System

Abstract

A system is provided.SOLUTION: A system includes means for inputting basic information of a patient whose cognitive decline is targeted, means for generating a personalized cognitive training task using artificial intelligence based on the basic information of the patient, means for presenting the generated cognitive training task to the patient, means for collecting answers or reactions of the patient, means for analyzing the collected answers or reactions and generating feedback for adjusting training contents of the next time, and means for presenting the generated feedback to the patient.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] Appropriate cognitive training is essential to slow the decline in memory and cognitive function in dementia patients. However, it can be extremely difficult and stressful for caregivers to find and implement training methods tailored to each individual patient. Furthermore, if appropriate training is not provided, the progression of dementia may be accelerated. Given this background, there is a need for a system that can provide individually optimized cognitive training tasks to maintain and improve the memory and cognitive function of dementia patients. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means.

[0006] A means for inputting basic patient information is provided, and input data such as the patient's age, interests, and past experiences is collected. Next, a means is provided using artificial intelligence to generate personalized cognitive training tasks based on the collected basic information. This artificial intelligence uses natural language processing and image recognition technology to create training content appropriate for the patient. The generated cognitive training tasks are presented to the patient via a terminal. The system also has a means for collecting the patient's answers and reactions to the tasks they have completed, and a means for analyzing this information to generate feedback to adjust the next training content. The generated feedback is again presented to the patient via a terminal. This series of processes results in a system that continuously provides individually optimized cognitive training to help maintain and improve patients' memory and cognitive function.

[0007] "Basic information" refers to individual data such as the dementia patient's age, name, stage of dementia, past experiences, and preferences.

[0008] "Artificial intelligence" refers to technology that automatically analyzes data and generates personalized cognitive training tasks based on the results.

[0009] "Cognitive training tasks" refer to tasks or activities provided to patients with the goal of maintaining or improving memory or cognitive function.

[0010] "Terminal" refers to a device (e.g., smartphone or tablet) used by dementia patients or caregivers, which is used to present tasks and record user responses.

[0011] "Natural language processing" refers to a technology that, as part of artificial intelligence, analyzes input text data and outputs it in a form that humans can understand.

[0012] "Image recognition" refers to the technology, as part of artificial intelligence, that analyzes input image data and recognizes specific patterns or objects.

[0013] "Personalization" refers to the process of generating optimized training tasks and feedback based on a patient's individual information.

[0014] "Feedback" refers to information generated based on collected user reactions and response data that is useful for adjusting and improving future training tasks.

[0015] "Collection" refers to the process of recording the reactions and answers of dementia patients to the tasks they undertake and accumulating them as data.

[0016] "Analysis" refers to the process of evaluating the collected data in detail to identify the effectiveness of training tasks and areas for improvement. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline. This system is easy to use for caregivers and family members and supports the maintenance and improvement of patients' cognitive function.

[0039] System Overview

[0040] Server: The core processing unit of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on basic user information.

[0041] Terminal: Serves as the user interface, collecting information from the dementia patient and caregiver, displaying training tasks, and recording user responses.

[0042] Users: People with dementia and their caregivers use the system and complete the training tasks.

[0043] Program processing

[0044] 1. Initial setup and user information entry

[0045] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences), allowing the system to collect detailed data related to the patient. The input information is then sent to the server.

[0046] 2. Storage of User Information

[0047] Server: Stores the received user information in a database. This allows patient information to be captured and used as the basis for generating future training tasks.

[0048] 3. Generation of cognitive training tasks

[0049] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information.

[0050] Server: Uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information. The generated tasks are sent to the device and presented to the user.

[0051] 4. Present and execute the task

[0052] Device: The device presents the generated training tasks to the dementia patient. For example, it displays quizzes and tasks that prompt recollection related to the patient's favorite hobbies or past work history when they were younger.

[0053] User: Works through the presented tasks and enters answers and responses.

[0054] 5. Collecting user responses

[0055] Terminal: Records the patient's answers and responses to the tasks and sends them to the server.

[0056] 6. Generate feedback

[0057] Server: Analyzes collected user responses and generates feedback to adjust the next training content. Natural language processing technology is used for the analysis, and appropriate interpretations are given to each response.

[0058] Server: Sends the generated feedback to the device.

[0059] 7. Viewing Feedback

[0060] Device: The generated feedback is displayed to the patient and reflected in the next training assignment.

[0061] Specific examples

[0062] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0063] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0064] The patient works on the task displayed on the device, recalling and entering the student's name and anecdote. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time." This feedback is displayed on the device and is used to improve the next training task.

[0065] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and also contributes to reducing the burden on caregivers.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, progression of dementia, preferences, past experiences, etc.). The input information is confirmed and sent to the server as a request.

[0069] Step 2:

[0070] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[0071] Step 3:

[0072] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[0073] Step 4:

[0074] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0075] Step 5:

[0076] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0077] Step 6:

[0078] User: Engages in cognitive training tasks, such as answering quizzes or recalling familiar places or events.

[0079] Step 7:

[0080] Terminal: Records the answers and responses entered by the patient to the tasks. The recorded data is sent to the server.

[0081] Step 8:

[0082] Server: Analyzes the user's responses and reactions using artificial intelligence technology to generate feedback to adjust the next training session based on the patient's performance and reactions.

[0083] Step 9:

[0084] Server: Sends generated feedback to the device, which evaluates the patient's effort and includes specific advice for improving the next training session.

[0085] Step 10:

[0086] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0087] Example 1

[0088] 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."

[0089] Providing individually optimized cognitive training tasks for patients with declining cognitive function requires a method for generating tasks that correspond to each patient's unique background information and progress. It is also important that the system be easy to use for caregivers and patients themselves. However, existing systems cannot meet these requirements, making it difficult to effectively support patients in maintaining and improving their cognitive function. This issue must be resolved.

[0090] 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.

[0091] In this invention, the server includes means for providing a user interface through which a caregiver inputs basic information about a patient, means for storing the input basic information in a database, means for generating personalized cognitive training tasks based on the patient's basic information using artificial intelligence, means for presenting the generated training tasks to the patient, means for collecting answers and responses from the patient, means for analyzing the collected answers and responses and generating feedback for adjusting the content of the next training, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and to generate and present feedback that continuously improves effectiveness.

[0092] The "user interface" is the part of the system that includes the screens and instructions that allow caregivers to enter basic patient information.

[0093] "Basic information" refers to data necessary for individual optimization of cognitive training tasks, such as the patient's name, age, stage of dementia, preferences, and past experiences.

[0094] "Database" refers to an information management device within the system that stores and manages input basic information and collected answers and responses.

[0095] "Artificial intelligence" is a technology that uses natural language processing and image recognition technology to analyze data and make decisions and generate problems that are closer to those of humans.

[0096] "Cognitive training tasks" refer to specific training content generated by artificial intelligence with the aim of maintaining and improving a patient's cognitive function.

[0097] "Feedback" refers to information and evaluations that are used to adjust the content of the next training based on the results of the patient's cognitive training tasks.

[0098] "Personalized" means having a format and content optimized for a specific patient based on the patient's individual basic information.

[0099] "Answer or response" refers to the response results, such as input, behavior, and evaluation, that a patient makes in response to the presented cognitive training task.

[0100] "Means for collecting" refers to a method or device for recording the patient's answers and responses through the user interface and storing them in a database.

[0101] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline, and is easy for caregivers and family members to use, helping to maintain and improve patients' cognitive function. The system consists of three elements: a server, a terminal, and a user.

[0102] Server: Performs the central processing of the system. The server stores user information in a database and uses artificial intelligence (generative AI models) to generate personalized cognitive training tasks based on the user's basic information. It then analyzes the collected user responses and generates feedback to adjust the next training content based on that information. Specifically, the server uses the following hardware and software: For hardware, it uses, for example, AWS (registered trademark) (Amazon Web Services) or Google (registered trademark) Cloud Platform. For software, it uses MySQL (registered trademark) or PostgreSQL for database management, and OpenAI (registered trademark) GPT-4 (registered trademark) or Hugging Face Transformers for the artificial intelligence model.

[0103] Terminal: A terminal that functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, and recording user responses. Examples of terminals that can be used include iPads (registered trademarks) and Android tablets. Specifically, the terminal communicates with the server, inputs and collects necessary data via the user interface, and displays training tasks. Data entered into the terminal is sent to the server in real time, where it is stored and analyzed.

[0104] Users: Dementia patients and their caregivers use the system. Users access the system through a terminal and work on training tasks. The users (patients) input their responses to the tasks displayed on the terminal, and their responses are recorded on the terminal and sent to the server.

[0105] Specific examples

[0106] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server generates a cognitive training task by inputting the following prompt sentences into a generative AI model:

[0107] "Generate cognitive training tasks for a 60-year-old patient with early-stage dementia based on his gardening hobby and past experience as a teacher."

[0108] The task generated by the AI ​​based on this prompt is, "Please remember the names of the students you have taught. Also, please think back on their personalities and memorable moments." The patient works on this task, recalling and inputting the names of the students and stories they shared. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time," which is displayed on the device.

[0109] This system will help maintain and improve the memory and cognitive functions of dementia patients, and also contribute to reducing the burden on caregivers.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] Terminal: The caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences) through the terminal's user interface. This information is entered in a form and sent to the server in one go by pressing the "Send" button.

[0113] * Input: Patient's basic information

[0114] *Output: Basic information data sent to the server

[0115] Step 2:

[0116] Server: Stores the received basic information in a database using an SQL query, where data corresponding to each field is saved in the appropriate table (e.g., "INSERT INTO users (name, age, condition, preferences) VALUES (...)").

[0117] * Input: Basic information data sent from the device

[0118] * Output: Patient information stored in the database

[0119] Step 3:

[0120] Terminal: The caregiver requests the server to generate a cognitive training task through the terminal. The user interface has a "Generate Task" button, and clicking this button sends the request to the server.

[0121] * Input: Issue creation request

[0122] *Output: Request data is sent to the server

[0123] Step 4:

[0124] Server: Upon receiving the request, the server uses an artificial intelligence model (e.g., a generative AI model) to generate a personalized cognitive training task based on the basic information. It generates a prompt (e.g., "Please generate a cognitive training task for a 60-year-old person with early-stage dementia based on his gardening hobby and past experience as a teacher") and inputs this prompt into the AI ​​model to generate the task. The generated task is then sent to the device via the server's API.

[0125] * Input: Basic information, prompt text

[0126] * Output: Generated training tasks

[0127] Step 5:

[0128] Device: Generated training tasks are displayed on the device. The user interface dashboard lists new tasks and provides them in a format for the patient to complete (e.g., quiz or open-ended).

[0129] * Input: Assignment data sent from the server

[0130] * Output: Assignment displayed on the device

[0131] Step 6:

[0132] User: The patient works on tasks displayed on the device and inputs their answers. For example, to the task, "Remember the names of the students you have taught and reflect on their personalities and memorable moments," the patient uses the device's keyboard to input their answers.

[0133] * Input: Patient's response

[0134] * Output: Answer data recorded on the device

[0135] Step 7:

[0136] Terminal: The patient's answers and reactions to the tasks are recorded on the terminal, temporarily saved, and then sent to the server.

[0137] * Input: Patient response data

[0138] *Output: Reaction data sent to the server

[0139] Step 8:

[0140] Server: The received response data is stored in a database (e.g., "INSERT INTO responses (user_id, response) VALUES (...)"). The collected data is then analyzed and feedback is generated to adjust the next training content. At this time, the response data and a supplementary prompt (e.g., "Please rate this response and suggest the next training content") are input to the AI ​​model. The generated feedback is then sent back to the device via the API.

[0141] * Input: Patient response data, supplementary prompt text

[0142] * Output: Generated feedback

[0143] Step 9:

[0144] Device: The generated feedback is displayed to the patient. The feedback message is displayed on the dashboard of the user interface and is reflected in the next training challenge. For example, a positive message such as "Great! Let's try a more difficult challenge next time" is displayed.

[0145] * Input: Feedback sent from the server

[0146] * Output: Feedback displayed on the device

[0147] (Application example 1)

[0148] 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."

[0149] Conventionally, cognitive training for patients with cognitive decline has often not been individually optimized, making effective training difficult. In particular, there has been a lack of systems for dementia prevention in brick-and-mortar stores, resulting in a lack of environments where patients can easily train in their daily lives. Furthermore, it has been difficult to generate personalized tasks based on the patient's past experiences and preferences, making it difficult to maintain motivation.

[0150] 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.

[0151] In this invention, the server includes: means for inputting basic information of a patient suffering from cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and responses from the patient; means for analyzing the collected answers and responses and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; and means for providing the patient with personalized cognitive training tasks via a terminal installed in a physical store and recording the patient's responses. This enables patients suffering from cognitive decline to engage in individually optimized cognitive training tasks even in a physical store, providing an environment where they can easily train in their daily lives.

[0152] "Cognitive decline" refers to a decline in cognitive abilities such as memory, attention, and comprehension.

[0153] "Basic patient information" refers to information such as the name, age, progression of dementia, hobbies, and past experiences of patients with cognitive decline.

[0154] "Artificial intelligence" refers to technology that mimics human intelligence on a computer, independently learning from past data to solve problems.

[0155] "Personalized cognitive training tasks" refer to tasks designed to improve cognitive function that are optimized for a specific patient based on basic information about the individual patient.

[0156] "Generating means" refers to the process or device that uses artificial intelligence to create specific tasks or content.

[0157] "Presenting means" refers to the process or device for displaying or communicating the generated tasks and feedback to the patient.

[0158] "Patient response" refers to the responses or behaviors that a patient exhibits in response to cognitive training tasks.

