative AI model. An example of the generated brain teaser would be a question such as "Tell me about the crossword puzzle you solved recently."

[1297] Posting questions and submitting answers

[1298] The server sends the generated question to the device, which displays it to the user, and the user answers by text input or voice input, which the device then sends back to the server in JSON format.

[1299] Analysis of responses and assessment of dementia risk

[1300] The server analyzes the received responses using natural language processing technology (spaCy or BERT). Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression. This assessment also references past conversation data to make a comprehensive judgment.

[1301] Providing and storing feedback

[1302] The server generates feedback based on the evaluation results and sends it to the device. The device displays this feedback to the user and can also notify them by voice. For example, it may include something like, "Your cognitive function is good. Let's continue next time." Finally, the server stores this conversation history and the evaluation results in a database.

[1303] Specific examples

[1304] 1. The user starts the application and enters the following basic information: "Gender: Female," "Age: 65," and "Hobbies: Crossword puzzles."

[1305] 2. The device formats this information and sends it to the server, which stores it in a database.

[1306] 3. The user taps the "Start Conversation" button to begin the session.

[1307] 4. Based on the user information, the server uses the generative AI model to input the prompt sentence, "Generate a brain teaser for a 65-year-old female user who likes crossword puzzles."

[1308] 5. The generative AI model generates a question such as "Tell me about the crossword puzzle you solved recently," and the server sends this to the device.

[1309] 6. The device displays it to the user and plays it aloud.

[1310] 7. A user answers, "The last crossword puzzle I solved was about the zoo."

[1311] 8. The device formats the response and sends it to the server.

[1312] 9. The server analyzes the answers, evaluates cognitive function, generates evaluation results, and sends them to the device.

[1313] 10. The device displays feedback to the user saying, "Your cognitive function is good. Let's continue next time."

[1314] In this way, this invention allows users to exercise their minds on a daily basis and easily evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[1315] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1316] Step 1:

[1317] A user starts the application and enters basic information (gender, age, and interests). For example, the user enters gender "female," age "65," and interest "crossword puzzles." This inputs data tailored to the individual user.

[1318] Input: Gender "Female", Age "65", Interest "Crossword puzzles"

[1319] Output: Screen display where basic information is entered

[1320] ---

[1321] Step 2:

[1322] The device formats this basic information and sends it to the server in JSON format. For example, it generates the following JSON data:

[1323] json

[1324] {

[1325] "gender": "female",

[1326] "age": 65,

[1327] "preferences": ["crossword puzzle"]

[1328] }

[1329] Input: Basic information entered by the user

[1330] Output: JSON format data is sent to the server

[1331] ---

[1332] Step 3:

[1333] The server stores the received basic information in a database, for example, a MySQL database.

[1334] Input: Basic information data in JSON format

[1335] Output: User information stored in the database

[1336] ---

[1337] Step 4:

[1338] The user taps the "Start Conversation" button to begin a conversation session. The app catches this event and proceeds to the next processing step.

[1339] Input: Tap the "Start Conversation" button

[1340] Output: Start trigger for a conversation session

[1341] ---

[1342] Step 5:

[1343] The server uses the pre-stored basic information to create a prompt sentence to generate a brain teaser using a generative AI model (e.g., GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input to the generative AI model.

[1344] Input: User basic information stored in the database

[1345] Output: The prompt sentence that is fed into the generative AI model

[1346] ---

[1347] Step 6:

[1348] A generative AI model generates brain teasers, such as questions like, "Tell me about the last crossword puzzle you solved."

[1349] Input: prompt statement

[1350] Output: Generated brain teaser (question)

[1351] ---

[1352] Step 7:

[1353] The server sends the generated question to the terminal, which then displays the question to the user. For example, the terminal displays the question "Tell me about the crossword puzzle you solved recently" on the screen.

[1354] Input: Generated brain teaser (question)

[1355] Output: The question that is displayed to the user

[1356] ---

[1357] Step 8:

[1358] The user answers by text or voice input. For example, the user might answer by text input, "The last crossword puzzle I solved was on the theme 'zoo.'"

[1359] Input: User's answer

[1360] Output: The input response data

[1361] ---

[1362] Step 9:

[1363] The device formats the user's answer and sends it to the server in JSON format, for example generating the following JSON data:

[1364] json

[1365] {

[1366] "answer": "The last crossword I solved was about a zoo."

[1367] }

[1368] Input: User's answer

[1369] Output: JSON formatted answer data is sent to the server

[1370] ---

[1371] Step 10:

[1372] The server analyzes the received answers using natural language processing techniques (e.g., spaCy and BERT), evaluating the accuracy of the answers, vocabulary diversity, and response speed.

[1373] Input: JSON formatted response data

[1374] Output: Analysis results

[1375] ---

[1376] Step 11:

[1377] The server then runs an algorithm based on the analysis results to assess the user's dementia risk and progression, taking into account past conversation data.

[1378] Input: Analysis results and past conversation data

[1379] Output: Assessment results of dementia risk and progression

[1380] ---

[1381] Step 12:

[1382] The server generates feedback based on the evaluation results and sends it to the device. For example, it generates feedback such as "Your cognitive function is good. Let's continue next time."

[1383] Input: Dementia risk and progression assessment results

[1384] Output: Feedback data

[1385] ---

[1386] Step 13:

[1387] The device will display feedback to the user, such as an on-screen message like "Your cognitive function is good. Let's continue next time." Audio notifications are also possible.

[1388] Input: Feedback data

[1389] Output: Feedback that is displayed to the user

[1390] ---

[1391] Step 14:

[1392] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[1393] Input: Conversation history and evaluation results

[1394] Output: History and evaluation results stored in a database

[1395] (Application example 1)

[1396] 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."

[1397] In modern society, the prevention and progression management of dementia have become important issues as the population ages. It is particularly difficult for elderly people who visit physical stores to easily check their cognitive function. There is also a need for fun, sustained mental exercises in daily life to maintain cognitive function. However, existing systems are unable to meet these needs, so it is necessary to develop a method that allows for easy checking and continuous monitoring of cognitive function.

[1398] 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.

[1399] In this invention, the server includes means for receiving and storing basic information of a user, means for generating and presenting brain teasers based on the user's basic information, means for receiving and analyzing the user's answers, means for evaluating the risk of developing dementia and the state of progression based on the analysis results, means for providing the evaluation results to the user, and means for the user to check their cognitive function when visiting a physical store. This enables elderly people visiting a physical store to easily check their cognitive function using their smartphone when they visit, making it possible to engage in enjoyable and sustainable dementia prevention efforts.

[1400] "Basic user information" refers to basic personal data such as gender, age, and preferences that are necessary for the system to function.

[1401] "Brain teasers" are questions or tasks presented to users with the purpose of activating or assessing cognitive function.

[1402] "Means for generating and presenting questions" refers to a method or function for generating optimal brain teasers based on the user's basic information and displaying them to the user.

[1403] The "means for receiving and analyzing user responses" refers to a method or function for receiving the content of a user's response and analyzing it to evaluate the content.

[1404] "Means for assessing the risk of developing dementia and the progression of dementia based on analysis results" refers to a method or function that uses the analysis results of a user's responses to determine and evaluate a person's risk of dementia and the progression of that person's dementia.

[1405] "Means for providing evaluation results to users" refers to the method or function for communicating the evaluation results to users.

[1406] "Means for checking cognitive function when a user visits a physical store" refers to a method or function for evaluating a user's cognitive function on the spot when the user visits a physical store.

[1407] This invention provides a system for easily assessing the cognitive function of elderly people visiting physical stores. The system generates brain-teasing questions based on the user's basic information, and assesses the user's risk of dementia based on their answers. Specific embodiments of the system are described below.

[1408] System configuration

[1409] The system consists of three elements: a server, a terminal, and a user.

[1410] Hardware and Software

[1411] The servers have high-performance computing power, store and analyze data, and use natural language processing (NLP) and generative AI models (e.g., GPT-3).

[1412] The devices used by users are smartphones or tablets, and the system uses Google Text-to-Speech (TTS) for voice input and Google Cloud Natural Language API for conversation analysis.

[1413] The users are elderly people who interact with the system using terminals when they visit a physical store.

[1414] User registration and basic information acquisition

[1415] When a user uses the system for the first time, they enter basic information (gender, age, preferences), which is sent from the terminal to the server and stored in the database.

[1416] Generating and presenting brain teasers

[1417] The server uses a generative AI model to generate brain teasing questions based on the stored basic information. The questions are dynamically changed according to the user's preferences. The generated questions are sent to the device and presented to the user.

[1418] Example prompt sentence:

[1419] "Tell me about the last crossword puzzle you solved."

[1420] User responses and analysis

[1421] Users answer the questions posed to them, and the answers are sent to the server via their device for analysis. The server uses natural language processing (NLP) technology to evaluate the accuracy of the answers, the diversity of vocabulary, and the speed of response.

[1422] Dementia risk assessment and feedback

[1423] Based on the analysis results, the server uses a generative AI model to assess dementia risk. The assessment results are sent to the device and presented to the user. Feedback includes recommendations for the next brain exercise.

[1424] Use in physical stores

[1425] Users visiting a physical store interact with the system using a terminal. For example, they can do mental exercises while waiting in a cafe or supermarket to check the state of their cognitive function. The system is intended for use in physical stores, and automatically starts the cognitive function check process when the user arrives at the store.

[1426] For example, an elderly person visiting a cafe can launch a "Shopping Support App" and answer questions about a crossword puzzle. Based on their answers, their cognitive function is evaluated and the results are fed back to them.

[1427] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1428] Step 1:

[1429] The user starts the application and enters basic information, such as gender, age, and preferences. This provides the user's basic information.

[1430] Step 2:

[1431] The device formats the basic information and sends it to the server. Specifically, it converts the user's input data into JSON format and sends it to the server using an HTTP request. At this stage, the server saves the basic information in a database.

[1432] Step 3:

[1433] A conversation session begins when the user taps the "Start conversation" button, which allows the user's actions to be obtained as input.

[1434] Step 4:

[1435] The server generates brain teasers appropriate for the user at that time based on the user's basic information. Specifically, it generates questions using a generative AI model. In this generation process, the server dynamically creates questions based on the user's preferences and past data.

[1436] Step 5:

[1437] The generated question is sent to the terminal and presented to the user. The terminal displays the question and, if necessary, outputs it aloud. The user confirms the question and answers it by voice or text.

[1438] Step 6:

[1439] The user's answer is sent from the device to the server. The device formats the user's answer and sends it to the server using an HTTP request, which causes the server to receive the user's answer as input.

[1440] Step 7:

[1441] The server analyzes the user's answers. Specifically, it uses natural language processing (NLP) technology to evaluate the accuracy of the answers, vocabulary diversity, response speed, etc. The analysis can be performed using the Google Cloud Natural Language API.

[1442] Step 8:

[1443] Based on the analysis results, the server performs a dementia risk assessment. A generative AI model is used to comprehensively evaluate the analysis results and past data. This assessment determines the risk of developing dementia and the progression of the condition.

[1444] Step 9:

[1445] The evaluation results and feedback are sent from the server to the device and presented to the user. The device displays the evaluation results and provides audio feedback, including recommendations for the next brain exercise.

[1446] Step 10:

[1447] The server saves the conversation history and evaluation results in a database, which enables continuous monitoring in subsequent sessions. This saving process includes inserting data into the database.

[1448] The above processing steps allow users to easily check their cognitive function in a physical store, enabling them to continuously work on preventing dementia.

[1449] 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.

[1450] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. The following explains the system's program processing in detail, including specific examples.

[1451] First-time user registration

[1452] The user enters basic information

[1453] Users launch the application on their smartphones, PCs, or other devices and enter basic information such as gender, age, and interests. This information is used to provide users with brain exercises tailored to their needs.

[1454] The device sends the information to the server

[1455] The device formats the user's basic information and sends it to the server, which then stores it in a database.

[1456] Start a conversation

[1457] A user starts a conversation

[1458] When the user taps the "Start Conversation" button, the device sends a conversation start request to the server.

[1459] The server selects the brain teaser.

[1460] The server runs an algorithm to generate the most suitable brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 and likes crossword puzzles, the server will select a word-based quiz.

[1461] The server sends a question to the device

[1462] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[1463] User responses and emotion recognition

[1464] The user answers the question

[1465] The user answers the questions presented to them, and can do so by text input or voice input.

[1466] The device sends the answer to the server

[1467] The terminal formats the user's response data and sends it to the server.

[1468] The server recognizes emotions using an emotion engine

[1469] When the server analyzes the received answer data, it uses an emotion engine to recognize the user's emotional state. For example, if the answer sounds nervous, it recognizes the emotion "nervous."

[1470] Running the diagnostic algorithm

[1471] The server analyzes the responses and emotional data

[1472] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of the answers, reaction time, emotional state, etc.

[1473] The server assesses the risk of dementia

[1474] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past conversation data and emotional data.

[1475] Providing feedback

[1476] The server generates and sends the evaluation results

[1477] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[1478] The device displays feedback to the user

[1479] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[1480] Save data and prepare for next time

[1481] The server stores conversation history and emotion data

[1482] The server stores the user's conversation history, evaluation results, and emotion data in a database, which allows for continuous monitoring in the next conversation session.

[1483] Specific examples

[1484] First-time registration and conversation flow

[1485] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[1486] The device formats this information and sends it to the server, which stores it in a database.

[1487] The user taps the "Start Conversation" button and the conversation session begins.

[1488] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the terminal.

[1489] The terminal displays the question to the user and plays it aloud.

[1490] A user answers, "The last crossword puzzle I solved was on the theme 'zoo'."

[1491] The device formats the response and sends it to the server.

[1492] The server analyzes the answers and uses an emotion engine to recognize the user's emotions (e.g., "fun").

[1493] The server evaluates cognitive function, generates feedback based on the evaluation results, and sends it to the device.

[1494] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[1495] The server stores the conversation history and emotion data in a database in preparation for the next conversation session.

[1496] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[1497] The processing flow will be explained below.

[1498] Step 1:

[1499] The user launches the app.

[1500] When a user launches the application on a device such as a smartphone or PC, an input form for basic information (gender, age, interests, etc.) appears on the screen.

[1501] Step 2:

[1502] The user enters basic information.

[1503] The user enters information such as gender, age, and preferences, and then taps the "Save" button to complete the entry.

[1504] Step 3:

[1505] The terminal sends the input data to the server.

[1506] The terminal formats the input data and generates a request to be sent to the server, and the request is sent.

[1507] Step 4:

[1508] The server stores user information.

[1509] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[1510] Step 5:

[1511] A user initiates a conversation.

[1512] The user taps the "Start conversation" button, and the device sends a conversation start request to the server.

[1513] Step 6:

[1514] The server selects a brain teaser.

[1515] The server runs an algorithm based on the user's basic information and past conversation data to generate the most appropriate brain teaser. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[1516] Step 7:

[1517] The server sends a question to the terminal.

[1518] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[1519] Step 8:

[1520] The user answers the question.

[1521] The user answers the questions posed, and the answers can be entered by text or voice.

[1522] Step 9:

[1523] The device sends the response to the server.

[1524] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[1525] Step 10:

[1526] The server recognizes emotions using an emotion engine.

[1527] The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state, such as "tension" or "joy" based on the tone of voice and the content of the text.

[1528] Step 11:

[1529] The server analyzes the responses and sentiment data.

[1530] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of responses, reaction time, perceived emotional state, and more.

[1531] Step 12:

[1532] The server assesses the risk of dementia.

[1533] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, taking into account past conversation data and emotion data to make a comprehensive assessment.

[1534] Step 13:

[1535] The server generates and transmits the evaluation results.

[1536] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[1537] Step 14:

[1538] The device displays feedback to the user.

[1539] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[1540] Step 15:

[1541] The server stores conversation history and emotion data.

[1542] The server stores the user's conversation history, evaluation results, and emotion data in a database, which organizes the data for use in the next conversation session.

[1543] The above is a specific processing flow of the system of the present invention that combines an emotion engine. This allows users to exercise their minds on a daily basis, objectively and specifically evaluate the state of their cognitive function, and receive more personalized feedback and improvement measures.

[1544] Example 2

[1545] 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."

[1546] Conventional dementia prevention and monitoring tools assess users' cognitive function primarily based on data entry accuracy and reaction time, without taking into account their emotional state. This makes it difficult to understand the user's condition in more detail or provide personalized feedback. This makes it difficult to detect the risk and progression of dementia early and develop appropriate countermeasures.

[1547] 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.

[1548] In this invention, the server includes means for receiving and storing basic information of the user, means for generating and presenting brain teasing questions based on the basic information of the user, and means for recognizing the emotional state of the user from the response data, thereby enabling the provision of personalized feedback that takes the emotional state into consideration.

[1549] "Basic user information" refers to basic attribute information about a user, such as the user's gender, age, and preferences.

[1550] "Means for storing" refers to means for storing received information in a storage system such as a database.

[1551] "Brain teasers" are tasks such as quizzes and questions designed to stimulate and assess the user's cognitive function.

[1552] A "means for generating questions" is an algorithm or system that generates optimal brain-teasing questions based on the user's basic information and past data.

[1553] The "presentation means" is a means for presenting the generated question to the user visually or audibly.

[1554] A "means for receiving a response" is a means for receiving the text or voice input by the user and formatting it into an analyzable format.

[1555] The "means of analysis" refers to the means of analyzing the received response data using natural language processing or an emotion recognition engine.

[1556] A "means for assessing the risk of developing dementia and the progression of the condition" is an algorithm or system that assesses a user's risk of dementia and its progression based on the results of the analysis.

[1557] "Means for providing evaluation results" refers to means for notifying users of the evaluation results visually or audibly.

[1558] "Means for recognizing emotional states" refers to means for identifying emotions from user response data using natural language processing and voice analysis.

[1559] The "means for comprehensively adjusting the evaluation results" is an algorithm or system that performs a comprehensive evaluation including the recognized emotional state and adjusts the final result.

[1560] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. Specific embodiments of the system are described below.

[1561] The system of the present invention is used by users via devices such as smartphones or PCs. The user launches the application and enters basic information such as gender, age, and preferences. The device formats this information and sends it to the server. The server stores the received information in a database and uses it for subsequent processing.

[1562] In this system, when the user starts a conversation, the server generates optimal brain teasing questions based on the user's basic information and past conversation data. The generated questions are sent to the device and presented to the user. The user answers the questions by text input or voice input.

[1563] The device formats the user's response data and sends it to the server. The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state. Based on the data obtained from this analysis, the server evaluates the user's cognitive function. This makes it possible to assess the risk of dementia and monitor its progression.

[1564] The evaluation results are sent from the server to the device, where they are displayed to the user. A voice notification is also possible. The server then stores the user's conversation history, evaluation results, and recognized emotion data in a database for future use in the next session.