[0159] "Means of collection" refers to the process or device used to record and store patient responses.

[0160] "Feedback" refers to providing information to adjust the next training session based on the collected data.

[0161] "Brick and mortar store" means a physical retail establishment that is open to the public.

[0162] "Terminal" refers to an electronic device or computer used to input or output information.

[0163] This invention is a system for providing personalized cognitive training exercises to patients with cognitive decline. This system utilizes artificial intelligence to generate cognitive training exercises optimized for each patient based on basic information about the patient, and provides them via a terminal installed in a physical store. The configuration and operation of this system are described in detail below.

[0164] server

[0165] The server is responsible for the core functions of this system, mainly fulfilling the following roles:

[0166] Save basic information

[0167] The server stores basic information about the patient (such as name, age, hobbies, and past experiences) received through the terminal in a database. This data is used to generate future training tasks.

[0168] Generation of cognitive training tasks

[0169] The server generates personalized cognitive training tasks using a generative AI model based on the stored basic patient information. The generative AI model is equipped with natural language processing and image recognition technologies, and is capable of generating appropriate tasks based on the input information.

[0170] Terminal

[0171] The terminal is a device installed in a physical store that provides an interface between the patient and the system. Its main functions are as follows:

[0172] Presentation of cognitive training tasks

[0173] The device presents the cognitive training tasks sent from the server to the patient, for example, using a tablet or dedicated smart display.

[0174] Collecting patient responses

[0175] The device records the answers and responses the patient gives to the training tasks and sends them to a server, where they are used to generate feedback.

[0176] feedback

[0177] Generate feedback

[0178] The server analyzes the collected responses from the patient and generates feedback to adjust the content of the next training session, which is then presented to the patient via their device.

[0179] Providing feedback

[0180] The terminal displays the feedback sent from the server to the patient, which provides guidance for the next training session.

[0181] Specific examples

[0182] For example, a 70-year-old patient might use a terminal in a brick-and-mortar store and enter the following information:

[0183] Name: Taro

[0184] Age: 70

[0185] Hobbies: Gardening

[0186] Previous experience: Teacher

[0187] This information is sent to a server, which uses a generative AI model to generate challenges like this:

[0188] Think back to places you've traveled to in the past.

[0189] Think back to the names of your friends from school.

[0190] Think back to the title of your favorite movie.

[0191] The device presents the task to the patient, and when the patient enters their answer, the response is sent back to the server, which analyzes it and generates feedback such as, "In your next training session, try a more detailed task based on your past experiences," which is displayed on the device.

[0192] This series of processes allows patients to continuously engage in personalized cognitive training tasks, which is expected to maintain and improve cognitive function.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] The device accepts basic information from the user. This information includes name, age, hobbies, past experiences, etc. The device then sends this input data to the server. The input data is in JSON format, for example. Specifically, the user enters information into a form on the tablet and presses the send button, which sends the data to the server.

[0196] Step 2:

[0197] The server saves the received basic information in the database. In this step, the data is formatted to store the received data in the database in the appropriate format. Specifically, the server connects to the database and saves the received data using an INSERT statement.

[0198] Step 3:

[0199] The device requests the server to generate cognitive training tasks. The server uses the generative AI model to generate personalized cognitive training tasks based on the stored basic information. This uses natural language processing and image recognition technologies. The generated tasks are sent to the device in JSON format. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves the generated tasks.

[0200] Step 4:

[0201] The device presents the cognitive training tasks received from the server to the user. Specifically, the tasks are displayed on the tablet's display. The user then works on the presented tasks and inputs their answers.

[0202] Step 5:

[0203] The device collects the answers and reactions entered by the user and sends them to the server. At this time, the user's answer data is sent in JSON format. Specifically, the device collects the data entered in the input form and sends it to the server when the send button is pressed.

[0204] Step 6:

[0205] The server analyzes the collected answers and reactions and generates feedback to adjust the next training content. This uses natural language processing technology. The generated feedback is sent to the device in JSON format. Specifically, the server analyzes the answer data, generates appropriate feedback, and sends it.

[0206] Step 7:

[0207] The device then presents the feedback received from the server to the user, allowing the user to work on the next assignment based on this feedback. Specifically, the feedback message is displayed on the tablet screen.

[0208] 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.

[0209] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and further combines it with an emotion engine that recognizes the user's emotions. This system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[0210] System Overview

[0211] Server: This performs the core processing of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on the user's basic information. It also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[0212] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[0213] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[0214] Program processing

[0215] 1. Initial setup and user information entry

[0216] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.). The terminal confirms the input information and sends it as a request to the server.

[0217] 2. Storage of User Information

[0218] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[0219] 3. Generation of cognitive training tasks

[0220] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[0221] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0222] 4. Present and execute the task

[0223] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0224] User: Works on the tasks presented to them. For example, they train by answering quizzes or recalling nostalgic places and events.

[0225] 5. Emotional state analysis using the emotion engine

[0226] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends it to the server.

[0227] Server: Analyzes the collected data using an emotion engine and recognizes the patient's emotional state (e.g., joy, sadness, excitement, etc.).

[0228] 6. Collecting user reaction and sentiment data

[0229] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[0230] 7. Generate feedback

[0231] Server: Analyzes the user's responses, reactions, and emotional state and generates feedback to adjust the next training content. The feedback includes appropriate advice based on the patient's cognitive and emotional state using artificial intelligence and emotion analysis technologies.

[0232] Server: Sends the generated feedback to the device.

[0233] 8. Viewing Feedback

[0234] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0235] Specific examples

[0236] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0237] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0238] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[0239] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[0240] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.), allowing the system to collect detailed data related to the patient. The input information is verified and sent as a request to the server.

[0244] Step 2:

[0245] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal, thereby informing the caregiver that the information has been saved successfully.

[0246] Step 3:

[0247] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. This request includes the patient's basic information.

[0248] Step 4:

[0249] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0250] Step 5:

[0251] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0252] Step 6:

[0253] User: Works on presented cognitive training tasks, such as recalling a student's name or answering questions about past events.

[0254] Step 7:

[0255] Device: While the patient is working on the task, it collects voice and facial expression data and sends them to the server. This data is used to analyze the patient's emotional state.

[0256] Step 8:

[0257] Server: Analyzes collected voice and facial expression data using an emotion engine to recognize the patient's emotional state (e.g., joy, sadness, excitement, etc.). Stores the recognition results for further processing.

[0258] Step 9:

[0259] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[0260] Step 10:

[0261] Server: Analyzes the user's responses, reactions, and emotional state using artificial intelligence and emotion analysis technology, and generates feedback to adjust the next training content based on the patient's cognitive and emotional state.

[0262] Step 11:

[0263] Server: Sends the generated feedback to the device. The feedback includes advice based on the cognitive training content and emotional state.

[0264] Step 12:

[0265] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0266] Specific examples

[0267] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0268] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0269] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[0270] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[0271] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[0272] Example 2

[0273] 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."

[0274] It has been difficult with current technologies to provide individually optimized cognitive training tasks for patients with cognitive decline, and to properly understand the patient's emotional state and provide feedback based on that. Furthermore, a system that can be easily used by caregivers and family members is needed.

[0275] 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.

[0276] In this invention, the server includes means for inputting basic information of a patient with cognitive decline, means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information, means for presenting the generated cognitive training tasks to the patient, means for collecting the patient's answers, reactions, voice data, and facial expression data, means for analyzing the collected answers, reactions, voice data, and facial expression data to recognize the patient's emotional state, means for adjusting the next training content based on the analysis results and generating feedback, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and feedback based on the patient's emotional state.

[0277] "Basic patient information" refers to the name, age, stage of dementia, preferences, past experiences, etc. of the patient whose cognitive function is declining.

[0278] "Personalized cognitive training tasks" refer to tasks for improving cognitive function that are individually optimized using artificial intelligence based on the patient's basic information.

[0279] "Artificial intelligence" refers to technologies that enable computers to mimic human intelligence, including natural language processing and image recognition techniques.

[0280] "Natural language processing" refers to the technology that enables computers to understand, generate, and respond to human language.

[0281] "Image recognition technology" refers to the technology that allows computers to analyze image and video data and identify patterns and objects.

[0282] "Means for presenting training tasks" refers to a mechanism for displaying cognitive training tasks generated through a terminal to a patient.

[0283] "Means for collecting responses" refers to a mechanism for recording and storing the responses or responses provided by the patient to the training tasks.

[0284] "Audio data" refers to data that records the sounds made when a patient is performing a task.

[0285] "Facial expression data" refers to data recording the facial expressions observed when a patient is performing a task.

[0286] "Means for recognizing emotional state" refers to a mechanism for analyzing collected voice data and facial expression data to identify the patient's emotional state (happiness, sadness, excitement, etc.).

[0287] "Means for generating feedback" refers to a mechanism for adjusting the next training content and providing appropriate advice and guidance based on the patient's answers, reactions, and emotional state.

[0288] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and also combines an emotion engine that recognizes the user's emotions. The system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[0289] System Overview

[0290] Server: This is the core processing part of the system, and uses artificial intelligence to generate personalized cognitive training tasks based on the user's basic information. The server also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[0291] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[0292] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[0293] Program processing

[0294] The system works through the following steps:

[0295] 1. Initial setup and user information entry: Using the terminal, the caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.) and sends the entered information to the server.

[0296] 2. Save user information: The server saves the received user information in the database and sends a success message to the terminal.

[0297] 3. Generation of cognitive training tasks: Based on requests from the device, the server generates personalized cognitive training tasks using artificial intelligence (natural language processing and image recognition technology). The generated tasks are sent from the server to the device.

[0298] 4. Presentation and execution of the task: The device presents the generated cognitive training task to the patient, who then completes the task with the support of a caregiver.

[0299] 5. Analysis of emotional state using emotion engine: The device collects voice and facial expression data while the patient is working on the task and sends it to the server. The server analyzes this data using the emotion engine and recognizes the patient's emotional state.

[0300] 6. Collecting user response and emotional data: The device records the patient's answers and responses, as well as their emotional state analyzed by the emotion engine, and sends them to the server.

[0301] 7. Feedback Generation: The server analyzes the received data and generates feedback to adjust the next training content. The generated feedback is sent to the device.

[0302] 8. Displaying feedback: The device presents the received feedback to the patient and provides guidance for the next training session.

[0303] Hardware and software used

[0304] Hardware: A typical computer, tablet, or smartphone can be used as the device. The device's built-in microphone and camera are used to collect voice and facial expression data.

[0305] Software: The server is built using programming languages ​​such as Python or Java (registered trademark), and the database uses MySQL or PostgreSQL, etc. For artificial intelligence, natural language processing technologies such as TENSORFLOW (registered trademark) and PyTorch are used, and image recognition technologies such as Amazon Rekognition and Google Cloud Vision are used.

[0306] Specific examples

[0307] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. This information is entered into a device and sent to a server. The server uses artificial intelligence to generate the following tasks:

[0308] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0309] The patient completes the tasks displayed on the device and inputs the names of their students and other anecdotes. Voice and facial expression data collected while the patient is working on the tasks is sent to a server, where an emotion engine analyzes this data to recognize the patient's emotional state.

[0310] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and can be used to improve the next training session.

[0311] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[0312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0313] Step 1:

[0314] Initial setup and entering user information

[0315] Terminal: The caregiver enters basic information about the patient (name, age, progression of dementia, preferences, past experiences, etc.) through the terminal's user interface. After confirming the entered information, the caregiver presses the "Send" button.

[0316] Input: Patient basic information.

[0317] Output: The request to the server.

[0318] Specific operation: The terminal checks the entered basic information of the patient and sends it as an HTTP request to the server.

[0319] Step 2:

[0320] Saving user information

[0321] Server: Saves the received user information in the database. If the save is successful, generates a success message stating "User information has been saved successfully" and sends it to the terminal.

[0322] Input: Basic patient information sent from the terminal.

[0323] Output: User information saved in database, success message.

[0324] Specific operation: The server analyzes the received basic information of the patient and inserts it into the database in the appropriate format. If the insertion is successful, it generates a message indicating success and sends it to the terminal.

[0325] Step 3:

[0326] Generation of cognitive training tasks

[0327] Terminal: Requests the server to generate cognitive training tasks based on basic information about a specific patient.

[0328] Input: A request based on basic patient information.

[0329] Output: The request to the server.

[0330] Specific operation: The terminal sends a request to the server to generate cognitive training tasks based on the stored basic information of the patient.

[0331] Server: Uses artificial intelligence to generate personalized cognitive training tasks.

[0332] Input: Patient basic information.

[0333] Output: The generated cognitive training task.

[0334] Specific operation: The server uses natural language processing and image recognition technology to generate cognitive training tasks based on the patient's basic information and sends them to the terminal.

[0335] Step 4:

[0336] Presenting and executing the task

[0337] Terminal: Presents the generated cognitive training tasks to the patient and encourages them to complete the tasks.

[0338] Input: Generated cognitive training tasks.

[0339] Output: The issue displayed in the user interface.

[0340] Specific operation: The terminal displays the cognitive training task received from the server on the screen and asks the patient to complete the task.

[0341] User: Works on the assignment and inputs answers and responses via the device.

[0342] Input: Answers and responses to the assignment.

[0343] Output: Answers or responses typed into the terminal.

[0344] Specific operations: The patient enters answers to the questions that are displayed one after another, and these are recorded on the terminal.

[0345] Step 5:

[0346] Emotional state analysis using emotion engine

[0347] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends them to the server.

[0348] Input: Patient's voice and facial expression data.

[0349] Output: Sending voice data and facial expression data to the server.

[0350] Specific operation: The device's built-in microphone and camera collect voice and facial expression data, which are then sent to the server.

[0351] Server: Analyzes these data using an emotion engine to recognize the patient's emotional state.

[0352] Input: Collected speech and facial expression data.

[0353] Output: Parsed emotional state.

[0354] Specific operation: The server uses voice analysis software and image recognition software to recognize the patient's emotional state (happiness, sadness, excitement, etc.) based on the collected data.

[0355] Step 6:

[0356] Collecting user reaction and sentiment data

[0357] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends these to the server.

[0358] Input: Patient's answers, reactions, and emotional state.

[0359] Output: Data sent to the server.

[0360] Specific operation: The device records the user's answers and reactions and sends them to the server along with pre-analyzed emotional data.

[0361] Step 7:

[0362] Generate feedback

[0363] Server: Analyzes the received answers, reactions, and emotional state data and generates feedback to adjust the next training content.

[0364] Input: User answers, reactions, and sentiment data.

[0365] Output: The generated feedback.

[0366] How it works: The server uses artificial intelligence technology to adjust the next training session based on the collected data and generate appropriate feedback.

[0367] Server: Sends the generated feedback to the device.

[0368] Input: Generated feedback.

[0369] Output: Data sent to the terminal.

[0370] Specific operation: The server sends the generated feedback to the device and reflects it in the next training content.

[0371] Step 8:

[0372] View Feedback

[0373] Terminal: Presents the received feedback to the patient.

[0374] Input: Feedback from the server.

[0375] Output: Feedback displayed in the user interface.

[0376] Specific behavior: The device displays the received feedback on the screen and provides guidance to patients and caregivers for the next training session.

[0377] These steps enable the system to provide individually optimized cognitive training tasks and feedback based on emotional state.

[0378] (Application example 2)

[0379] 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."

[0380] Conventional cognitive training systems do not adequately consider the individual cognitive function and emotional state of dementia patients, making it difficult to provide effective training. Furthermore, when it comes to training for individuals engaged in specific occupations, such as security staff, there is a lack of technology to provide cognitive training tailored to the characteristics of their jobs. This has led to a demand for performance improvement through emotional management as well as cognitive function improvement.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0382] In this invention, the server includes: means for inputting basic information of a patient with cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and reactions from the patient; means for analyzing the collected answers and reactions and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; means for collecting voice data and facial expression data and analyzing them using an emotion engine; and means for adjusting the training content based on the analyzed emotional state. This makes it possible to provide effective training that integrates cognitive function improvement and emotion management to dementia patients and individuals engaged in specific occupations.

[0383] "Cognitive function" is a general term for an individual's intellectual abilities to understand and remember information, solve problems, and make decisions.

[0384] "Basic patient information" refers to information specific to an individual patient, such as name, age, stage of dementia, preferences, and past experiences.

[0385] "Artificial intelligence" is a technology that allows computer systems to mimic human intellectual behavior, analyze complex data, and make decisions, and includes natural language processing and image recognition technologies.

[0386] "Personalized cognitive training tasks" are tasks for improving cognitive function that are optimized for each individual patient based on basic information about that individual patient.

[0387] A "generated cognitive training task" is a specific cognitive training task that is generated by artificial intelligence and presented to a patient.

[0388] "Patient responses and reactions" refer to the answers and behaviors that patients show in response to cognitive training tasks.

[0389] "Feedback" is information that includes guidelines and advice for adjusting the next training content based on collected patient responses and reactions.

[0390] "Audio data" is a recording of the audio collected as the patient performs the task.

[0391] "Facial expression data" refers to facial expression data recorded while the patient is performing the task.

[0392] "Emotion Engine" is an artificial intelligence technology that analyzes voice data and facial expression data to recognize an individual's emotional state.

[0393] "Security staff" refers to security guards and crime prevention officers who work to ensure the safety of facilities and events.

[0394] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The specific roles and processing procedures of each component are described below.

[0395] Server Roles and Operations

[0396] The server is the central processing unit for the entire system, and it uses the following means:

[0397] 1. Management of User Information

[0398] The server stores the basic information submitted by the user (name, age, years of experience, preferences, past experience) in a database, which will be used for further processing.

[0399] 2. Generation of cognitive training tasks

[0400] It uses artificial intelligence to generate personalized cognitive training exercises based on individual user information, and also uses natural language processing and image recognition technology to provide accurate and effective exercises.

[0401] 3. Emotion analysis

[0402] The emotional engine analyzes the user's emotional state based on the voice and facial expression data collected during the task, allowing the system to understand the user's mental state and reflect this in the training content.

[0403] 4. Generate feedback

[0404] Based on the user's responses and the results of sentiment analysis, feedback is generated to adjust the next training task and sent to the device.

[0405] Terminal roles and processing

[0406] The terminal acts as a user interface and sends and receives data to the server using the following means:

[0407] 1. Initial Setup

[0408] Caregivers and users themselves use a terminal to enter basic information and send it to the server.

[0409] 2. Presenting training tasks

[0410] The generated cognitive training tasks are presented to the user and progress is displayed in real time.

[0411] 3. Recording the reaction

[0412] Voice data and facial expression data are collected during task execution and sent to a server.

[0413] 4. Providing feedback

[0414] The feedback sent from the server is displayed to the user to provide guidance for the next training session.

[0415] User Roles and Actions

[0416] The user engages in cognitive training tasks using the following means:

[0417] 1. Provision of information

[0418] Enter basic information during initial setup.

[0419] 2. Execute the assignment

[0420] The person engages in cognitive training tasks presented on the device and provides answers and responses.

[0421] 3. Receiving feedback

[0422] Review the feedback and incorporate it into your next training session.

[0423] Specific examples

[0424] For example, consider a scenario in which a 50-year-old security officer is starting new training. This officer has 20 years of experience, and previously worked as a police officer. His hobby is fishing. This information is entered into a terminal and sent to the server. The server generates the following challenge based on this information:

[0425] Think back to the most memorable incident you encountered in the course of your work. Reflect on how you felt and responded to the incident.

[0426] An example prompt for this assignment is:

[0427] "Prompt: A 50-year-old veteran security officer has 20 years of work experience. He recently underwent new training. His favorite hobby is fishing, and he previously worked as a police officer. Generate a cognitive training task based on his information."

[0428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0429] Step 1:

[0430] The terminal is started up, and the caregiver or security staff enters basic information (such as name, age, years of experience, preferences, and past experience) through the user interface. The entered basic information is sent from the terminal to the server as a request.

[0431] Input: Basic information such as name, age, years of experience, preferences, past experience, etc.

[0432] Output: The request sent to the server

[0433] Step 2:

[0434] The server saves the received basic information in the database. If the save is successful, it generates a save success message and sends it to the terminal.

[0435] Input: Basic information sent from the device

[0436] Output: Save successful message

[0437] Step 3:

[0438] The device requests the server to generate a cognitive training task based on the basic information. The request includes the basic information.

[0439] Input: Basic information

[0440] Output: Request to server

[0441] Step 4:

[0442] The server uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information, and the generated tasks are sent from the server to the device.

[0443] Input: Basic information and request

[0444] Output: Cognitive training task

[0445] Step 5:

[0446] The device presents the generated cognitive training tasks to the user, who then engages in the tasks and inputs their answers and reactions into the device.

[0447] Input: Cognitive training task

[0448] Output: User's response or response

[0449] Step 6:

[0450] While the device is working on the task, it collects voice and facial expression data and sends it to a server.

[0451] Input: User's voice data and facial expression data

[0452] Output: Send data to the server

[0453] Step 7:

[0454] The server analyzes the collected data using an emotion engine to recognize the user's emotional state.

[0455] Input: Voice data and facial expression data

[0456] Output: Emotional state recognition result

[0457] Step 8:

[0458] The server generates feedback based on the user's answers, reactions, and emotional state to adjust the next training session and sends it to the device.

[0459] Input: User answers, reactions, and emotional states

[0460] Output: Feedback

[0461] Step 9:

[0462] The device presents the feedback received from the server to the user and provides guidelines for reflecting it in the next training session.

[0463] Input: Feedback

[0464] Output: Feedback presented to the user

[0465] Through these steps, the system will be able to provide effective training for dementia patients and security staff that integrates cognitive enhancement and emotional state management.

[0466] 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.

[0467] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0468] 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.

[0469] [Second embodiment]

[0470] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0481] 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."

[0482] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline. This system is easy to use for caregivers and family members and supports the maintenance and improvement of patients' cognitive function.

[0483] System Overview

[0484] Server: The core processing unit of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on basic user information.

[0485] Terminal: Serves as the user interface, collecting information from the dementia patient and caregiver, displaying training tasks, and recording user responses.

[0486] Users: People with dementia and their caregivers use the system and complete the training tasks.

[0487] Program processing

[0488] 1. Initial setup and user information entry

[0489] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences), allowing the system to collect detailed data related to the patient. The input information is then sent to the server.

[0490] 2. Storage of User Information

[0491] Server: Stores the received user information in a database. This allows patient information to be captured and used as the basis for generating future training tasks.

[0492] 3. Generation of cognitive training tasks

[0493] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information.

[0494] Server: Uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information. The generated tasks are sent to the device and presented to the user.

[0495] 4. Present and execute the task

[0496] Device: The device presents the generated training tasks to the dementia patient. For example, it displays quizzes and tasks that prompt recollection related to the patient's favorite hobbies or past work history when they were younger.

[0497] User: Works through the presented tasks and enters answers and responses.

[0498] 5. Collecting user responses

[0499] Terminal: Records the patient's answers and responses to the tasks and sends them to the server.

[0500] 6. Generate feedback

[0501] Server: Analyzes collected user responses and generates feedback to adjust the next training content. Natural language processing technology is used for the analysis, and appropriate interpretations are given to each response.

[0502] Server: Sends the generated feedback to the device.

[0503] 7. Viewing Feedback

[0504] Device: The generated feedback is displayed to the patient and reflected in the next training assignment.

[0505] Specific examples

[0506] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0507] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0508] The patient works on the task displayed on the device, recalling and entering the student's name and anecdote. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time." This feedback is displayed on the device and is used to improve the next training task.

[0509] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and also contributes to reducing the burden on caregivers.

[0510] The processing flow will be explained below.

[0511] Step 1:

[0512] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, progression of dementia, preferences, past experiences, etc.). The input information is confirmed and sent to the server as a request.

[0513] Step 2:

[0514] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[0515] Step 3:

[0516] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[0517] Step 4:

[0518] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0519] Step 5:

[0520] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0521] Step 6:

[0522] User: Engages in cognitive training tasks, such as answering quizzes or recalling familiar places or events.

[0523] Step 7:

[0524] Terminal: Records the answers and responses entered by the patient to the tasks. The recorded data is sent to the server.

[0525] Step 8:

[0526] Server: Analyzes the user's responses and reactions using artificial intelligence technology to generate feedback to adjust the next training session based on the patient's performance and reactions.

[0527] Step 9:

[0528] Server: Sends generated feedback to the device, which evaluates the patient's effort and includes specific advice for improving the next training session.

[0529] Step 10:

[0530] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0531] Example 1

[0532] 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."

[0533] Providing individually optimized cognitive training tasks for patients with declining cognitive function requires a method for generating tasks that correspond to each patient's unique background information and progress. It is also important that the system be easy to use for caregivers and patients themselves. However, existing systems cannot meet these requirements, making it difficult to effectively support patients in maintaining and improving their cognitive function. This issue must be resolved.

[0534] 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.

[0535] In this invention, the server includes means for providing a user interface through which a caregiver inputs basic information about a patient, means for storing the input basic information in a database, means for generating personalized cognitive training tasks based on the patient's basic information using artificial intelligence, means for presenting the generated training tasks to the patient, means for collecting answers and responses from the patient, means for analyzing the collected answers and responses and generating feedback for adjusting the content of the next training, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and to generate and present feedback that continuously improves effectiveness.

[0536] The "user interface" is the part of the system that includes the screens and instructions that allow caregivers to enter basic patient information.

[0537] "Basic information" refers to data necessary for individual optimization of cognitive training tasks, such as the patient's name, age, stage of dementia, preferences, and past experiences.

[0538] "Database" refers to an information management device within the system that stores and manages input basic information and collected answers and responses.

[0539] "Artificial intelligence" is a technology that uses natural language processing and image recognition technology to analyze data and make decisions and generate problems that are closer to those of humans.

[0540] "Cognitive training tasks" refer to specific training content generated by artificial intelligence with the aim of maintaining and improving a patient's cognitive function.

[0541] "Feedback" refers to information and evaluations that are used to adjust the content of the next training based on the results of the patient's cognitive training tasks.

[0542] "Personalized" means having a format and content optimized for a specific patient based on the patient's individual basic information.

[0543] "Answer or response" refers to the response results, such as input, behavior, and evaluation, that a patient makes in response to the presented cognitive training task.

[0544] "Means for collecting" refers to a method or device for recording the patient's answers and responses through the user interface and storing them in a database.

[0545] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline, and is easy for caregivers and family members to use, helping to maintain and improve patients' cognitive function. The system consists of three elements: a server, a terminal, and a user.

[0546] Server: Performs the central processing of the system. The server stores user information in a database and uses artificial intelligence (generative AI model) to generate personalized cognitive training tasks based on the user's basic information. It then analyzes the collected user responses and generates feedback to adjust the next training content based on that information. Specifically, the server uses the following hardware and software. For hardware, it uses, for example, AWS (Amazon Web Services) or Google Cloud Platform. For software, it uses MySQL or PostgreSQL for database management, and OpenAI GPT-4 or Hugging Face Transformers for the artificial intelligence model.