[1565] Specific examples

[1566] For example, a 65-year-old woman who enjoys crossword puzzles launches the app and enters her gender, age, and interests. The device formats this information and sends it to the server. After the server stores the information in a database, the user can tap the "Start Conversation" button to begin a conversation.

[1567] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device. The device displays the question to the user and plays it back aloud. When the user answers, "The crossword puzzle I solved recently was on the theme of 'zoo,'" the device formats the answer and sends it to the server.

[1568] The server analyzes the response data and uses an emotion engine to recognize the user's emotion. For example, it recognizes "enjoyment." The server then evaluates cognitive function based on the analysis results, generates feedback based on the evaluation results, and sends it to the device. The device then displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[1569] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[1570] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1571] Step 1:

[1572] The user enters basic information

[1573] Input: The user enters basic information such as gender, age, and preferences into an application on a smartphone or PC.

[1574] What it does: The user fills out a form on the app's start screen, filling out their information, such as being a 65-year-old woman who likes crossword puzzles.

[1575] Output: The input basic information is prepared as data for further processing.

[1576] Step 2:

[1577] The device sends the information to the server

[1578] Input: Basic information entered by the user.

[1579] What happens: The device takes the input information, formats it into JSON, and sends the formatted data to the server over HTTPS.

[1580] Output: Formatted basic information data received by the server.

[1581] Step 3:

[1582] The server stores basic information in a database

[1583] Input: Basic information data sent from the device.

[1584] Specific operation: The server stores the received basic information in a database, for example, a NoSQL database.

[1585] Output: User basic information stored in the database.

[1586] Step 4:

[1587] A user starts a conversation

[1588] Input: The action a user takes to start a conversation, such as tapping a button.

[1589] Specific operation: The user taps the "Start conversation" button on the app screen, which causes the device to send a conversation start request to the server.

[1590] Output: A conversation initiation request sent to the server.

[1591] Step 5:

[1592] The server chooses the best brain teaser

[1593] Input: User basic information and past conversation data.

[1594] How it works: The server uses a generative AI model to select the most suitable brain exercise for a user based on their basic information and past conversation data. For example, if a 65-year-old woman who likes crossword puzzles is selected, the server will select a crossword-related quiz.

[1595] Output: Optimal brain teaser question data.

[1596] Step 6:

[1597] The server sends a question to the device

[1598] Input: Selected brain teaser question data.

[1599] Specific operation: The server sends the generated question data to the terminal, which then displays the received question to the user and plays it back aloud.

[1600] Output: The question that is presented to the user.

[1601] Step 7:

[1602] The user answers the question

[1603] Input: The question posed by the server.

[1604] What happens: The user answers the question by typing or speaking, for example, "The last crossword puzzle I solved was about the zoo."

[1605] Output: User response data.

[1606] Step 8:

[1607] The device sends the answer to the server

[1608] Input: User response data.

[1609] Specific operation: The device receives the user's response data, formats it into JSON format again, and sends this formatted data to the server using HTTPS.

[1610] Output: The formatted response data received by the server.

[1611] Step 9:

[1612] The server recognizes emotions using an emotion engine

[1613] Input: Response data sent from the device.

[1614] Specific operation: The server analyzes the received response data and uses the emotion engine to recognize the user's emotional state, for example, identifying emotions such as "joy" or "tension."

[1615] Output: Recognized emotion data.

[1616] Step 10:

[1617] The server analyzes the responses and emotional data

[1618] Input: User response data and sentiment data.

[1619] How it works: The server uses natural language processing technology to analyze the content of the answers, and simultaneously processes the response time and emotional state, thereby evaluating the accuracy of the answers, response time, and emotional state.

[1620] Output: User's cognitive function assessment data.

[1621] Step 11:

[1622] The server assesses the risk of dementia

[1623] Input: User's cognitive assessment data.

[1624] Specific operation: Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past data as well.

[1625] Output: Dementia risk assessment results.

[1626] Step 12:

[1627] The server generates and sends the evaluation results

[1628] Input: Dementia risk assessment results.

[1629] Specific operation: The server generates feedback on the cognitive function assessment results and recommended future brain exercises and sends them to the device.

[1630] Output: Feedback data sent to the device.

[1631] Step 13:

[1632] The device displays feedback to the user

[1633] Input: Feedback data sent by the server.

[1634] Specific operation: The device receives the feedback data and notifies the user by text or voice, so that the user receives the feedback.

[1635] Output: The feedback that is presented to the user.

[1636] Step 14:

[1637] The server stores conversation history and emotion data

[1638] Input: User conversation history, rating results, and emotion data.

[1639] What happens: The server stores this data in a database and keeps it available for the next session.

[1640] Output: User history and emotion data stored in a database.

[1641] In this way, the system of the present invention comprehensively analyzes the user's basic information, response data, and emotional state, and provides a detailed assessment of cognitive function and personalized feedback, thereby enabling the prevention of dementia risk and early detection.

[1642] (Application example 2)

[1643] 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."

[1644] Conventional dementia prevention systems simply provide mental exercises without considering the user's emotional state. This results in a lack of feedback that reflects the user's emotional changes, reducing the accuracy of long-term dementia prevention and progress monitoring. It is necessary to provide dementia prevention training for the elderly as a digital assistant in brick-and-mortar stores, aiming to provide detailed support tailored to each individual user.

[1645] The identification process by the identification 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 receiving and storing basic information about the user, means for generating and presenting brain teasing questions based on the user's basic information, means for receiving and analyzing the user's answers, means for analyzing the user's emotions using an emotion recognition engine, and means for generating feedback according to data acquired based on the user's emotional state. This makes it possible to provide dementia prevention measures according to the individual emotional state of the user.

[1646] A "user" is someone who uses the system to exercise their mind to prevent dementia.

[1647] "Basic information" refers to personal data such as gender, age, and preferences provided by the user.

[1648] "Means for storing" refers to functions or devices that allow users' basic information to be saved and reused.

[1649] "Generating means" refers to algorithms or devices that generate appropriate brain teasers or questions based on basic information about the user.

[1650] "Presentation means" refers to the function or device for displaying the generated brain teasers or questions to the user.

[1651] "Means for analysis" refers to the algorithms or devices used to evaluate user responses and analyze their content and accuracy.

[1652] An "emotion recognition engine" is an artificial intelligence technology or software that analyzes a user's voice or text and recognizes their emotional state.

[1653] "Means for generating feedback" refers to algorithms or devices that provide appropriate responses or advice to users based on analysis results and emotional data.

[1654] "Emotional state" refers to a state that indicates a user's changes in feelings or mood.

[1655] "Dementia risk assessment" refers to algorithms and devices used to assess a user's risk of developing dementia and the progression of the condition.

[1656] "Means for providing assessment results" refers to functions and devices for notifying users of the assessed dementia risk and progression.

[1657]

[1658] This invention builds a system that acts as a digital assistant in brick-and-mortar stores, providing brain exercise sessions aimed at preventing dementia, particularly for the elderly. The system operates as a smartphone application and provides individualized feedback based on the user's basic information and emotional recognition, enabling more effective dementia prevention and progression monitoring.

[1659] System configuration

[1660] The system consists of the following components:

[1661] User device: Smartphone or tablet used by the user

[1662] Server: A remote server that processes and analyzes data

[1663] Database: A database that stores basic information about users, past conversation data, and emotional data.

[1664] What the program does

[1665] When a user uses the app for the first time, they enter basic information such as gender, age, and interests on their device. The device sends this information to the server, which then stores it in a database.

[1666] When you tap the "Start Conversation" button in the application, your device sends a conversation start request to the server, which then generates the optimal brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 years old and enjoys crossword puzzles, the server will generate a question such as "Tell me about the crossword puzzle you solved recently."

[1667] The generated question is sent to the device and presented to the user. When the user answers, the answer data is sent to the server as text or voice. The server uses natural language processing technology and an emotion engine to analyze the answer and recognize the user's emotional state. For example, if the answer sounds fun, the emotion of "fun" is recognized.

[1668] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data, and generates feedback based on the results. The feedback is sent to the device and displayed to the user. For example, a positive message such as "Your cognitive function is good. Let's continue next time" is provided.

[1669] Technology used

[1670] Emotion Recognition Engine: Uses generative AI models and natural language processing techniques to recognize emotional states. Examples include the spacy and transformers libraries.

[1671] Database: A database management system for storing user basic information and past conversation data. For example, an SQL or NoSQL database.

[1672] Server technology: Back-end servers for data processing and analysis. Examples include Python-based servers and cloud services.

[1673] Specific examples

[1674] Example 1:

[1675] The user launches the app and enters their gender (female), age (70), and preference (puzzles). The app begins asking questions such as, "Tell me about the puzzle you solved recently." The user answers, "I solved a number puzzle recently," and the answer is sent to the server. The emotion engine recognizes "fun," and provides feedback such as, "Cognitive function is good. Please continue to enjoy the app."

[1676] Example 2:

[1677] prompt:

[1678] User ID = "id1"

[1679] Question = Start conversation session (user ID)

[1680] Brain teaser presentation (user ID, question)

[1681] Answer = "The puzzle I solved recently was about flowers."

[1682] Emotion result = Answer and Emotion Recognition(User ID, Answer)

[1683] Evaluation result = Cognitive function evaluation (user ID)

[1684] print(f"Feedback: {Evaluation result}")

[1685]

[1686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1687]

[1688] Step 1:

[1689] The user starts the application on the device and enters basic information (gender, age, interests).

[1690] Input: The user enters basic information such as gender, age, and preferences.

[1691] Data processing: The terminal formats the basic information entered and sends it to the server.