[0547] Terminal: This terminal functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, and recording user responses. Examples of terminals include iPads and Android tablets. Specifically, the terminal communicates with the server, inputs and collects the necessary data via the user interface, and displays the training tasks. Data entered into the terminal is sent to the server in real time, where it is stored and analyzed.

[0548] Users: Dementia patients and their caregivers use the system. Users access the system through a terminal and work on training tasks. The users (patients) input their responses to the tasks displayed on the terminal, and their responses are recorded on the terminal and sent to the server.

[0549] Specific examples

[0550] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server generates a cognitive training task by inputting the following prompt sentences into a generative AI model:

[0551] "Generate cognitive training tasks for a 60-year-old patient with early-stage dementia based on his gardening hobby and past experience as a teacher."

[0552] The task generated by the AI ​​based on this prompt is, "Please remember the names of the students you have taught. Also, please think back on their personalities and memorable moments." The patient works on this task, recalling and inputting the names of the students and stories they shared. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time," which is displayed on the device.

[0553] This system will help maintain and improve the memory and cognitive functions of dementia patients, and also contribute to reducing the burden on caregivers.

[0554] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0555] Step 1:

[0556] Terminal: The caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences) through the terminal's user interface. This information is entered in a form and sent to the server in one go by pressing the "Send" button.

[0557] * Input: Patient's basic information

[0558] *Output: Basic information data sent to the server

[0559] Step 2:

[0560] Server: Stores the received basic information in a database using an SQL query, where data corresponding to each field is saved in the appropriate table (e.g., "INSERT INTO users (name, age, condition, preferences) VALUES (...)").

[0561] * Input: Basic information data sent from the device

[0562] * Output: Patient information stored in the database

[0563] Step 3:

[0564] Terminal: The caregiver requests the server to generate a cognitive training task through the terminal. The user interface has a "Generate Task" button, and clicking this button sends the request to the server.

[0565] * Input: Issue creation request

[0566] *Output: Request data is sent to the server

[0567] Step 4:

[0568] Server: Upon receiving the request, the server uses an artificial intelligence model (e.g., a generative AI model) to generate a personalized cognitive training task based on the basic information. It generates a prompt (e.g., "Please generate a cognitive training task for a 60-year-old person with early-stage dementia based on his gardening hobby and past experience as a teacher") and inputs this prompt into the AI ​​model to generate the task. The generated task is then sent to the device via the server's API.

[0569] * Input: Basic information, prompt text

[0570] * Output: Generated training tasks

[0571] Step 5:

[0572] Device: Generated training tasks are displayed on the device. The user interface dashboard lists new tasks and provides them in a format for the patient to complete (e.g., quiz or open-ended).

[0573] * Input: Assignment data sent from the server

[0574] * Output: Assignment displayed on the device

[0575] Step 6:

[0576] User: The patient works on tasks displayed on the device and inputs their answers. For example, to the task, "Remember the names of the students you have taught and reflect on their personalities and memorable moments," the patient uses the device's keyboard to input their answers.

[0577] * Input: Patient's response

[0578] * Output: Answer data recorded on the device

[0579] Step 7:

[0580] Terminal: The patient's answers and reactions to the tasks are recorded on the terminal, temporarily saved, and then sent to the server.

[0581] * Input: Patient response data

[0582] *Output: Reaction data sent to the server

[0583] Step 8:

[0584] Server: The received response data is stored in a database (e.g., "INSERT INTO responses (user_id, response) VALUES (...)"). The collected data is then analyzed and feedback is generated to adjust the next training content. At this time, the response data and a supplementary prompt (e.g., "Please rate this response and suggest the next training content") are input to the AI ​​model. The generated feedback is then sent back to the device via the API.

[0585] * Input: Patient response data, supplementary prompt text

[0586] * Output: Generated feedback

[0587] Step 9:

[0588] Device: The generated feedback is displayed to the patient. The feedback message is displayed on the dashboard of the user interface and is reflected in the next training challenge. For example, a positive message such as "Great! Let's try a more difficult challenge next time" is displayed.

[0589] * Input: Feedback sent from the server

[0590] * Output: Feedback displayed on the device

[0591] (Application example 1)

[0592] 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."

[0593] Conventionally, cognitive training for patients with cognitive decline has often not been individually optimized, making effective training difficult. In particular, there has been a lack of systems for dementia prevention in brick-and-mortar stores, resulting in a lack of environments where patients can easily train in their daily lives. Furthermore, it has been difficult to generate personalized tasks based on the patient's past experiences and preferences, making it difficult to maintain motivation.

[0594] 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.

[0595] In this invention, the server includes: means for inputting basic information of a patient suffering from cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and responses from the patient; means for analyzing the collected answers and responses and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; and means for providing the patient with personalized cognitive training tasks via a terminal installed in a physical store and recording the patient's responses. This enables patients suffering from cognitive decline to engage in individually optimized cognitive training tasks even in a physical store, providing an environment where they can easily train in their daily lives.

[0596] "Cognitive decline" refers to a decline in cognitive abilities such as memory, attention, and comprehension.

[0597] "Basic patient information" refers to information such as the name, age, progression of dementia, hobbies, and past experiences of patients with cognitive decline.

[0598] "Artificial intelligence" refers to technology that mimics human intelligence on a computer, independently learning from past data to solve problems.

[0599] "Personalized cognitive training tasks" refer to tasks designed to improve cognitive function that are optimized for a specific patient based on basic information about the individual patient.

[0600] "Generating means" refers to the process or device that uses artificial intelligence to create specific tasks or content.

[0601] "Presenting means" refers to the process or device for displaying or communicating the generated tasks and feedback to the patient.

[0602] "Patient response" refers to the responses or behaviors that a patient exhibits in response to cognitive training tasks.

[0603] "Means of collection" refers to the process or device used to record and store patient responses.

[0604] "Feedback" refers to providing information to adjust the next training session based on the collected data.

[0605] "Brick and mortar store" means a physical retail establishment that is open to the public.

[0606] "Terminal" refers to an electronic device or computer used to input or output information.

[0607] This invention is a system for providing personalized cognitive training exercises to patients with cognitive decline. This system utilizes artificial intelligence to generate cognitive training exercises optimized for each patient based on basic information about the patient, and provides them via a terminal installed in a physical store. The configuration and operation of this system are described in detail below.

[0608] server

[0609] The server is responsible for the core functions of this system, mainly fulfilling the following roles:

[0610] Save basic information

[0611] The server stores basic information about the patient (such as name, age, hobbies, and past experiences) received through the terminal in a database. This data is used to generate future training tasks.

[0612] Generation of cognitive training tasks

[0613] The server generates personalized cognitive training tasks using a generative AI model based on the stored basic patient information. The generative AI model is equipped with natural language processing and image recognition technologies, and is capable of generating appropriate tasks based on the input information.

[0614] Terminal

[0615] The terminal is a device installed in a physical store that provides an interface between the patient and the system. Its main functions are as follows:

[0616] Presentation of cognitive training tasks

[0617] The device presents the cognitive training tasks sent from the server to the patient, for example, using a tablet or dedicated smart display.

[0618] Collecting patient responses

[0619] The device records the answers and responses the patient gives to the training tasks and sends them to a server, where they are used to generate feedback.

[0620] feedback

[0621] Generate feedback

[0622] The server analyzes the collected responses from the patient and generates feedback to adjust the content of the next training session, which is then presented to the patient via their device.

[0623] Providing feedback

[0624] The terminal displays the feedback sent from the server to the patient, which provides guidance for the next training session.

[0625] Specific examples

[0626] For example, a 70-year-old patient might use a terminal in a brick-and-mortar store and enter the following information:

[0627] Name: Taro

[0628] Age: 70

[0629] Hobbies: Gardening

[0630] Previous experience: Teacher

[0631] This information is sent to a server, which uses a generative AI model to generate challenges like this:

[0632] Think back to places you've traveled to in the past.

[0633] Think back to the names of your friends from school.

[0634] Think back to the title of your favorite movie.

[0635] The device presents the task to the patient, and when the patient enters their answer, the response is sent back to the server, which analyzes it and generates feedback such as, "In your next training session, try a more detailed task based on your past experiences," which is displayed on the device.

[0636] This series of processes allows patients to continuously engage in personalized cognitive training tasks, which is expected to maintain and improve cognitive function.

[0637] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0638] Step 1:

[0639] The device accepts basic information from the user. This information includes name, age, hobbies, past experiences, etc. The device then sends this input data to the server. The input data is in JSON format, for example. Specifically, the user enters information into a form on the tablet and presses the send button, which sends the data to the server.

[0640] Step 2:

[0641] The server saves the received basic information in the database. In this step, the data is formatted to store the received data in the database in the appropriate format. Specifically, the server connects to the database and saves the received data using an INSERT statement.

[0642] Step 3:

[0643] The device requests the server to generate cognitive training tasks. The server uses the generative AI model to generate personalized cognitive training tasks based on the stored basic information. This uses natural language processing and image recognition technologies. The generated tasks are sent to the device in JSON format. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves the generated tasks.

[0644] Step 4:

[0645] The device presents the cognitive training tasks received from the server to the user. Specifically, the tasks are displayed on the tablet's display. The user then works on the presented tasks and inputs their answers.

[0646] Step 5:

[0647] The device collects the answers and reactions entered by the user and sends them to the server. At this time, the user's answer data is sent in JSON format. Specifically, the device collects the data entered in the input form and sends it to the server when the send button is pressed.

[0648] Step 6:

[0649] The server analyzes the collected answers and reactions and generates feedback to adjust the next training content. This uses natural language processing technology. The generated feedback is sent to the device in JSON format. Specifically, the server analyzes the answer data, generates appropriate feedback, and sends it.

[0650] Step 7:

[0651] The device then presents the feedback received from the server to the user, allowing the user to work on the next assignment based on this feedback. Specifically, the feedback message is displayed on the tablet screen.

[0652] 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.

[0653] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and further combines it with an emotion engine that recognizes the user's emotions. This system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[0654] System Overview

[0655] Server: This performs the core processing of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on the user's basic information. It also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[0656] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[0657] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[0658] Program processing

[0659] 1. Initial setup and user information entry

[0660] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.). The terminal confirms the input information and sends it as a request to the server.

[0661] 2. Storage of User Information

[0662] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[0663] 3. Generation of cognitive training tasks

[0664] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[0665] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0666] 4. Present and execute the task

[0667] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0668] User: Works on the tasks presented to them. For example, they train by answering quizzes or recalling nostalgic places and events.

[0669] 5. Emotional state analysis using the emotion engine

[0670] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends it to the server.

[0671] Server: Analyzes the collected data using an emotion engine and recognizes the patient's emotional state (e.g., joy, sadness, excitement, etc.).

[0672] 6. Collecting user reaction and sentiment data

[0673] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[0674] 7. Generate feedback

[0675] Server: Analyzes the user's responses, reactions, and emotional state and generates feedback to adjust the next training content. The feedback includes appropriate advice based on the patient's cognitive and emotional state using artificial intelligence and emotion analysis technologies.

[0676] Server: Sends the generated feedback to the device.

[0677] 8. Viewing Feedback

[0678] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0679] Specific examples

[0680] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0681] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0682] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[0683] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[0684] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.), allowing the system to collect detailed data related to the patient. The input information is verified and sent as a request to the server.

[0688] Step 2:

[0689] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal, thereby informing the caregiver that the information has been saved successfully.

[0690] Step 3:

[0691] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. This request includes the patient's basic information.

[0692] Step 4:

[0693] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0694] Step 5:

[0695] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0696] Step 6:

[0697] User: Works on presented cognitive training tasks, such as recalling a student's name or answering questions about past events.

[0698] Step 7:

[0699] Device: While the patient is working on the task, it collects voice and facial expression data and sends them to the server. This data is used to analyze the patient's emotional state.

[0700] Step 8:

[0701] Server: Analyzes collected voice and facial expression data using an emotion engine to recognize the patient's emotional state (e.g., joy, sadness, excitement, etc.). Stores the recognition results for further processing.

[0702] Step 9:

[0703] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[0704] Step 10:

[0705] Server: Analyzes the user's responses, reactions, and emotional state using artificial intelligence and emotion analysis technology, and generates feedback to adjust the next training content based on the patient's cognitive and emotional state.

[0706] Step 11:

[0707] Server: Sends the generated feedback to the device. The feedback includes advice based on the cognitive training content and emotional state.

[0708] Step 12:

[0709] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0710] Specific examples

[0711] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0712] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0713] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[0714] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[0715] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[0716] Example 2

[0717] 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."

[0718] It has been difficult with current technologies to provide individually optimized cognitive training tasks for patients with cognitive decline, and to properly understand the patient's emotional state and provide feedback based on that. Furthermore, a system that can be easily used by caregivers and family members is needed.

[0719] 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.

[0720] In this invention, the server includes means for inputting basic information of a patient with cognitive decline, means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information, means for presenting the generated cognitive training tasks to the patient, means for collecting the patient's answers, reactions, voice data, and facial expression data, means for analyzing the collected answers, reactions, voice data, and facial expression data to recognize the patient's emotional state, means for adjusting the next training content based on the analysis results and generating feedback, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and feedback based on the patient's emotional state.

[0721] "Basic patient information" refers to the name, age, stage of dementia, preferences, past experiences, etc. of the patient whose cognitive function is declining.

[0722] "Personalized cognitive training tasks" refer to tasks for improving cognitive function that are individually optimized using artificial intelligence based on the patient's basic information.

[0723] "Artificial intelligence" refers to technologies that enable computers to mimic human intelligence, including natural language processing and image recognition techniques.

[0724] "Natural language processing" refers to the technology that enables computers to understand, generate, and respond to human language.

[0725] "Image recognition technology" refers to the technology that allows computers to analyze image and video data and identify patterns and objects.

[0726] "Means for presenting training tasks" refers to a mechanism for displaying cognitive training tasks generated through a terminal to a patient.

[0727] "Means for collecting responses" refers to a mechanism for recording and storing the responses or responses provided by the patient to the training tasks.

[0728] "Audio data" refers to data that records the sounds made when a patient is performing a task.

[0729] "Facial expression data" refers to data recording the facial expressions observed when a patient is performing a task.

[0730] "Means for recognizing emotional state" refers to a mechanism for analyzing collected voice data and facial expression data to identify the patient's emotional state (happiness, sadness, excitement, etc.).

[0731] "Means for generating feedback" refers to a mechanism for adjusting the next training content and providing appropriate advice and guidance based on the patient's answers, reactions, and emotional state.

[0732] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and also combines an emotion engine that recognizes the user's emotions. The system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[0733] System Overview

[0734] Server: This is the core processing part of the system, and uses artificial intelligence to generate personalized cognitive training tasks based on the user's basic information. The server also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[0735] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[0736] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[0737] Program processing

[0738] The system works through the following steps:

[0739] 1. Initial setup and user information entry: Using the terminal, the caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.) and sends the entered information to the server.

[0740] 2. Save user information: The server saves the received user information in the database and sends a success message to the terminal.

[0741] 3. Generation of cognitive training tasks: Based on requests from the device, the server generates personalized cognitive training tasks using artificial intelligence (natural language processing and image recognition technology). The generated tasks are sent from the server to the device.

[0742] 4. Presentation and execution of the task: The device presents the generated cognitive training task to the patient, who then completes the task with the support of a caregiver.

[0743] 5. Analysis of emotional state using emotion engine: The device collects voice and facial expression data while the patient is working on the task and sends it to the server. The server analyzes this data using the emotion engine and recognizes the patient's emotional state.

[0744] 6. Collecting user response and emotional data: The device records the patient's answers and responses, as well as their emotional state analyzed by the emotion engine, and sends them to the server.

[0745] 7. Feedback Generation: The server analyzes the received data and generates feedback to adjust the next training content. The generated feedback is sent to the device.

[0746] 8. Displaying feedback: The device presents the received feedback to the patient and provides guidance for the next training session.

[0747] Hardware and software used

[0748] Hardware: A typical computer, tablet, or smartphone can be used as the device. The device's built-in microphone and camera are used to collect voice and facial expression data.

[0749] Software: The server is built using programming languages ​​such as Python or Java, and the database uses MySQL or PostgreSQL, etc. For artificial intelligence, TensorFlow or PyTorch is used for natural language processing, and Amazon Rekognition or Google Cloud Vision is used for image recognition.

[0750] Specific examples

[0751] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. This information is entered into a device and sent to a server. The server uses artificial intelligence to generate the following tasks:

[0752] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0753] The patient completes the tasks displayed on the device and inputs the names of their students and other anecdotes. Voice and facial expression data collected while the patient is working on the tasks is sent to a server, where an emotion engine analyzes this data to recognize the patient's emotional state.

[0754] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and can be used to improve the next training session.

[0755] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[0756] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0757] Step 1:

[0758] Initial setup and entering user information

[0759] Terminal: The caregiver enters basic information about the patient (name, age, progression of dementia, preferences, past experiences, etc.) through the terminal's user interface. After confirming the entered information, the caregiver presses the "Send" button.

[0760] Input: Patient basic information.

[0761] Output: The request to the server.

[0762] Specific operation: The terminal checks the entered basic information of the patient and sends it as an HTTP request to the server.

[0763] Step 2:

[0764] Saving user information

[0765] Server: Saves the received user information in the database. If the save is successful, generates a success message stating "User information has been saved successfully" and sends it to the terminal.

[0766] Input: Basic patient information sent from the terminal.

[0767] Output: User information saved in database, success message.

[0768] Specific operation: The server analyzes the received basic information of the patient and inserts it into the database in the appropriate format. If the insertion is successful, it generates a message indicating success and sends it to the terminal.

[0769] Step 3:

[0770] Generation of cognitive training tasks

[0771] Terminal: Requests the server to generate cognitive training tasks based on basic information about a specific patient.

[0772] Input: A request based on basic patient information.

[0773] Output: The request to the server.

[0774] Specific operation: The terminal sends a request to the server to generate cognitive training tasks based on the stored basic information of the patient.

[0775] Server: Uses artificial intelligence to generate personalized cognitive training tasks.

[0776] Input: Patient basic information.

[0777] Output: The generated cognitive training task.

[0778] Specific operation: The server uses natural language processing and image recognition technology to generate cognitive training tasks based on the patient's basic information and sends them to the terminal.

[0779] Step 4:

[0780] Presenting and executing the task

[0781] Terminal: Presents the generated cognitive training tasks to the patient and encourages them to complete the tasks.

[0782] Input: Generated cognitive training tasks.

[0783] Output: The issue displayed in the user interface.

[0784] Specific operation: The terminal displays the cognitive training task received from the server on the screen and asks the patient to complete the task.

[0785] User: Works on the assignment and inputs answers and responses via the device.

[0786] Input: Answers and responses to the assignment.

[0787] Output: Answers or responses typed into the terminal.

[0788] Specific operations: The patient enters answers to the questions that are displayed one after another, and these are recorded on the terminal.

[0789] Step 5:

[0790] Emotional state analysis using emotion engine

[0791] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends them to the server.

[0792] Input: Patient's voice and facial expression data.

[0793] Output: Sending voice data and facial expression data to the server.

[0794] Specific operation: The device's built-in microphone and camera collect voice and facial expression data, which are then sent to the server.

[0795] Server: Analyzes these data using an emotion engine to recognize the patient's emotional state.

[0796] Input: Collected speech and facial expression data.

[0797] Output: Parsed emotional state.

[0798] Specific operation: The server uses voice analysis software and image recognition software to recognize the patient's emotional state (happiness, sadness, excitement, etc.) based on the collected data.

[0799] Step 6:

[0800] Collecting user reaction and sentiment data

[0801] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends these to the server.

[0802] Input: Patient's answers, reactions, and emotional state.

[0803] Output: Data sent to the server.

[0804] Specific operation: The device records the user's answers and reactions and sends them to the server along with pre-analyzed emotional data.

[0805] Step 7:

[0806] Generate feedback

[0807] Server: Analyzes the received answers, reactions, and emotional state data and generates feedback to adjust the next training content.

[0808] Input: User answers, reactions, and sentiment data.

[0809] Output: The generated feedback.

[0810] How it works: The server uses artificial intelligence technology to adjust the next training session based on the collected data and generate appropriate feedback.

[0811] Server: Sends the generated feedback to the device.

[0812] Input: Generated feedback.

[0813] Output: Data sent to the terminal.

[0814] Specific operation: The server sends the generated feedback to the device and reflects it in the next training content.

[0815] Step 8:

[0816] View Feedback

[0817] Terminal: Presents the received feedback to the patient.

[0818] Input: Feedback from the server.

[0819] Output: Feedback displayed in the user interface.

[0820] Specific behavior: The device displays the received feedback on the screen and provides guidance to patients and caregivers for the next training session.

[0821] These steps enable the system to provide individually optimized cognitive training tasks and feedback based on emotional state.

[0822] (Application example 2)

[0823] 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."

[0824] Conventional cognitive training systems do not adequately consider the individual cognitive function and emotional state of dementia patients, making it difficult to provide effective training. Furthermore, when it comes to training for individuals engaged in specific occupations, such as security staff, there is a lack of technology to provide cognitive training tailored to the characteristics of their jobs. This has led to a demand for performance improvement through emotional management as well as cognitive function improvement.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0826] In this invention, the server includes: means for inputting basic information of a patient with cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and reactions from the patient; means for analyzing the collected answers and reactions and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; means for collecting voice data and facial expression data and analyzing them using an emotion engine; and means for adjusting the training content based on the analyzed emotional state. This makes it possible to provide effective training that integrates cognitive function improvement and emotion management to dementia patients and individuals engaged in specific occupations.

[0827] "Cognitive function" is a general term for an individual's intellectual abilities to understand and remember information, solve problems, and make decisions.

[0828] "Basic patient information" refers to information specific to an individual patient, such as name, age, stage of dementia, preferences, and past experiences.

[0829] "Artificial intelligence" is a technology that allows computer systems to mimic human intellectual behavior, analyze complex data, and make decisions, and includes natural language processing and image recognition technologies.

[0830] "Personalized cognitive training tasks" are tasks for improving cognitive function that are optimized for each individual patient based on basic information about that individual patient.

[0831] A "generated cognitive training task" is a specific cognitive training task that is generated by artificial intelligence and presented to a patient.

[0832] "Patient responses and reactions" refer to the answers and behaviors that patients show in response to cognitive training tasks.

[0833] "Feedback" is information that includes guidelines and advice for adjusting the next training content based on collected patient responses and reactions.

[0834] "Audio data" is a recording of the audio collected as the patient performs the task.

[0835] "Facial expression data" refers to facial expression data recorded while the patient is performing the task.

[0836] "Emotion Engine" is an artificial intelligence technology that analyzes voice data and facial expression data to recognize an individual's emotional state.

[0837] "Security staff" refers to security guards and crime prevention officers who work to ensure the safety of facilities and events.

[0838] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The specific roles and processing procedures of each component are described below.

[0839] Server Roles and Operations

[0840] The server is the central processing unit for the entire system, and it uses the following means:

[0841] 1. Management of User Information

[0842] The server stores the basic information submitted by the user (name, age, years of experience, preferences, past experience) in a database, which will be used for further processing.

[0843] 2. Generation of cognitive training tasks

[0844] It uses artificial intelligence to generate personalized cognitive training exercises based on individual user information, and also uses natural language processing and image recognition technology to provide accurate and effective exercises.

[0845] 3. Emotion analysis

[0846] The emotional engine analyzes the user's emotional state based on the voice and facial expression data collected during the task, allowing the system to understand the user's mental state and reflect this in the training content.

[0847] 4. Generate feedback

[0848] Based on the user's responses and the results of sentiment analysis, feedback is generated to adjust the next training task and sent to the device.

[0849] Terminal roles and processing

[0850] The terminal acts as a user interface and sends and receives data to the server using the following means:

[0851] 1. Initial Setup

[0852] Caregivers and users themselves use a terminal to enter basic information and send it to the server.

[0853] 2. Presenting training tasks

[0854] The generated cognitive training tasks are presented to the user and progress is displayed in real time.

[0855] 3. Recording the reaction

[0856] Voice data and facial expression data are collected during task execution and sent to a server.

[0857] 4. Providing feedback

[0858] The feedback sent from the server is displayed to the user to provide guidance for the next training session.

[0859] User Roles and Actions

[0860] The user engages in cognitive training tasks using the following means:

[0861] 1. Provision of information

[0862] Enter basic information during initial setup.

[0863] 2. Execute the assignment

[0864] The person engages in cognitive training tasks presented on the device and provides answers and responses.

[0865] 3. Receiving feedback

[0866] Review the feedback and incorporate it into your next training session.

[0867] Specific examples

[0868] For example, consider a scenario in which a 50-year-old security officer is starting new training. This officer has 20 years of experience, and previously worked as a police officer. His hobby is fishing. This information is entered into a terminal and sent to the server. The server generates the following challenge based on this information:

[0869] Think back to the most memorable incident you encountered in the course of your work. Reflect on how you felt and responded to the incident.

[0870] An example prompt for this assignment is:

[0871] "Prompt: A 50-year-old veteran security officer has 20 years of work experience. He recently underwent new training. His favorite hobby is fishing, and he previously worked as a police officer. Generate a cognitive training task based on his information."

[0872] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0873] Step 1:

[0874] The terminal is started up, and the caregiver or security staff enters basic information (such as name, age, years of experience, preferences, and past experience) through the user interface. The entered basic information is sent from the terminal to the server as a request.

[0875] Input: Basic information such as name, age, years of experience, preferences, past experience, etc.

[0876] Output: The request sent to the server

[0877] Step 2:

[0878] The server saves the received basic information in the database. If the save is successful, it generates a save success message and sends it to the terminal.

[0879] Input: Basic information sent from the device

[0880] Output: Save successful message

[0881] Step 3:

[0882] The device requests the server to generate a cognitive training task based on the basic information. The request includes the basic information.

[0883] Input: Basic information

[0884] Output: Request to server

[0885] Step 4:

[0886] The server uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information, and the generated tasks are sent from the server to the device.

[0887] Input: Basic information and request

[0888] Output: Cognitive training task

[0889] Step 5:

[0890] The device presents the generated cognitive training tasks to the user, who then engages in the tasks and inputs their answers and reactions into the device.

[0891] Input: Cognitive training task

[0892] Output: User's response or response

[0893] Step 6:

[0894] While the device is working on the task, it collects voice and facial expression data and sends it to a server.

[0895] Input: User's voice data and facial expression data

[0896] Output: Send data to the server

[0897] Step 7:

[0898] The server analyzes the collected data using an emotion engine to recognize the user's emotional state.

[0899] Input: Voice data and facial expression data

[0900] Output: Emotional state recognition result

[0901] Step 8:

[0902] The server generates feedback based on the user's answers, reactions, and emotional state to adjust the next training session and sends it to the device.

[0903] Input: User answers, reactions, and emotional states

[0904] Output: Feedback

[0905] Step 9:

[0906] The device presents the feedback received from the server to the user and provides guidelines for reflecting it in the next training session.

[0907] Input: Feedback

[0908] Output: Feedback presented to the user

[0909] Through these steps, the system will be able to provide effective training for dementia patients and security staff that integrates cognitive enhancement and emotional state management.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] [Third embodiment]

[0914] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0915] 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.