[1692] Output: Basic formatted information is sent to the server.

[1693] Step 2:

[1694] The server stores the basic information in a database.

[1695] Input: Basic information sent from the device.

[1696] Data processing: The server records the basic information received in a database.

[1697] Output: Basic information stored in the database.

[1698] Step 3:

[1699] The user taps the Start Conversation button.

[1700] Input: The user taps the "Start Conversation" button on their device.

[1701] Data processing: The terminal sends a conversation start request to the server.

[1702] Output: A conversation initiation request is sent to the server.

[1703] Step 4:

[1704] The server generates optimal brain exercises based on the user's basic information and past conversation data.

[1705] Input: Basic information about the user and past conversation data.

[1706] Data processing: The server uses the generated AI model to generate the optimal brain teaser for the user.

[1707] Output: The generated brain teaser.

[1708] Step 5:

[1709] The server sends the generated brain teasers to the terminal, which then presents them to the user.

[1710] Input: A server-generated brain teaser.

[1711] Data processing: The device formats the brain teasers received from the server for display.

[1712] Output: The brain teaser displayed on the terminal.

[1713] Step 6:

[1714] The user answers the questions presented.

[1715] Input: The user inputs the answer into the terminal by voice or text.

[1716] Data processing: The terminal formats the entered answers and sends them to the server.

[1717] Output: The formatted response data is sent to the server.

[1718] Step 7:

[1719] The server analyzes the response data and recognizes the user's emotions using an emotion recognition engine.

[1720] Input: Response data sent from the device.

[1721] Data processing: The server analyzes the responses using natural language processing technology and an emotion recognition engine to recognize the emotional state.

[1722] Output: Analysis results and emotion data.

[1723] Step 8:

[1724] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data.

[1725] Input: Analysis results and emotion data.

[1726] Data processing: The server evaluates the dementia risk using an evaluation algorithm.

[1727] Output: Dementia risk assessment results.

[1728] Step 9:

[1729] The server generates feedback based on the evaluation results and sends it to the device.

[1730] Input: Dementia risk assessment results.

[1731] Data processing: The server generates feedback based on the evaluation results.

[1732] Output: The generated feedback is sent to the device.

[1733] Step 10:

[1734] The device displays the feedback to the user.

[1735] Input: Feedback sent by the server.

[1736] Data processing: The device formats the feedback for display.

[1737] Output: Feedback displayed on the device.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] [Fourth embodiment]

[1742] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1743] 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.

[1744] 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).

[1745] 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.

[1746] 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.

[1747] 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).

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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."

[1755] This invention utilizes an AI voice chatbot that utilizes generative AI to provide a system that allows users to easily monitor the prevention and progression of dementia. Below, the processing of the system's program is explained in natural language, with specific examples included. The subjects used are server, terminal, and user.

[1756] First-time user registration

[1757] The user enters basic information

[1758] A user launches the application and enters basic information such as gender, age, and interests, which is important for generating brain teasers tailored to the user's needs.

[1759] The device sends the information to the server

[1760] The device formats the basic information entered and sends it to the server, which stores it in a database.

[1761] Start a conversation

[1762] A user starts a conversation

[1763] A conversation session begins when the user taps the "Start Conversation" button.

[1764] The server selects the brain teaser.

[1765] The server runs an algorithm that uses basic information about the user to generate a brain teaser that's appropriate for that moment. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[1766] The server sends a question to the device

[1767] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[1768] User Answers

[1769] The user answers the question

[1770] The user answers the presented brain teasers by either text input or voice input.

[1771] The device sends the answer to the server

[1772] The device formats the user's response and sends it to the server.

[1773] Running the diagnostic algorithm

[1774] The server parses the answer

[1775] The server then analyzes the responses it receives using natural language processing (NLP) techniques, which evaluate accuracy, vocabulary diversity, response speed, and other factors.

[1776] The server assesses the risk of dementia

[1777] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, and makes a comprehensive judgment by also referencing past conversation data.

[1778] Providing feedback

[1779] The server generates and sends the evaluation results

[1780] The server generates feedback based on the evaluation results and sends it to the device, including the evaluation results and recommendations for the next brain exercise.

[1781] The device displays feedback to the user

[1782] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[1783] Save data and prepare for next time

[1784] The server stores the conversation history

[1785] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[1786] Specific examples

[1787] First-time registration and conversation flow

[1788] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[1789] The device formats this information and sends it to the server, which stores it in a database.

[1790] The user taps the "Start Conversation" button to begin the session.

[1791] The server generates a question based on the user's information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device.

[1792] The device displays the question to the user and plays it aloud.

[1793] The user answered, "The last crossword I solved was on the theme of 'zoo'."

[1794] The device formats the response and sends it to the server.

[1795] The server analyzes the responses, evaluates cognitive function, generates evaluation results, and sends them to the device.

[1796] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[1797] In this way, this invention allows users to easily perform daily mental exercises and evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[1798] The processing flow will be explained below.

[1799] Step 1:

[1800] The user launches the app.

[1801] A user launches the application on a device such as a smartphone or PC. An input form for basic information (gender, age, interests, etc.) is displayed on the screen.

[1802] Step 2:

[1803] The user enters basic information.

[1804] The user enters information such as gender, age, and preferences, and then completes the entry by tapping the "Save" button.

[1805] Step 3:

[1806] The terminal sends the input data to the server.

[1807] The terminal formats the input data and generates a request to send to the server. The request is sent to the server.

[1808] Step 4:

[1809] The server stores user information.

[1810] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[1811] Step 5:

[1812] A user initiates a conversation.

[1813] When the user taps the "Start conversation" button, the device sends a conversation start request to the server.

[1814] Step 6:

[1815] The server selects a brain teaser.

[1816] The server runs an algorithm to generate appropriate brain teasers based on the user's basic information, and sends the selected content to the device.

[1817] Step 7:

[1818] The terminal presents a question to the user.

[1819] The device displays the question received from the server on the screen and plays it back aloud if necessary.

[1820] Step 8:

[1821] The user answers the question.

[1822] The user answers the questions presented to them by text input or voice input.

[1823] Step 9:

[1824] The device sends the response to the server.

[1825] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[1826] Step 10:

[1827] The server parses the response.

[1828] The server analyzes the responses it receives using natural language processing (NLP) technology and evaluates the accuracy of the responses and response time.

[1829] Step 11:

[1830] The server assesses the risk of dementia.

[1831] The server then runs an algorithm based on the analysis results to assess the risk and progression of dementia, taking into account past conversation data as well.

[1832] Step 12:

[1833] The server generates and transmits the evaluation results.

[1834] The server generates the evaluation results and feedback content and sends them to the terminal.

[1835] Step 13:

[1836] The device displays feedback to the user.

[1837] The device displays the feedback received from the server on the screen and plays it back as audio if necessary.

[1838] Step 14:

[1839] The server stores the conversation history.

[1840] The server stores the user's conversation history and evaluation results in a database and organizes the data to be used in the next conversation session.

[1841] The above is a specific processing flow of the system for carrying out the invention.

[1842] Example 1

[1843] 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."

[1844] In recent years, with the progress of the aging society, the prevention and early detection of dementia have become important issues. Conventional dementia prevention systems are insufficient in providing individualized feedback to users, and in many cases, they require specialized operation and knowledge, placing a heavy burden on users. Therefore, there is a need for a system that allows users to easily monitor the prevention and progression of dementia.

[1845] 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.

[1846] In this invention, the server includes means for generating brain teasing questions based on the user's basic information using a generative AI model, means for analyzing the answers received by the server using natural language processing technology, and means for assessing the risk of developing dementia and the progression of the disease based on the analysis results. This enables the user to receive continuous and personalized assessment and feedback of cognitive function without requiring specialized knowledge or operation.

[1847] "User" refers to an individual who uses this system to prevent dementia or monitor its progression.

[1848] "Basic information" refers to information necessary to generate personalized brain teasers, such as the user's gender, age, and preferences.

[1849] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized brain teasers based on basic user information.

[1850] "Brain teasers" refer to quizzes or questions used to assess a user's cognitive function.

[1851] "Questions" refer to specific tasks or quizzes related to brain teasers generated by the generative AI model.

[1852] "Answer" refers to the response or reaction a user provides to a brain teaser.

[1853] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[1854] "Analysis results" refers to the evaluation of user responses analyzed using natural language processing technology.

[1855] "Risk of developing dementia" refers to a measure that assesses the likelihood that a user will develop dementia in the future.

[1856] "Progression" refers to an indicator that shows the progress of dementia and its precursor, mild cognitive impairment.

[1857] "Evaluation results" refers to a comprehensive evaluation and feedback of cognitive function based on the analysis results.

[1858] "Feedback" refers to advice or information provided to users based on the evaluation results.

[1859] MODE FOR CARRYING OUT THE INVENTION

[1860] The purpose of this invention is to enable users to easily prevent dementia and monitor its progression using a system that utilizes generative AI. The configuration and operation of the system are described in detail below.

[1861] System configuration

[1862] This system mainly consists of a server, a terminal, and a user.

[1863] The server integrates a database, generative AI model, and natural language processing technology. Here, MySQL is used for the database, GPT-3 for the generative AI model, and spaCy and BERT for the natural language processing technology.

[1864] A terminal is a device operated by a user, such as a smartphone, tablet, or PC. This terminal is equipped with a communication module for network communication with the server, and also has input devices (keyboard, touch panel) and output devices (display, speaker).

[1865] Users are ordinary individuals who use the system to assess their own cognitive function and receive feedback.

[1866] System operating principle

[1867] Acquisition and storage of basic user information

[1868] When a user first uses the system, they launch the application and enter basic information such as gender, age, and interests. For example, if the user is female, 65 years old, and enjoys crossword puzzles, the device formats this information and sends it in JSON format to the server. The server receives this information and stores it in a MySQL database.

[1869] Brain teaser generation

[1870] When the user taps the "Start Conversation" button, the server uses the pre-stored basic information to create a prompt sentence that generates a brain teaser using a generative AI model (GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input into the generative AI model. An example of the generated brain teaser would be a question such as "Tell me about the crossword puzzle you solved recently."

[1871] Posting questions and submitting answers

[1872] The server sends the generated question to the device, which displays it to the user, and the user answers by text input or voice input, which the device then sends back to the server in JSON format.

[1873] Analysis of responses and assessment of dementia risk

[1874] The server analyzes the received responses using natural language processing technology (spaCy or BERT). Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression. This assessment also references past conversation data to make a comprehensive judgment.

[1875] Providing and storing feedback

[1876] The server generates feedback based on the evaluation results and sends it to the device. The device displays this feedback to the user and can also notify them by voice. For example, it may include something like, "Your cognitive function is good. Let's continue next time." Finally, the server stores this conversation history and the evaluation results in a database.

[1877] Specific examples

[1878] 1. The user starts the application and enters the following basic information: "Gender: Female," "Age: 65," and "Hobbies: Crossword puzzles."

[1879] 2. The device formats this information and sends it to the server, which stores it in a database.

[1880] 3. The user taps the "Start Conversation" button to begin the session.

[1881] 4. Based on the user information, the server uses the generative AI model to input the prompt sentence, "Generate a brain teaser for a 65-year-old female user who likes crossword puzzles."

[1882] 5. The generative AI model generates a question such as "Tell me about the crossword puzzle you solved recently," and the server sends this to the device.

[1883] 6. The device displays it to the user and plays it aloud.

[1884] 7. A user answers, "The last crossword puzzle I solved was about the zoo."

[1885] 8. The device formats the response and sends it to the server.

[1886] 9. The server analyzes the answers, evaluates cognitive function, generates evaluation results, and sends them to the device.

[1887] 10. The device displays feedback to the user saying, "Your cognitive function is good. Let's continue next time."

[1888] In this way, this invention allows users to exercise their minds on a daily basis and easily evaluate the state of their cognitive function, thereby contributing to the prevention and early detection of dementia.

[1889] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1890] Step 1:

[1891] A user starts the application and enters basic information (gender, age, and interests). For example, the user enters gender "female," age "65," and interest "crossword puzzles." This inputs data tailored to the individual user.

[1892] Input: Gender "Female", Age "65", Interest "Crossword puzzles"

[1893] Output: Screen display where basic information is entered

[1894] ---

[1895] Step 2:

[1896] The device formats this basic information and sends it to the server in JSON format. For example, it generates the following JSON data:

[1897] json

[1898] {

[1899] "gender": "female",

[1900] "age": 65,

[1901] "preferences": ["crossword puzzle"]

[1902] }

[1903] Input: Basic information entered by the user

[1904] Output: JSON format data is sent to the server

[1905] ---

[1906] Step 3:

[1907] The server stores the received basic information in a database, for example, a MySQL database.

[1908] Input: Basic information data in JSON format

[1909] Output: User information stored in the database

[1910] ---

[1911] Step 4:

[1912] The user taps the "Start Conversation" button to begin a conversation session. The app catches this event and proceeds to the next processing step.

[1913] Input: Tap the "Start Conversation" button

[1914] Output: Start trigger for a conversation session

[1915] ---

[1916] Step 5:

[1917] The server uses the pre-stored basic information to create a prompt sentence to generate a brain teaser using a generative AI model (e.g., GPT-3). For example, the prompt sentence "Please generate a brain teaser for a 65-year-old female user who likes crossword puzzles" is input to the generative AI model.

[1918] Input: User basic information stored in the database

[1919] Output: The prompt sentence that is fed into the generative AI model

[1920] ---

[1921] Step 6:

[1922] A generative AI model generates brain teasers, such as questions like, "Tell me about the last crossword puzzle you solved."

[1923] Input: prompt statement

[1924] Output: Generated brain teaser (question)

[1925] ---

[1926] Step 7:

[1927] The server sends the generated question to the terminal, which then displays the question to the user. For example, the terminal displays the question "Tell me about the crossword puzzle you solved recently" on the screen.

[1928] Input: Generated brain teaser (question)

[1929] Output: The question that is displayed to the user

[1930] ---

[1931] Step 8:

[1932] The user answers by text or voice input. For example, the user might answer by text input, "The last crossword puzzle I solved was on the theme 'zoo.'"

[1933] Input: User's answer

[1934] Output: The input response data

[1935] ---

[1936] Step 9:

[1937] The device formats the user's answer and sends it to the server in JSON format, for example generating the following JSON data:

[1938] json

[1939] {

[1940] "answer": "The last crossword I solved was about a zoo."

[1941] }

[1942] Input: User's answer

[1943] Output: JSON formatted answer data is sent to the server

[1944] ---

[1945] Step 10:

[1946] The server analyzes the received answers using natural language processing techniques (e.g., spaCy and BERT), evaluating the accuracy of the answers, vocabulary diversity, and response speed.

[1947] Input: JSON formatted response data

[1948] Output: Analysis results

[1949] ---

[1950] Step 11:

[1951] The server then runs an algorithm based on the analysis results to assess the user's dementia risk and progression, taking into account past conversation data.

[1952] Input: Analysis results and past conversation data

[1953] Output: Assessment results of dementia risk and progression

[1954] ---

[1955] Step 12:

[1956] The server generates feedback based on the evaluation results and sends it to the device. For example, it generates feedback such as "Your cognitive function is good. Let's continue next time."

[1957] Input: Dementia risk and progression assessment results

[1958] Output: Feedback data

[1959] ---

[1960] Step 13:

[1961] The device will display feedback to the user, such as an on-screen message like "Your cognitive function is good. Let's continue next time." Audio notifications are also possible.

[1962] Input: Feedback data

[1963] Output: Feedback that is displayed to the user

[1964] ---

[1965] Step 14:

[1966] The server stores the conversation history and evaluation results in a database, allowing for continuous monitoring in subsequent sessions.

[1967] Input: Conversation history and evaluation results

[1968] Output: History and evaluation results stored in a database

[1969] (Application example 1)

[1970] 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."

[1971] In modern society, the prevention and progression management of dementia have become important issues as the population ages. It is particularly difficult for elderly people who visit physical stores to easily check their cognitive function. There is also a need for fun, sustained mental exercises in daily life to maintain cognitive function. However, existing systems are unable to meet these needs, so it is necessary to develop a method that allows for easy checking and continuous monitoring of cognitive function.

[1972] 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.

[1973] In this invention, the server includes means for receiving and storing basic information of a user, means for generating and presenting brain teasers based on the user's basic information, means for receiving and analyzing the user's answers, means for evaluating the risk of developing dementia and the state of progression based on the analysis results, means for providing the evaluation results to the user, and means for the user to check their cognitive function when visiting a physical store. This enables elderly people visiting a physical store to easily check their cognitive function using their smartphone when they visit, making it possible to engage in enjoyable and sustainable dementia prevention efforts.

[1974] "Basic user information" refers to basic personal data such as gender, age, and preferences that are necessary for the system to function.

[1975] "Brain teasers" are questions or tasks presented to users with the purpose of activating or assessing cognitive function.

[1976] "Means for generating and presenting questions" refers to a method or function for generating optimal brain teasers based on the user's basic information and displaying them to the user.

[1977] The "means for receiving and analyzing user responses" refers to a method or function for receiving the content of a user's response and analyzing it to evaluate the content.

[1978] "Means for assessing the risk of developing dementia and the progression of dementia based on analysis results" refers to a method or function that uses the analysis results of a user's responses to determine and evaluate a person's risk of dementia and the progression of that person's dementia.

[1979] "Means for providing evaluation results to users" refers to the method or function for communicating the evaluation results to users.

[1980] "Means for checking cognitive function when a user visits a physical store" refers to a method or function for evaluating a user's cognitive function on the spot when the user visits a physical store.

[1981] This invention provides a system for easily assessing the cognitive function of elderly people visiting physical stores. The system generates brain-teasing questions based on the user's basic information, and assesses the user's risk of dementia based on their answers. Specific embodiments of the system are described below.

[1982] System configuration

[1983] The system consists of three elements: a server, a terminal, and a user.

[1984] Hardware and Software

[1985] The servers have high-performance computing power, store and analyze data, and use natural language processing (NLP) and generative AI models (e.g., GPT-3).

[1986] The devices used by users are smartphones or tablets, and the system uses Google Text-to-Speech (TTS) for voice input and Google Cloud Natural Language API for conversation analysis.

[1987] The users are elderly people who interact with the system using terminals when they visit a physical store.

[1988] User registration and basic information acquisition

[1989] When a user uses the system for the first time, they enter basic information (gender, age, preferences), which is sent from the terminal to the server and stored in the database.

[1990] Generating and presenting brain teasers

[1991] The server uses a generative AI model to generate brain teasing questions based on the stored basic information. The questions are dynamically changed according to the user's preferences. The generated questions are sent to the device and presented to the user.

[1992] Example prompt sentence:

[1993] "Tell me about the last crossword puzzle you solved."

[1994] User responses and analysis

[1995] Users answer the questions posed to them, and the answers are sent to the server via their device for analysis. The server uses natural language processing (NLP) technology to evaluate the accuracy of the answers, the diversity of vocabulary, and the speed of response.