[0916] 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).

[0917] 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.

[0918] 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.

[0919] 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).

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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."

[0926] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline. This system is easy to use for caregivers and family members and supports the maintenance and improvement of patients' cognitive function.

[0927] System Overview

[0928] Server: The core processing unit of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on basic user information.

[0929] Terminal: Serves as the user interface, collecting information from the dementia patient and caregiver, displaying training tasks, and recording user responses.

[0930] Users: People with dementia and their caregivers use the system and complete the training tasks.

[0931] Program processing

[0932] 1. Initial setup and user information entry

[0933] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences), allowing the system to collect detailed data related to the patient. The input information is then sent to the server.

[0934] 2. Storage of User Information

[0935] Server: Stores the received user information in a database. This allows patient information to be captured and used as the basis for generating future training tasks.

[0936] 3. Generation of cognitive training tasks

[0937] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information.

[0938] Server: Uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information. The generated tasks are sent to the device and presented to the user.

[0939] 4. Present and execute the task

[0940] Device: The device presents the generated training tasks to the dementia patient. For example, it displays quizzes and tasks that prompt recollection related to the patient's favorite hobbies or past work history when they were younger.

[0941] User: Works through the presented tasks and enters answers and responses.

[0942] 5. Collecting user responses

[0943] Terminal: Records the patient's answers and responses to the tasks and sends them to the server.

[0944] 6. Generate feedback

[0945] Server: Analyzes collected user responses and generates feedback to adjust the next training content. Natural language processing technology is used for the analysis, and appropriate interpretations are given to each response.

[0946] Server: Sends the generated feedback to the device.

[0947] 7. Viewing Feedback

[0948] Device: The generated feedback is displayed to the patient and reflected in the next training assignment.

[0949] Specific examples

[0950] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[0951] "Recall the names of students you have taught and reflect on their personalities and great moments."

[0952] The patient works on the task displayed on the device, recalling and entering the student's name and anecdote. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time." This feedback is displayed on the device and is used to improve the next training task.

[0953] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and also contributes to reducing the burden on caregivers.

[0954] The processing flow will be explained below.

[0955] Step 1:

[0956] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, progression of dementia, preferences, past experiences, etc.). The input information is confirmed and sent to the server as a request.

[0957] Step 2:

[0958] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[0959] Step 3:

[0960] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[0961] Step 4:

[0962] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[0963] Step 5:

[0964] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[0965] Step 6:

[0966] User: Engages in cognitive training tasks, such as answering quizzes or recalling familiar places or events.

[0967] Step 7:

[0968] Terminal: Records the answers and responses entered by the patient to the tasks. The recorded data is sent to the server.

[0969] Step 8:

[0970] Server: Analyzes the user's responses and reactions using artificial intelligence technology to generate feedback to adjust the next training session based on the patient's performance and reactions.

[0971] Step 9:

[0972] Server: Sends generated feedback to the device, which evaluates the patient's effort and includes specific advice for improving the next training session.

[0973] Step 10:

[0974] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[0975] Example 1

[0976] 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."

[0977] Providing individually optimized cognitive training tasks for patients with declining cognitive function requires a method for generating tasks that correspond to each patient's unique background information and progress. It is also important that the system be easy to use for caregivers and patients themselves. However, existing systems cannot meet these requirements, making it difficult to effectively support patients in maintaining and improving their cognitive function. This issue must be resolved.

[0978] 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.

[0979] In this invention, the server includes means for providing a user interface through which a caregiver inputs basic information about a patient, means for storing the input basic information in a database, means for generating personalized cognitive training tasks based on the patient's basic information using artificial intelligence, means for presenting the generated training tasks to the patient, means for collecting answers and responses from the patient, means for analyzing the collected answers and responses and generating feedback for adjusting the content of the next training, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and to generate and present feedback that continuously improves effectiveness.

[0980] The "user interface" is the part of the system that includes the screens and instructions that allow caregivers to enter basic patient information.

[0981] "Basic information" refers to data necessary for individual optimization of cognitive training tasks, such as the patient's name, age, stage of dementia, preferences, and past experiences.

[0982] "Database" refers to an information management device within the system that stores and manages input basic information and collected answers and responses.

[0983] "Artificial intelligence" is a technology that uses natural language processing and image recognition technology to analyze data and make decisions and generate problems that are closer to those of humans.

[0984] "Cognitive training tasks" refer to specific training content generated by artificial intelligence with the aim of maintaining and improving a patient's cognitive function.

[0985] "Feedback" refers to information and evaluations that are used to adjust the content of the next training based on the results of the patient's cognitive training tasks.

[0986] "Personalized" means having a format and content optimized for a specific patient based on the patient's individual basic information.

[0987] "Answer or response" refers to the response results, such as input, behavior, and evaluation, that a patient makes in response to the presented cognitive training task.

[0988] "Means for collecting" refers to a method or device for recording the patient's answers and responses through the user interface and storing them in a database.

[0989] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline, and is easy for caregivers and family members to use, helping to maintain and improve patients' cognitive function. The system consists of three elements: a server, a terminal, and a user.

[0990] Server: Performs the central processing of the system. The server stores user information in a database and uses artificial intelligence (generative AI model) to generate personalized cognitive training tasks based on the user's basic information. It then analyzes the collected user responses and generates feedback to adjust the next training content based on that information. Specifically, the server uses the following hardware and software. For hardware, it uses, for example, AWS (Amazon Web Services) or Google Cloud Platform. For software, it uses MySQL or PostgreSQL for database management, and OpenAI GPT-4 or Hugging Face Transformers for the artificial intelligence model.

[0991] Terminal: This terminal functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, and recording user responses. Examples of terminals include iPads and Android tablets. Specifically, the terminal communicates with the server, inputs and collects the necessary data via the user interface, and displays the training tasks. Data entered into the terminal is sent to the server in real time, where it is stored and analyzed.

[0992] Users: Dementia patients and their caregivers use the system. Users access the system through a terminal and work on training tasks. The users (patients) input their responses to the tasks displayed on the terminal, and their responses are recorded on the terminal and sent to the server.

[0993] Specific examples

[0994] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server generates a cognitive training task by inputting the following prompt sentences into a generative AI model:

[0995] "Generate cognitive training tasks for a 60-year-old patient with early-stage dementia based on his gardening hobby and past experience as a teacher."

[0996] The task generated by the AI ​​based on this prompt is, "Please remember the names of the students you have taught. Also, please think back on their personalities and memorable moments." The patient works on this task, recalling and inputting the names of the students and stories they shared. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time," which is displayed on the device.

[0997] This system will help maintain and improve the memory and cognitive functions of dementia patients, and also contribute to reducing the burden on caregivers.

[0998] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0999] Step 1:

[1000] Terminal: The caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences) through the terminal's user interface. This information is entered in a form and sent to the server in one go by pressing the "Send" button.

[1001] * Input: Patient's basic information

[1002] *Output: Basic information data sent to the server

[1003] Step 2:

[1004] Server: Stores the received basic information in a database using an SQL query, where data corresponding to each field is saved in the appropriate table (e.g., "INSERT INTO users (name, age, condition, preferences) VALUES (...)").

[1005] * Input: Basic information data sent from the device

[1006] * Output: Patient information stored in the database

[1007] Step 3:

[1008] Terminal: The caregiver requests the server to generate a cognitive training task through the terminal. The user interface has a "Generate Task" button, and clicking this button sends the request to the server.

[1009] * Input: Issue creation request

[1010] *Output: Request data is sent to the server

[1011] Step 4:

[1012] Server: Upon receiving the request, the server uses an artificial intelligence model (e.g., a generative AI model) to generate a personalized cognitive training task based on the basic information. It generates a prompt (e.g., "Please generate a cognitive training task for a 60-year-old person with early-stage dementia based on his gardening hobby and past experience as a teacher") and inputs this prompt into the AI ​​model to generate the task. The generated task is then sent to the device via the server's API.

[1013] * Input: Basic information, prompt text

[1014] * Output: Generated training tasks

[1015] Step 5:

[1016] Device: Generated training tasks are displayed on the device. The user interface dashboard lists new tasks and provides them in a format for the patient to complete (e.g., quiz or open-ended).

[1017] * Input: Assignment data sent from the server

[1018] * Output: Assignment displayed on the device

[1019] Step 6:

[1020] User: The patient works on tasks displayed on the device and inputs their answers. For example, to the task, "Remember the names of the students you have taught and reflect on their personalities and memorable moments," the patient uses the device's keyboard to input their answers.

[1021] * Input: Patient's response

[1022] * Output: Answer data recorded on the device

[1023] Step 7:

[1024] Terminal: The patient's answers and reactions to the tasks are recorded on the terminal, temporarily saved, and then sent to the server.

[1025] * Input: Patient response data

[1026] *Output: Reaction data sent to the server

[1027] Step 8:

[1028] Server: The received response data is stored in a database (e.g., "INSERT INTO responses (user_id, response) VALUES (...)"). The collected data is then analyzed and feedback is generated to adjust the next training content. At this time, the response data and a supplementary prompt (e.g., "Please rate this response and suggest the next training content") are input to the AI ​​model. The generated feedback is then sent back to the device via the API.

[1029] * Input: Patient response data, supplementary prompt text

[1030] * Output: Generated feedback

[1031] Step 9:

[1032] Device: The generated feedback is displayed to the patient. The feedback message is displayed on the dashboard of the user interface and is reflected in the next training challenge. For example, a positive message such as "Great! Let's try a more difficult challenge next time" is displayed.

[1033] * Input: Feedback sent from the server

[1034] * Output: Feedback displayed on the device

[1035] (Application example 1)

[1036] 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."

[1037] Conventionally, cognitive training for patients with cognitive decline has often not been individually optimized, making effective training difficult. In particular, there has been a lack of systems for dementia prevention in brick-and-mortar stores, resulting in a lack of environments where patients can easily train in their daily lives. Furthermore, it has been difficult to generate personalized tasks based on the patient's past experiences and preferences, making it difficult to maintain motivation.

[1038] 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.

[1039] In this invention, the server includes: means for inputting basic information of a patient suffering from cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and responses from the patient; means for analyzing the collected answers and responses and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; and means for providing the patient with personalized cognitive training tasks via a terminal installed in a physical store and recording the patient's responses. This enables patients suffering from cognitive decline to engage in individually optimized cognitive training tasks even in a physical store, providing an environment where they can easily train in their daily lives.

[1040] "Cognitive decline" refers to a decline in cognitive abilities such as memory, attention, and comprehension.

[1041] "Basic patient information" refers to information such as the name, age, progression of dementia, hobbies, and past experiences of patients with cognitive decline.

[1042] "Artificial intelligence" refers to technology that mimics human intelligence on a computer, independently learning from past data to solve problems.

[1043] "Personalized cognitive training tasks" refer to tasks designed to improve cognitive function that are optimized for a specific patient based on basic information about the individual patient.

[1044] "Generating means" refers to the process or device that uses artificial intelligence to create specific tasks or content.

[1045] "Presenting means" refers to the process or device for displaying or communicating the generated tasks and feedback to the patient.

[1046] "Patient response" refers to the responses or behaviors that a patient exhibits in response to cognitive training tasks.

[1047] "Means of collection" refers to the process or device used to record and store patient responses.

[1048] "Feedback" refers to providing information to adjust the next training session based on the collected data.

[1049] "Brick and mortar store" means a physical retail establishment that is open to the public.

[1050] "Terminal" refers to an electronic device or computer used to input or output information.

[1051] This invention is a system for providing personalized cognitive training exercises to patients with cognitive decline. This system utilizes artificial intelligence to generate cognitive training exercises optimized for each patient based on basic information about the patient, and provides them via a terminal installed in a physical store. The configuration and operation of this system are described in detail below.

[1052] server

[1053] The server is responsible for the core functions of this system, mainly fulfilling the following roles:

[1054] Save basic information

[1055] The server stores basic information about the patient (such as name, age, hobbies, and past experiences) received through the terminal in a database. This data is used to generate future training tasks.

[1056] Generation of cognitive training tasks

[1057] The server generates personalized cognitive training tasks using a generative AI model based on the stored basic patient information. The generative AI model is equipped with natural language processing and image recognition technologies, and is capable of generating appropriate tasks based on the input information.

[1058] Terminal

[1059] The terminal is a device installed in a physical store that provides an interface between the patient and the system. Its main functions are as follows:

[1060] Presentation of cognitive training tasks

[1061] The device presents the cognitive training tasks sent from the server to the patient, for example, using a tablet or dedicated smart display.

[1062] Collecting patient responses

[1063] The device records the answers and responses the patient gives to the training tasks and sends them to a server, where they are used to generate feedback.

[1064] feedback

[1065] Generate feedback

[1066] The server analyzes the collected responses from the patient and generates feedback to adjust the content of the next training session, which is then presented to the patient via their device.

[1067] Providing feedback

[1068] The terminal displays the feedback sent from the server to the patient, which provides guidance for the next training session.

[1069] Specific examples

[1070] For example, a 70-year-old patient might use a terminal in a brick-and-mortar store and enter the following information:

[1071] Name: Taro

[1072] Age: 70

[1073] Hobbies: Gardening

[1074] Previous experience: Teacher

[1075] This information is sent to a server, which uses a generative AI model to generate challenges like this:

[1076] Think back to places you've traveled to in the past.

[1077] Think back to the names of your friends from school.

[1078] Think back to the title of your favorite movie.

[1079] The device presents the task to the patient, and when the patient enters their answer, the response is sent back to the server, which analyzes it and generates feedback such as, "In your next training session, try a more detailed task based on your past experiences," which is displayed on the device.