[1996] Dementia risk assessment and feedback

[1997] Based on the analysis results, the server uses a generative AI model to assess dementia risk. The assessment results are sent to the device and presented to the user. Feedback includes recommendations for the next brain exercise.

[1998] Use in physical stores

[1999] Users visiting a physical store interact with the system using a terminal. For example, they can do mental exercises while waiting in a cafe or supermarket to check the state of their cognitive function. The system is intended for use in physical stores, and automatically starts the cognitive function check process when the user arrives at the store.

[2000] For example, an elderly person visiting a cafe can launch a "Shopping Support App" and answer questions about a crossword puzzle. Based on their answers, their cognitive function is evaluated and the results are fed back to them.

[2001] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2002] Step 1:

[2003] The user starts the application and enters basic information, such as gender, age, and preferences. This provides the user's basic information.

[2004] Step 2:

[2005] The device formats the basic information and sends it to the server. Specifically, it converts the user's input data into JSON format and sends it to the server using an HTTP request. At this stage, the server saves the basic information in a database.

[2006] Step 3:

[2007] A conversation session begins when the user taps the "Start conversation" button, which allows the user's actions to be obtained as input.

[2008] Step 4:

[2009] The server generates brain teasers appropriate for the user at that time based on the user's basic information. Specifically, it generates questions using a generative AI model. In this generation process, the server dynamically creates questions based on the user's preferences and past data.

[2010] Step 5:

[2011] The generated question is sent to the terminal and presented to the user. The terminal displays the question and, if necessary, outputs it aloud. The user confirms the question and answers it by voice or text.

[2012] Step 6:

[2013] The user's answer is sent from the device to the server. The device formats the user's answer and sends it to the server using an HTTP request, which causes the server to receive the user's answer as input.

[2014] Step 7:

[2015] The server analyzes the user's answers. Specifically, it uses natural language processing (NLP) technology to evaluate the accuracy of the answers, vocabulary diversity, response speed, etc. The analysis can be performed using the Google Cloud Natural Language API.

[2016] Step 8:

[2017] Based on the analysis results, the server performs a dementia risk assessment. A generative AI model is used to comprehensively evaluate the analysis results and past data. This assessment determines the risk of developing dementia and the progression of the condition.

[2018] Step 9:

[2019] The evaluation results and feedback are sent from the server to the device and presented to the user. The device displays the evaluation results and provides audio feedback, including recommendations for the next brain exercise.

[2020] Step 10:

[2021] The server saves the conversation history and evaluation results in a database, which enables continuous monitoring in subsequent sessions. This saving process includes inserting data into the database.

[2022] The above processing steps allow users to easily check their cognitive function in a physical store, enabling them to continuously work on preventing dementia.

[2023] 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.

[2024] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. The following explains the system's program processing in detail, including specific examples.

[2025] First-time user registration

[2026] The user enters basic information

[2027] Users launch the application on their smartphones, PCs, or other devices and enter basic information such as gender, age, and interests. This information is used to provide users with brain exercises tailored to their needs.

[2028] The device sends the information to the server

[2029] The device formats the user's basic information and sends it to the server, which then stores it in a database.

[2030] Start a conversation

[2031] A user starts a conversation

[2032] When the user taps the "Start Conversation" button, the device sends a conversation start request to the server.

[2033] The server selects the brain teaser.

[2034] The server runs an algorithm to generate the most suitable brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 and likes crossword puzzles, the server will select a word-based quiz.

[2035] The server sends a question to the device

[2036] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[2037] User responses and emotion recognition

[2038] The user answers the question

[2039] The user answers the questions presented to them, and can do so by text input or voice input.

[2040] The device sends the answer to the server

[2041] The terminal formats the user's response data and sends it to the server.

[2042] The server recognizes emotions using an emotion engine

[2043] When the server analyzes the received answer data, it uses an emotion engine to recognize the user's emotional state. For example, if the answer sounds nervous, it recognizes the emotion "nervous."

[2044] Running the diagnostic algorithm

[2045] The server analyzes the responses and emotional data

[2046] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of the answers, reaction time, emotional state, etc.

[2047] The server assesses the risk of dementia

[2048] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past conversation data and emotional data.

[2049] Providing feedback

[2050] The server generates and sends the evaluation results

[2051] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[2052] The device displays feedback to the user

[2053] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[2054] Save data and prepare for next time

[2055] The server stores conversation history and emotion data

[2056] The server stores the user's conversation history, evaluation results, and emotion data in a database, which allows for continuous monitoring in the next conversation session.

[2057] Specific examples

[2058] First-time registration and conversation flow

[2059] The user launches the app and enters their gender (female), age (65), and interest (crossword puzzles).

[2060] The device formats this information and sends it to the server, which stores it in a database.

[2061] The user taps the "Start Conversation" button and the conversation session begins.

[2062] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the terminal.

[2063] The terminal displays the question to the user and plays it aloud.

[2064] A user answers, "The last crossword puzzle I solved was on the theme 'zoo'."

[2065] The device formats the response and sends it to the server.

[2066] The server analyzes the answers and uses an emotion engine to recognize the user's emotions (e.g., "fun").

[2067] The server evaluates cognitive function, generates feedback based on the evaluation results, and sends it to the device.

[2068] The device displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[2069] The server stores the conversation history and emotion data in a database in preparation for the next conversation session.

[2070] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[2071] The processing flow will be explained below.

[2072] Step 1:

[2073] The user launches the app.

[2074] When a user launches the application on a device such as a smartphone or PC, an input form for basic information (gender, age, interests, etc.) appears on the screen.

[2075] Step 2:

[2076] The user enters basic information.

[2077] The user enters information such as gender, age, and preferences, and then taps the "Save" button to complete the entry.

[2078] Step 3:

[2079] The terminal sends the input data to the server.

[2080] The terminal formats the input data and generates a request to be sent to the server, and the request is sent.

[2081] Step 4:

[2082] The server stores user information.

[2083] The server stores the received data in a database and returns a response indicating that storage has been completed to the terminal.

[2084] Step 5:

[2085] A user initiates a conversation.

[2086] The user taps the "Start conversation" button, and the device sends a conversation start request to the server.

[2087] Step 6:

[2088] The server selects a brain teaser.

[2089] The server runs an algorithm based on the user's basic information and past conversation data to generate the most appropriate brain teaser. For example, if the user is 65 and likes crossword puzzles, it will select a word-based quiz.

[2090] Step 7:

[2091] The server sends a question to the terminal.

[2092] The server generates a brain teaser (e.g., "Say the names of three animals backwards") and sends it to the device, which then presents it to the user.

[2093] Step 8:

[2094] The user answers the question.

[2095] The user answers the questions posed, and the answers can be entered by text or voice.

[2096] Step 9:

[2097] The device sends the response to the server.

[2098] The terminal formats the user's response data and generates a request to send to the server. The request is sent to the server.

[2099] Step 10:

[2100] The server recognizes emotions using an emotion engine.

[2101] The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state, such as "tension" or "joy" based on the tone of voice and the content of the text.

[2102] Step 11:

[2103] The server analyzes the responses and sentiment data.

[2104] The server uses natural language processing (NLP) technology and an emotion engine to analyze the accuracy of responses, reaction time, perceived emotional state, and more.

[2105] Step 12:

[2106] The server assesses the risk of dementia.

[2107] Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, taking into account past conversation data and emotion data to make a comprehensive assessment.

[2108] Step 13:

[2109] The server generates and transmits the evaluation results.

[2110] The server generates the evaluation results and feedback content, which are then sent to the device. The feedback includes advice based on the evaluation results and recommendations for the next brain exercise.

[2111] Step 14:

[2112] The device displays feedback to the user.

[2113] The device displays the feedback received from the server to the user, and can also provide audio notifications.

[2114] Step 15:

[2115] The server stores conversation history and emotion data.

[2116] The server stores the user's conversation history, evaluation results, and emotion data in a database, which organizes the data for use in the next conversation session.

[2117] The above is a specific processing flow of the system of the present invention that combines an emotion engine. This allows users to exercise their minds on a daily basis, objectively and specifically evaluate the state of their cognitive function, and receive more personalized feedback and improvement measures.

[2118] Example 2

[2119] 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."

[2120] Conventional dementia prevention and monitoring tools assess users' cognitive function primarily based on data entry accuracy and reaction time, without taking into account their emotional state. This makes it difficult to understand the user's condition in more detail or provide personalized feedback. This makes it difficult to detect the risk and progression of dementia early and develop appropriate countermeasures.

[2121] 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.

[2122] In this invention, the server includes means for receiving and storing basic information of the user, means for generating and presenting brain teasing questions based on the basic information of the user, and means for recognizing the emotional state of the user from the response data, thereby enabling the provision of personalized feedback that takes the emotional state into consideration.

[2123] "Basic user information" refers to basic attribute information about a user, such as the user's gender, age, and preferences.

[2124] "Means for storing" refers to means for storing received information in a storage system such as a database.

[2125] "Brain teasers" are tasks such as quizzes and questions designed to stimulate and assess the user's cognitive function.

[2126] A "means for generating questions" is an algorithm or system that generates optimal brain-teasing questions based on the user's basic information and past data.

[2127] The "presentation means" is a means for presenting the generated question to the user visually or audibly.

[2128] A "means for receiving a response" is a means for receiving the text or voice input by the user and formatting it into an analyzable format.

[2129] The "means of analysis" refers to the means of analyzing the received response data using natural language processing or an emotion recognition engine.