[1080] This series of processes allows patients to continuously engage in personalized cognitive training tasks, which is expected to maintain and improve cognitive function.

[1081] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1082] Step 1:

[1083] The device accepts basic information from the user. This information includes name, age, hobbies, past experiences, etc. The device then sends this input data to the server. The input data is in JSON format, for example. Specifically, the user enters information into a form on the tablet and presses the send button, which sends the data to the server.

[1084] Step 2:

[1085] The server saves the received basic information in the database. In this step, the data is formatted to store the received data in the database in the appropriate format. Specifically, the server connects to the database and saves the received data using an INSERT statement.

[1086] Step 3:

[1087] The device requests the server to generate cognitive training tasks. The server uses the generative AI model to generate personalized cognitive training tasks based on the stored basic information. This uses natural language processing and image recognition technologies. The generated tasks are sent to the device in JSON format. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves the generated tasks.

[1088] Step 4:

[1089] The device presents the cognitive training tasks received from the server to the user. Specifically, the tasks are displayed on the tablet's display. The user then works on the presented tasks and inputs their answers.

[1090] Step 5:

[1091] The device collects the answers and reactions entered by the user and sends them to the server. At this time, the user's answer data is sent in JSON format. Specifically, the device collects the data entered in the input form and sends it to the server when the send button is pressed.

[1092] Step 6:

[1093] The server analyzes the collected answers and reactions and generates feedback to adjust the next training content. This uses natural language processing technology. The generated feedback is sent to the device in JSON format. Specifically, the server analyzes the answer data, generates appropriate feedback, and sends it.

[1094] Step 7:

[1095] The device then presents the feedback received from the server to the user, allowing the user to work on the next assignment based on this feedback. Specifically, the feedback message is displayed on the tablet screen.

[1096] 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.

[1097] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and further combines it with an emotion engine that recognizes the user's emotions. This system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[1098] System Overview

[1099] Server: This performs the core processing of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on the user's basic information. It also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[1100] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[1101] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[1102] Program processing

[1103] 1. Initial setup and user information entry

[1104] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.). The terminal confirms the input information and sends it as a request to the server.

[1105] 2. Storage of User Information

[1106] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[1107] 3. Generation of cognitive training tasks

[1108] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[1109] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[1110] 4. Present and execute the task

[1111] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[1112] User: Works on the tasks presented to them. For example, they train by answering quizzes or recalling nostalgic places and events.

[1113] 5. Emotional state analysis using the emotion engine

[1114] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends it to the server.

[1115] Server: Analyzes the collected data using an emotion engine and recognizes the patient's emotional state (e.g., joy, sadness, excitement, etc.).

[1116] 6. Collecting user reaction and sentiment data

[1117] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[1118] 7. Generate feedback

[1119] Server: Analyzes the user's responses, reactions, and emotional state and generates feedback to adjust the next training content. The feedback includes appropriate advice based on the patient's cognitive and emotional state using artificial intelligence and emotion analysis technologies.

[1120] Server: Sends the generated feedback to the device.

[1121] 8. Viewing Feedback

[1122] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[1123] Specific examples

[1124] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[1125] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1126] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[1127] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[1128] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[1129] The processing flow will be explained below.

[1130] Step 1:

[1131] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.), allowing the system to collect detailed data related to the patient. The input information is verified and sent as a request to the server.

[1132] Step 2:

[1133] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal, thereby informing the caregiver that the information has been saved successfully.

[1134] Step 3:

[1135] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. This request includes the patient's basic information.

[1136] Step 4:

[1137] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[1138] Step 5:

[1139] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[1140] Step 6:

[1141] User: Works on presented cognitive training tasks, such as recalling a student's name or answering questions about past events.

[1142] Step 7:

[1143] Device: While the patient is working on the task, it collects voice and facial expression data and sends them to the server. This data is used to analyze the patient's emotional state.

[1144] Step 8:

[1145] Server: Analyzes collected voice and facial expression data using an emotion engine to recognize the patient's emotional state (e.g., joy, sadness, excitement, etc.). Stores the recognition results for further processing.

[1146] Step 9:

[1147] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[1148] Step 10:

[1149] Server: Analyzes the user's responses, reactions, and emotional state using artificial intelligence and emotion analysis technology, and generates feedback to adjust the next training content based on the patient's cognitive and emotional state.

[1150] Step 11:

[1151] Server: Sends the generated feedback to the device. The feedback includes advice based on the cognitive training content and emotional state.

[1152] Step 12:

[1153] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[1154] Specific examples

[1155] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[1156] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1157] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[1158] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[1159] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[1160] Example 2

[1161] 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."

[1162] It has been difficult with current technologies to provide individually optimized cognitive training tasks for patients with cognitive decline, and to properly understand the patient's emotional state and provide feedback based on that. Furthermore, a system that can be easily used by caregivers and family members is needed.

[1163] 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.

[1164] In this invention, the server includes means for inputting basic information of a patient with cognitive decline, means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information, means for presenting the generated cognitive training tasks to the patient, means for collecting the patient's answers, reactions, voice data, and facial expression data, means for analyzing the collected answers, reactions, voice data, and facial expression data to recognize the patient's emotional state, means for adjusting the next training content based on the analysis results and generating feedback, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and feedback based on the patient's emotional state.

[1165] "Basic patient information" refers to the name, age, stage of dementia, preferences, past experiences, etc. of the patient whose cognitive function is declining.

[1166] "Personalized cognitive training tasks" refer to tasks for improving cognitive function that are individually optimized using artificial intelligence based on the patient's basic information.

[1167] "Artificial intelligence" refers to technologies that enable computers to mimic human intelligence, including natural language processing and image recognition techniques.

[1168] "Natural language processing" refers to the technology that enables computers to understand, generate, and respond to human language.

[1169] "Image recognition technology" refers to the technology that allows computers to analyze image and video data and identify patterns and objects.

[1170] "Means for presenting training tasks" refers to a mechanism for displaying cognitive training tasks generated through a terminal to a patient.

[1171] "Means for collecting responses" refers to a mechanism for recording and storing the responses or responses provided by the patient to the training tasks.

[1172] "Audio data" refers to data that records the sounds made when a patient is performing a task.

[1173] "Facial expression data" refers to data recording the facial expressions observed when a patient is performing a task.

[1174] "Means for recognizing emotional state" refers to a mechanism for analyzing collected voice data and facial expression data to identify the patient's emotional state (happiness, sadness, excitement, etc.).

[1175] "Means for generating feedback" refers to a mechanism for adjusting the next training content and providing appropriate advice and guidance based on the patient's answers, reactions, and emotional state.

[1176] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and also combines an emotion engine that recognizes the user's emotions. The system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[1177] System Overview

[1178] Server: This is the core processing part of the system, and uses artificial intelligence to generate personalized cognitive training tasks based on the user's basic information. The server also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[1179] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[1180] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[1181] Program processing

[1182] The system works through the following steps:

[1183] 1. Initial setup and user information entry: Using the terminal, the caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.) and sends the entered information to the server.

[1184] 2. Save user information: The server saves the received user information in the database and sends a success message to the terminal.

[1185] 3. Generation of cognitive training tasks: Based on requests from the device, the server generates personalized cognitive training tasks using artificial intelligence (natural language processing and image recognition technology). The generated tasks are sent from the server to the device.

[1186] 4. Presentation and execution of the task: The device presents the generated cognitive training task to the patient, who then completes the task with the support of a caregiver.

[1187] 5. Analysis of emotional state using emotion engine: The device collects voice and facial expression data while the patient is working on the task and sends it to the server. The server analyzes this data using the emotion engine and recognizes the patient's emotional state.

[1188] 6. Collecting user response and emotional data: The device records the patient's answers and responses, as well as their emotional state analyzed by the emotion engine, and sends them to the server.

[1189] 7. Feedback Generation: The server analyzes the received data and generates feedback to adjust the next training content. The generated feedback is sent to the device.

[1190] 8. Displaying feedback: The device presents the received feedback to the patient and provides guidance for the next training session.

[1191] Hardware and software used

[1192] Hardware: A typical computer, tablet, or smartphone can be used as the device. The device's built-in microphone and camera are used to collect voice and facial expression data.

[1193] Software: The server is built using programming languages ​​such as Python or Java, and the database uses MySQL or PostgreSQL, etc. For artificial intelligence, TensorFlow or PyTorch is used for natural language processing, and Amazon Rekognition or Google Cloud Vision is used for image recognition.

[1194] Specific examples

[1195] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. This information is entered into a device and sent to a server. The server uses artificial intelligence to generate the following tasks:

[1196] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1197] The patient completes the tasks displayed on the device and inputs the names of their students and other anecdotes. Voice and facial expression data collected while the patient is working on the tasks is sent to a server, where an emotion engine analyzes this data to recognize the patient's emotional state.

[1198] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and can be used to improve the next training session.

[1199] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[1200] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1201] Step 1:

[1202] Initial setup and entering user information

[1203] Terminal: The caregiver enters basic information about the patient (name, age, progression of dementia, preferences, past experiences, etc.) through the terminal's user interface. After confirming the entered information, the caregiver presses the "Send" button.

[1204] Input: Patient basic information.

[1205] Output: The request to the server.

[1206] Specific operation: The terminal checks the entered basic information of the patient and sends it as an HTTP request to the server.

[1207] Step 2:

[1208] Saving user information

[1209] Server: Saves the received user information in the database. If the save is successful, generates a success message stating "User information has been saved successfully" and sends it to the terminal.

[1210] Input: Basic patient information sent from the terminal.

[1211] Output: User information saved in database, success message.

[1212] Specific operation: The server analyzes the received basic information of the patient and inserts it into the database in the appropriate format. If the insertion is successful, it generates a message indicating success and sends it to the terminal.

[1213] Step 3:

[1214] Generation of cognitive training tasks

[1215] Terminal: Requests the server to generate cognitive training tasks based on basic information about a specific patient.

[1216] Input: A request based on basic patient information.

[1217] Output: The request to the server.

[1218] Specific operation: The terminal sends a request to the server to generate cognitive training tasks based on the stored basic information of the patient.

[1219] Server: Uses artificial intelligence to generate personalized cognitive training tasks.

[1220] Input: Patient basic information.

[1221] Output: The generated cognitive training task.

[1222] Specific operation: The server uses natural language processing and image recognition technology to generate cognitive training tasks based on the patient's basic information and sends them to the terminal.

[1223] Step 4:

[1224] Presenting and executing the task

[1225] Terminal: Presents the generated cognitive training tasks to the patient and encourages them to complete the tasks.

[1226] Input: Generated cognitive training tasks.

[1227] Output: The issue displayed in the user interface.

[1228] Specific operation: The terminal displays the cognitive training task received from the server on the screen and asks the patient to complete the task.

[1229] User: Works on the assignment and inputs answers and responses via the device.

[1230] Input: Answers and responses to the assignment.

[1231] Output: Answers or responses typed into the terminal.

[1232] Specific operations: The patient enters answers to the questions that are displayed one after another, and these are recorded on the terminal.

[1233] Step 5:

[1234] Emotional state analysis using emotion engine

[1235] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends them to the server.

[1236] Input: Patient's voice and facial expression data.

[1237] Output: Sending voice data and facial expression data to the server.

[1238] Specific operation: The device's built-in microphone and camera collect voice and facial expression data, which are then sent to the server.

[1239] Server: Analyzes these data using an emotion engine to recognize the patient's emotional state.

[1240] Input: Collected speech and facial expression data.

[1241] Output: Parsed emotional state.

[1242] Specific operation: The server uses voice analysis software and image recognition software to recognize the patient's emotional state (happiness, sadness, excitement, etc.) based on the collected data.

[1243] Step 6:

[1244] Collecting user reaction and sentiment data

[1245] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends these to the server.

[1246] Input: Patient's answers, reactions, and emotional state.

[1247] Output: Data sent to the server.

[1248] Specific operation: The device records the user's answers and reactions and sends them to the server along with pre-analyzed emotional data.

[1249] Step 7:

[1250] Generate feedback

[1251] Server: Analyzes the received answers, reactions, and emotional state data and generates feedback to adjust the next training content.

[1252] Input: User answers, reactions, and sentiment data.

[1253] Output: The generated feedback.

[1254] How it works: The server uses artificial intelligence technology to adjust the next training session based on the collected data and generate appropriate feedback.

[1255] Server: Sends the generated feedback to the device.

[1256] Input: Generated feedback.

[1257] Output: Data sent to the terminal.

[1258] Specific operation: The server sends the generated feedback to the device and reflects it in the next training content.

[1259] Step 8:

[1260] View Feedback

[1261] Terminal: Presents the received feedback to the patient.

[1262] Input: Feedback from the server.

[1263] Output: Feedback displayed in the user interface.

[1264] Specific behavior: The device displays the received feedback on the screen and provides guidance to patients and caregivers for the next training session.

[1265] These steps enable the system to provide individually optimized cognitive training tasks and feedback based on emotional state.

[1266] (Application example 2)

[1267] 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."

[1268] Conventional cognitive training systems do not adequately consider the individual cognitive function and emotional state of dementia patients, making it difficult to provide effective training. Furthermore, when it comes to training for individuals engaged in specific occupations, such as security staff, there is a lack of technology to provide cognitive training tailored to the characteristics of their jobs. This has led to a demand for performance improvement through emotional management as well as cognitive function improvement.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1270] In this invention, the server includes: means for inputting basic information of a patient with cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and reactions from the patient; means for analyzing the collected answers and reactions and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; means for collecting voice data and facial expression data and analyzing them using an emotion engine; and means for adjusting the training content based on the analyzed emotional state. This makes it possible to provide effective training that integrates cognitive function improvement and emotion management to dementia patients and individuals engaged in specific occupations.