[2130] A "means for assessing the risk of developing dementia and the progression of the condition" is an algorithm or system that assesses a user's risk of dementia and its progression based on the results of the analysis.

[2131] "Means for providing evaluation results" refers to means for notifying users of the evaluation results visually or audibly.

[2132] "Means for recognizing emotional states" refers to means for identifying emotions from user response data using natural language processing and voice analysis.

[2133] The "means for comprehensively adjusting the evaluation results" is an algorithm or system that performs a comprehensive evaluation including the recognized emotional state and adjusts the final result.

[2134] This invention relates to an AI voice chatbot system that utilizes generative AI combined with an emotion engine to facilitate dementia prevention and progression monitoring. Specific embodiments of the system are described below.

[2135] The system of the present invention is used by users via devices such as smartphones or PCs. The user launches the application and enters basic information such as gender, age, and preferences. The device formats this information and sends it to the server. The server stores the received information in a database and uses it for subsequent processing.

[2136] In this system, when the user starts a conversation, the server generates optimal brain teasing questions based on the user's basic information and past conversation data. The generated questions are sent to the device and presented to the user. The user answers the questions by text input or voice input.

[2137] The device formats the user's response data and sends it to the server. The server analyzes the received response data and uses an emotion engine to recognize the user's emotional state. Based on the data obtained from this analysis, the server evaluates the user's cognitive function. This makes it possible to assess the risk of dementia and monitor its progression.

[2138] The evaluation results are sent from the server to the device, where they are displayed to the user. A voice notification is also possible. The server then stores the user's conversation history, evaluation results, and recognized emotion data in a database for future use in the next session.

[2139] Specific examples

[2140] For example, a 65-year-old woman who enjoys crossword puzzles launches the app and enters her gender, age, and interests. The device formats this information and sends it to the server. After the server stores the information in a database, the user can tap the "Start Conversation" button to begin a conversation.

[2141] The server generates a question based on the user information, such as "Tell me about the crossword puzzle you solved recently," and sends it to the device. The device displays the question to the user and plays it back aloud. When the user answers, "The crossword puzzle I solved recently was on the theme of 'zoo,'" the device formats the answer and sends it to the server.

[2142] The server analyzes the response data and uses an emotion engine to recognize the user's emotion. For example, it recognizes "enjoyment." The server then evaluates cognitive function based on the analysis results, generates feedback based on the evaluation results, and sends it to the device. The device then displays feedback to the user saying, "Cognitive function is good. Let's continue next time."

[2143] In this way, the present invention, which combines an emotion engine, allows users to easily perform daily mental exercises and evaluate the state of their cognitive function in more detail. By taking emotion into account, more personalized feedback and improvement measures can be provided, contributing to the prevention and early detection of dementia.

[2144] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2145] Step 1:

[2146] The user enters basic information

[2147] Input: The user enters basic information such as gender, age, and preferences into an application on a smartphone or PC.

[2148] What it does: The user fills out a form on the app's start screen, filling out their information, such as being a 65-year-old woman who likes crossword puzzles.

[2149] Output: The input basic information is prepared as data for further processing.

[2150] Step 2:

[2151] The device sends the information to the server

[2152] Input: Basic information entered by the user.

[2153] What happens: The device takes the input information, formats it into JSON, and sends the formatted data to the server over HTTPS.

[2154] Output: Formatted basic information data received by the server.

[2155] Step 3:

[2156] The server stores basic information in a database

[2157] Input: Basic information data sent from the device.

[2158] Specific operation: The server stores the received basic information in a database, for example, a NoSQL database.

[2159] Output: User basic information stored in the database.

[2160] Step 4:

[2161] A user starts a conversation

[2162] Input: The action a user takes to start a conversation, such as tapping a button.

[2163] Specific operation: The user taps the "Start conversation" button on the app screen, which causes the device to send a conversation start request to the server.

[2164] Output: A conversation initiation request sent to the server.

[2165] Step 5:

[2166] The server chooses the best brain teaser

[2167] Input: User basic information and past conversation data.

[2168] How it works: The server uses a generative AI model to select the most suitable brain exercise for a user based on their basic information and past conversation data. For example, if a 65-year-old woman who likes crossword puzzles is selected, the server will select a crossword-related quiz.

[2169] Output: Optimal brain teaser question data.

[2170] Step 6:

[2171] The server sends a question to the device

[2172] Input: Selected brain teaser question data.

[2173] Specific operation: The server sends the generated question data to the terminal, which then displays the received question to the user and plays it back aloud.

[2174] Output: The question that is presented to the user.

[2175] Step 7:

[2176] The user answers the question

[2177] Input: The question posed by the server.

[2178] What happens: The user answers the question by typing or speaking, for example, "The last crossword puzzle I solved was about the zoo."

[2179] Output: User response data.

[2180] Step 8:

[2181] The device sends the answer to the server

[2182] Input: User response data.

[2183] Specific operation: The device receives the user's response data, formats it into JSON format again, and sends this formatted data to the server using HTTPS.

[2184] Output: The formatted response data received by the server.

[2185] Step 9:

[2186] The server recognizes emotions using an emotion engine

[2187] Input: Response data sent from the device.

[2188] Specific operation: The server analyzes the received response data and uses the emotion engine to recognize the user's emotional state, for example, identifying emotions such as "joy" or "tension."

[2189] Output: Recognized emotion data.

[2190] Step 10:

[2191] The server analyzes the responses and emotional data

[2192] Input: User response data and sentiment data.

[2193] How it works: The server uses natural language processing technology to analyze the content of the answers, and simultaneously processes the response time and emotional state, thereby evaluating the accuracy of the answers, response time, and emotional state.

[2194] Output: User's cognitive function assessment data.

[2195] Step 11:

[2196] The server assesses the risk of dementia

[2197] Input: User's cognitive assessment data.

[2198] Specific operation: Based on the analysis results, the server runs an algorithm to assess the user's dementia risk and progression, making a comprehensive assessment based on past data as well.

[2199] Output: Dementia risk assessment results.

[2200] Step 12:

[2201] The server generates and sends the evaluation results

[2202] Input: Dementia risk assessment results.

[2203] Specific operation: The server generates feedback on the cognitive function assessment results and recommended future brain exercises and sends them to the device.

[2204] Output: Feedback data sent to the device.

[2205] Step 13:

[2206] The device displays feedback to the user

[2207] Input: Feedback data sent by the server.

[2208] Specific operation: The device receives the feedback data and notifies the user by text or voice, so that the user receives the feedback.

[2209] Output: The feedback that is presented to the user.

[2210] Step 14:

[2211] The server stores conversation history and emotion data

[2212] Input: User conversation history, rating results, and emotion data.

[2213] What happens: The server stores this data in a database and keeps it available for the next session.

[2214] Output: User history and emotion data stored in a database.

[2215] In this way, the system of the present invention comprehensively analyzes the user's basic information, response data, and emotional state, and provides a detailed assessment of cognitive function and personalized feedback, thereby enabling the prevention of dementia risk and early detection.

[2216] (Application example 2)

[2217] 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."

[2218] Conventional dementia prevention systems simply provide mental exercises without considering the user's emotional state. This results in a lack of feedback that reflects the user's emotional changes, reducing the accuracy of long-term dementia prevention and progress monitoring. It is necessary to provide dementia prevention training for the elderly as a digital assistant in brick-and-mortar stores, aiming to provide detailed support tailored to each individual user.

[2219] The identification process by the identification 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 receiving and storing basic information about the user, means for generating and presenting brain teasing questions based on the user's basic information, means for receiving and analyzing the user's answers, means for analyzing the user's emotions using an emotion recognition engine, and means for generating feedback according to data acquired based on the user's emotional state. This makes it possible to provide dementia prevention measures according to the individual emotional state of the user.

[2220] A "user" is someone who uses the system to exercise their mind to prevent dementia.

[2221] "Basic information" refers to personal data such as gender, age, and preferences provided by the user.

[2222] "Means for storing" refers to functions or devices that allow users' basic information to be saved and reused.

[2223] "Generating means" refers to algorithms or devices that generate appropriate brain teasers or questions based on basic information about the user.

[2224] "Presentation means" refers to the function or device for displaying the generated brain teasers or questions to the user.

[2225] "Means for analysis" refers to the algorithms or devices used to evaluate user responses and analyze their content and accuracy.

[2226] An "emotion recognition engine" is an artificial intelligence technology or software that analyzes a user's voice or text and recognizes their emotional state.

[2227] "Means for generating feedback" refers to algorithms or devices that provide appropriate responses or advice to users based on analysis results and emotional data.

[2228] "Emotional state" refers to a state that indicates a user's changes in feelings or mood.

[2229] "Dementia risk assessment" refers to algorithms and devices used to assess a user's risk of developing dementia and the progression of the condition.

[2230] "Means for providing assessment results" refers to functions and devices for notifying users of the assessed dementia risk and progression.

[2231]

[2232] This invention builds a system that acts as a digital assistant in brick-and-mortar stores, providing brain exercise sessions aimed at preventing dementia, particularly for the elderly. The system operates as a smartphone application and provides individualized feedback based on the user's basic information and emotional recognition, enabling more effective dementia prevention and progression monitoring.

[2233] System configuration

[2234] The system consists of the following components:

[2235] User device: Smartphone or tablet used by the user

[2236] Server: A remote server that processes and analyzes data

[2237] Database: A database that stores basic information about users, past conversation data, and emotional data.

[2238] What the program does

[2239] When a user uses the app for the first time, they enter basic information such as gender, age, and interests on their device. The device sends this information to the server, which then stores it in a database.

[2240] When you tap the "Start Conversation" button in the application, your device sends a conversation start request to the server, which then generates the optimal brain teaser based on the user's basic information and past conversation data. For example, if the user is 65 years old and enjoys crossword puzzles, the server will generate a question such as "Tell me about the crossword puzzle you solved recently."

[2241] The generated question is sent to the device and presented to the user. When the user answers, the answer data is sent to the server as text or voice. The server uses natural language processing technology and an emotion engine to analyze the answer and recognize the user's emotional state. For example, if the answer sounds fun, the emotion of "fun" is recognized.

[2242] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data, and generates feedback based on the results. The feedback is sent to the device and displayed to the user. For example, a positive message such as "Your cognitive function is good. Let's continue next time" is provided.

[2243] Technology used

[2244] Emotion Recognition Engine: Uses generative AI models and natural language processing techniques to recognize emotional states. Examples include the spacy and transformers libraries.

[2245] Database: A database management system for storing user basic information and past conversation data. For example, an SQL or NoSQL database.

[2246] Server technology: Back-end servers for data processing and analysis. Examples include Python-based servers and cloud services.

[2247] Specific examples

[2248] Example 1:

[2249] The user launches the app and enters their gender (female), age (70), and preference (puzzles). The app begins asking questions such as, "Tell me about the puzzle you solved recently." The user answers, "I solved a number puzzle recently," and the answer is sent to the server. The emotion engine recognizes "fun," and provides feedback such as, "Cognitive function is good. Please continue to enjoy the app."

[2250] Example 2:

[2251] prompt:

[2252] User ID = "id1"

[2253] Question = Start conversation session (user ID)

[2254] Brain teaser presentation (user ID, question)

[2255] Answer = "The puzzle I solved recently was about flowers."

[2256] Emotion result = Answer and Emotion Recognition(User ID, Answer)

[2257] Evaluation result = Cognitive function evaluation (user ID)

[2258] print(f"Feedback: {Evaluation result}")

[2259]

[2260] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2261]

[2262] Step 1:

[2263] The user starts the application on the device and enters basic information (gender, age, interests).

[2264] Input: The user enters basic information such as gender, age, and preferences.

[2265] Data processing: The terminal formats the basic information entered and sends it to the server.

[2266] Output: Basic formatted information is sent to the server.

[2267] Step 2:

[2268] The server stores the basic information in a database.

[2269] Input: Basic information sent from the device.

[2270] Data processing: The server records the basic information received in a database.

[2271] Output: Basic information stored in the database.

[2272] Step 3:

[2273] The user taps the Start Conversation button.

[2274] Input: The user taps the "Start Conversation" button on their device.

[2275] Data processing: The terminal sends a conversation start request to the server.

[2276] Output: A conversation initiation request is sent to the server.

[2277] Step 4:

[2278] The server generates optimal brain exercises based on the user's basic information and past conversation data.

[2279] Input: Basic information about the user and past conversation data.

[2280] Data processing: The server uses the generated AI model to generate the optimal brain teaser for the user.

[2281] Output: The generated brain teaser.

[2282] Step 5:

[2283] The server sends the generated brain teasers to the terminal, which then presents them to the user.

[2284] Input: A server-generated brain teaser.

[2285] Data processing: The device formats the brain teasers received from the server for display.

[2286] Output: The brain teaser displayed on the terminal.

[2287] Step 6:

[2288] The user answers the questions presented.

[2289] Input: The user inputs the answer into the terminal by voice or text.

[2290] Data processing: The terminal formats the entered answers and sends them to the server.

[2291] Output: The formatted response data is sent to the server.

[2292] Step 7:

[2293] The server analyzes the response data and recognizes the user's emotions using an emotion recognition engine.

[2294] Input: Response data sent from the device.

[2295] Data processing: The server analyzes the responses using natural language processing technology and an emotion recognition engine to recognize the emotional state.

[2296] Output: Analysis results and emotion data.

[2297] Step 8:

[2298] The server performs a comprehensive dementia risk assessment based on the analysis results and emotional data.

[2299] Input: Analysis results and emotion data.

[2300] Data processing: The server evaluates the dementia risk using an evaluation algorithm.

[2301] Output: Dementia risk assessment results.

[2302] Step 9:

[2303] The server generates feedback based on the evaluation results and sends it to the device.

[2304] Input: Dementia risk assessment results.

[2305] Data processing: The server generates feedback based on the evaluation results.

[2306] Output: The generated feedback is sent to the device.

[2307] Step 10:

[2308] The device displays the feedback to the user.

[2309] Input: Feedback sent by the server.

[2310] Data processing: The device formats the feedback for display.

[2311] Output: Feedback displayed on the device.

[2312] 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.

[2313] 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.

[2314] 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.

[2315] 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.

[2316] 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.

[2317] 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.

[2318] 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).

[2319] 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.

[2320] 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."

[2321] 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.

[2322] 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).

[2323] 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.

[2324] 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.

[2325] 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.

[2326] 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.

[2327] 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.

[2328] 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.

[2329] 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.

[2330] 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.

[2331] 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.

[2332] 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.

[2333] The following is further disclosed regarding the above embodiment.

[2334] (Claim 1)

[2335] means for receiving and storing basic information about a user;

[2336] A means for generating and presenting brain teasers based on basic information of a user;

[2337] a means for receiving and analyzing user responses;

[2338] A method for assessing the risk of developing dementia and the progression of the condition based on the analysis results,

[2339] a means for providing the evaluation results to users;

[2340] A system including:

[2341] (Claim 2)

[2342] The system of claim 1, which refers to the user's past conversation data and performs a comprehensive dementia risk assessment.

[2343] (Claim 3)

[2344] 2. The system of claim 1, wherein the content of the brain teaser changes randomly or dynamically according to the user's preferences.

[2345] "Example 1"

[2346] (Claim 1)

[2347] means for receiving and storing basic information about a user;

[2348] A means of generating brain teasing questions based on basic user information using a generative AI model; and

[2349] means for presenting the generated question to a user;

[2350] means for receiving the user's response and transmitting it to a server;

[2351] A means for analyzing the response received by the server using natural language processing technology;

[2352] A method for assessing the risk of developing dementia and the progression of the condition based on the analysis results,

[2353] a means for providing the evaluation results to users;

[2354] A system including:

[2355] (Claim 2)

[2356] The system of claim 1, which refers to the user's past conversation data and performs a comprehensive dementia risk assessment.

[2357] (Claim 3)

[2358] The system of claim 1 uses a generative AI model to dynamically change the content of the brain teasers according to the user's preferences or randomly.

[2359] "Application Example 1"

[2360] (Claim 1)

[2361] means for receiving and storing basic information about a user;

[2362] A means for generating and presenting brain teasers based on basic information of a user;

[2363] a means for receiving and analyzing user responses;

[2364] A method for assessing the risk of developing dementia and the progression of the condition based on the analysis results,

[2365] a means for providing the evaluation results to users;

[2366] A means for customers to check their cognitive function when they visit a physical store;

[2367] A system including:

[2368] (Claim 2)

[2369] The system of claim 1, which refers to the user's past conversation data and performs a comprehensive dementia risk assessment.

[2370] (Claim 3)

[2371] 2. The system of claim 1, wherein the content of the brain teaser changes randomly or dynamically according to the user's preferences.

[2372] "Example 2: Combining Emotion Engines"

[2373] (Claim 1)

[2374] means for receiving and storing basic information about a user;

[2375] A means for generating and presenting brain teasers based on basic information of a user;

[2376] a means for receiving and analyzing user responses;

[2377] A method for assessing the risk of developing dementia and the progression of the condition based on the analysis results,

[2378] a means for providing the evaluation results to users;

[2379] A means for recognizing an emotional state from the user's response data;

[2380] a means for adjusting the overall evaluation results based on the perceived emotional state;

[2381] A system including:

[2382] (Claim 2)

[2383] The system of claim 1, which refers to the user's past conversation data and performs a comprehensive dementia risk assessment.

[2384] (Claim 3)

[2385] 2. The system of claim 1, wherein the content of the brain teaser changes randomly or dynamically according to the user's preferences.

[2386] "Application example 2 when combining emotion engines"

[2387] (Claim 1)

[2388] means for receiving and storing basic information about a user;

[2389] A means for generating and presenting brain teasers based on basic information of a user;

[2390] a means for receiving and analyzing user responses;

[2391] A method for assessing the risk of developing dementia and the progression of the condition based on the analysis results,

[2392] a means for providing the evaluation results to users;

[2393] A means for analyzing a user's emotions using an emotion recognition engine;

[2394] means for generating feedback according to the acquired data based on the user's emotional state;

[2395] A system including:

[2396] (Claim 2)

[2397] The system of claim 1, which refers to the user's past conversation data and emotion data to perform a comprehensive dementia risk assessment.

[2398] (Claim 3)

[2399] 10. The system of claim 1, wherein the content of the brain teaser changes randomly or dynamically according to the user's preferences, and further adjusts based on the user's emotional state. [Explanation of symbols]

[2400] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving and storing basic information about a user; A means for generating and presenting brain teasers based on basic information of a user; a means for receiving and analyzing user responses; A method for assessing the risk of developing dementia and the progression of the condition based on the analysis results, a means for providing the evaluation results to users; A system including:

2. The system according to claim 1, which refers to the user's past conversation data and performs a comprehensive dementia risk assessment.

3. 2. The system according to claim 1, wherein the content of the brain exercises is changed randomly or dynamically according to the user's preferences.

Images