[1271] "Cognitive function" is a general term for an individual's intellectual abilities to understand and remember information, solve problems, and make decisions.

[1272] "Basic patient information" refers to information specific to an individual patient, such as name, age, stage of dementia, preferences, and past experiences.

[1273] "Artificial intelligence" is a technology that allows computer systems to mimic human intellectual behavior, analyze complex data, and make decisions, and includes natural language processing and image recognition technologies.

[1274] "Personalized cognitive training tasks" are tasks for improving cognitive function that are optimized for each individual patient based on basic information about that individual patient.

[1275] A "generated cognitive training task" is a specific cognitive training task that is generated by artificial intelligence and presented to a patient.

[1276] "Patient responses and reactions" refer to the answers and behaviors that patients show in response to cognitive training tasks.

[1277] "Feedback" is information that includes guidelines and advice for adjusting the next training content based on collected patient responses and reactions.

[1278] "Audio data" is a recording of the audio collected as the patient performs the task.

[1279] "Facial expression data" refers to facial expression data recorded while the patient is performing the task.

[1280] "Emotion Engine" is an artificial intelligence technology that analyzes voice data and facial expression data to recognize an individual's emotional state.

[1281] "Security staff" refers to security guards and crime prevention officers who work to ensure the safety of facilities and events.

[1282] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The specific roles and processing procedures of each component are described below.

[1283] Server Roles and Operations

[1284] The server is the central processing unit for the entire system, and it uses the following means:

[1285] 1. Management of User Information

[1286] The server stores the basic information submitted by the user (name, age, years of experience, preferences, past experience) in a database, which will be used for further processing.

[1287] 2. Generation of cognitive training tasks

[1288] It uses artificial intelligence to generate personalized cognitive training exercises based on individual user information, and also uses natural language processing and image recognition technology to provide accurate and effective exercises.

[1289] 3. Emotion analysis

[1290] The emotional engine analyzes the user's emotional state based on the voice and facial expression data collected during the task, allowing the system to understand the user's mental state and reflect this in the training content.

[1291] 4. Generate feedback

[1292] Based on the user's responses and the results of sentiment analysis, feedback is generated to adjust the next training task and sent to the device.

[1293] Terminal roles and processing

[1294] The terminal acts as a user interface and sends and receives data to the server using the following means:

[1295] 1. Initial Setup

[1296] Caregivers and users themselves use a terminal to enter basic information and send it to the server.

[1297] 2. Presenting training tasks

[1298] The generated cognitive training tasks are presented to the user and progress is displayed in real time.

[1299] 3. Recording the reaction

[1300] Voice data and facial expression data are collected during task execution and sent to a server.

[1301] 4. Providing feedback

[1302] The feedback sent from the server is displayed to the user to provide guidance for the next training session.

[1303] User Roles and Actions

[1304] The user engages in cognitive training tasks using the following means:

[1305] 1. Provision of information

[1306] Enter basic information during initial setup.

[1307] 2. Execute the assignment

[1308] The person engages in cognitive training tasks presented on the device and provides answers and responses.

[1309] 3. Receiving feedback

[1310] Review the feedback and incorporate it into your next training session.

[1311] Specific examples

[1312] For example, consider a scenario in which a 50-year-old security officer is starting new training. This officer has 20 years of experience, and previously worked as a police officer. His hobby is fishing. This information is entered into a terminal and sent to the server. The server generates the following challenge based on this information:

[1313] Think back to the most memorable incident you encountered in the course of your work. Reflect on how you felt and responded to the incident.

[1314] An example prompt for this assignment is:

[1315] "Prompt: A 50-year-old veteran security officer has 20 years of work experience. He recently underwent new training. His favorite hobby is fishing, and he previously worked as a police officer. Generate a cognitive training task based on his information."

[1316] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1317] Step 1:

[1318] The terminal is started up, and the caregiver or security staff enters basic information (such as name, age, years of experience, preferences, and past experience) through the user interface. The entered basic information is sent from the terminal to the server as a request.

[1319] Input: Basic information such as name, age, years of experience, preferences, past experience, etc.

[1320] Output: The request sent to the server

[1321] Step 2:

[1322] The server saves the received basic information in the database. If the save is successful, it generates a save success message and sends it to the terminal.

[1323] Input: Basic information sent from the device

[1324] Output: Save successful message

[1325] Step 3:

[1326] The device requests the server to generate a cognitive training task based on the basic information. The request includes the basic information.

[1327] Input: Basic information

[1328] Output: Request to server

[1329] Step 4:

[1330] The server uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information, and the generated tasks are sent from the server to the device.

[1331] Input: Basic information and request

[1332] Output: Cognitive training task

[1333] Step 5:

[1334] The device presents the generated cognitive training tasks to the user, who then engages in the tasks and inputs their answers and reactions into the device.

[1335] Input: Cognitive training task

[1336] Output: User's response or response

[1337] Step 6:

[1338] While the device is working on the task, it collects voice and facial expression data and sends it to a server.

[1339] Input: User's voice data and facial expression data

[1340] Output: Send data to the server

[1341] Step 7:

[1342] The server analyzes the collected data using an emotion engine to recognize the user's emotional state.

[1343] Input: Voice data and facial expression data

[1344] Output: Emotional state recognition result

[1345] Step 8:

[1346] The server generates feedback based on the user's answers, reactions, and emotional state to adjust the next training session and sends it to the device.

[1347] Input: User answers, reactions, and emotional states

[1348] Output: Feedback

[1349] Step 9:

[1350] The device presents the feedback received from the server to the user and provides guidelines for reflecting it in the next training session.

[1351] Input: Feedback

[1352] Output: Feedback presented to the user

[1353] Through these steps, the system will be able to provide effective training for dementia patients and security staff that integrates cognitive enhancement and emotional state management.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] [Fourth embodiment]

[1358] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1359] 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.

[1360] 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).

[1361] 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.

[1362] 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.

[1363] 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).

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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."

[1371] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline. This system is easy to use for caregivers and family members and supports the maintenance and improvement of patients' cognitive function.

[1372] System Overview

[1373] Server: The core processing unit of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on basic user information.

[1374] Terminal: Serves as the user interface, collecting information from the dementia patient and caregiver, displaying training tasks, and recording user responses.

[1375] Users: People with dementia and their caregivers use the system and complete the training tasks.

[1376] Program processing

[1377] 1. Initial setup and user information entry

[1378] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences), allowing the system to collect detailed data related to the patient. The input information is then sent to the server.

[1379] 2. Storage of User Information

[1380] Server: Stores the received user information in a database. This allows patient information to be captured and used as the basis for generating future training tasks.

[1381] 3. Generation of cognitive training tasks

[1382] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information.

[1383] Server: Uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information. The generated tasks are sent to the device and presented to the user.

[1384] 4. Present and execute the task

[1385] Device: The device presents the generated training tasks to the dementia patient. For example, it displays quizzes and tasks that prompt recollection related to the patient's favorite hobbies or past work history when they were younger.

[1386] User: Works through the presented tasks and enters answers and responses.

[1387] 5. Collecting user responses

[1388] Terminal: Records the patient's answers and responses to the tasks and sends them to the server.

[1389] 6. Generate feedback

[1390] Server: Analyzes collected user responses and generates feedback to adjust the next training content. Natural language processing technology is used for the analysis, and appropriate interpretations are given to each response.

[1391] Server: Sends the generated feedback to the device.

[1392] 7. Viewing Feedback

[1393] Device: The generated feedback is displayed to the patient and reflected in the next training assignment.

[1394] Specific examples

[1395] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[1396] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1397] The patient works on the task displayed on the device, recalling and entering the student's name and anecdote. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time." This feedback is displayed on the device and is used to improve the next training task.

[1398] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and also contributes to reducing the burden on caregivers.

[1399] The processing flow will be explained below.

[1400] Step 1:

[1401] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, progression of dementia, preferences, past experiences, etc.). The input information is confirmed and sent to the server as a request.

[1402] Step 2:

[1403] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[1404] Step 3:

[1405] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[1406] Step 4:

[1407] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[1408] Step 5:

[1409] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[1410] Step 6:

[1411] User: Engages in cognitive training tasks, such as answering quizzes or recalling familiar places or events.

[1412] Step 7:

[1413] Terminal: Records the answers and responses entered by the patient to the tasks. The recorded data is sent to the server.

[1414] Step 8:

[1415] Server: Analyzes the user's responses and reactions using artificial intelligence technology to generate feedback to adjust the next training session based on the patient's performance and reactions.

[1416] Step 9:

[1417] Server: Sends generated feedback to the device, which evaluates the patient's effort and includes specific advice for improving the next training session.

[1418] Step 10:

[1419] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[1420] Example 1

[1421] 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."

[1422] Providing individually optimized cognitive training tasks for patients with declining cognitive function requires a method for generating tasks that correspond to each patient's unique background information and progress. It is also important that the system be easy to use for caregivers and patients themselves. However, existing systems cannot meet these requirements, making it difficult to effectively support patients in maintaining and improving their cognitive function. This issue must be resolved.

[1423] 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.

[1424] In this invention, the server includes means for providing a user interface through which a caregiver inputs basic information about a patient, means for storing the input basic information in a database, means for generating personalized cognitive training tasks based on the patient's basic information using artificial intelligence, means for presenting the generated training tasks to the patient, means for collecting answers and responses from the patient, means for analyzing the collected answers and responses and generating feedback for adjusting the content of the next training, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and to generate and present feedback that continuously improves effectiveness.

[1425] The "user interface" is the part of the system that includes the screens and instructions that allow caregivers to enter basic patient information.

[1426] "Basic information" refers to data necessary for individual optimization of cognitive training tasks, such as the patient's name, age, stage of dementia, preferences, and past experiences.

[1427] "Database" refers to an information management device within the system that stores and manages input basic information and collected answers and responses.

[1428] "Artificial intelligence" is a technology that uses natural language processing and image recognition technology to analyze data and make decisions and generate problems that are closer to those of humans.

[1429] "Cognitive training tasks" refer to specific training content generated by artificial intelligence with the aim of maintaining and improving a patient's cognitive function.

[1430] "Feedback" refers to information and evaluations that are used to adjust the content of the next training based on the results of the patient's cognitive training tasks.

[1431] "Personalized" means having a format and content optimized for a specific patient based on the patient's individual basic information.

[1432] "Answer or response" refers to the response results, such as input, behavior, and evaluation, that a patient makes in response to the presented cognitive training task.

[1433] "Means for collecting" refers to a method or device for recording the patient's answers and responses through the user interface and storing them in a database.

[1434] This invention is a system that provides individually optimized cognitive training tasks to patients with cognitive decline, and is easy for caregivers and family members to use, helping to maintain and improve patients' cognitive function. The system consists of three elements: a server, a terminal, and a user.

[1435] Server: Performs the central processing of the system. The server stores user information in a database and uses artificial intelligence (generative AI model) to generate personalized cognitive training tasks based on the user's basic information. It then analyzes the collected user responses and generates feedback to adjust the next training content based on that information. Specifically, the server uses the following hardware and software. For hardware, it uses, for example, AWS (Amazon Web Services) or Google Cloud Platform. For software, it uses MySQL or PostgreSQL for database management, and OpenAI GPT-4 or Hugging Face Transformers for the artificial intelligence model.

[1436] Terminal: This terminal functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, and recording user responses. Examples of terminals include iPads and Android tablets. Specifically, the terminal communicates with the server, inputs and collects the necessary data via the user interface, and displays the training tasks. Data entered into the terminal is sent to the server in real time, where it is stored and analyzed.

[1437] Users: Dementia patients and their caregivers use the system. Users access the system through a terminal and work on training tasks. The users (patients) input their responses to the tasks displayed on the terminal, and their responses are recorded on the terminal and sent to the server.

[1438] Specific examples

[1439] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server generates a cognitive training task by inputting the following prompt sentences into a generative AI model:

[1440] "Generate cognitive training tasks for a 60-year-old patient with early-stage dementia based on his gardening hobby and past experience as a teacher."

[1441] The task generated by the AI ​​based on this prompt is, "Please remember the names of the students you have taught. Also, please think back on their personalities and memorable moments." The patient works on this task, recalling and inputting the names of the students and stories they shared. These responses are recorded on the device and sent to the server. The server analyzes this data and generates feedback such as, "Great! Let's try a more difficult task next time," which is displayed on the device.

[1442] This system will help maintain and improve the memory and cognitive functions of dementia patients, and also contribute to reducing the burden on caregivers.

[1443] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1444] Step 1:

[1445] Terminal: The caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, and past experiences) through the terminal's user interface. This information is entered in a form and sent to the server in one go by pressing the "Send" button.

[1446] * Input: Patient's basic information

[1447] *Output: Basic information data sent to the server

[1448] Step 2:

[1449] Server: Stores the received basic information in a database using an SQL query, where data corresponding to each field is saved in the appropriate table (e.g., "INSERT INTO users (name, age, condition, preferences) VALUES (...)").

[1450] * Input: Basic information data sent from the device

[1451] * Output: Patient information stored in the database

[1452] Step 3:

[1453] Terminal: The caregiver requests the server to generate a cognitive training task through the terminal. The user interface has a "Generate Task" button, and clicking this button sends the request to the server.

[1454] * Input: Issue creation request

[1455] *Output: Request data is sent to the server

[1456] Step 4:

[1457] Server: Upon receiving the request, the server uses an artificial intelligence model (e.g., a generative AI model) to generate a personalized cognitive training task based on the basic information. It generates a prompt (e.g., "Please generate a cognitive training task for a 60-year-old person with early-stage dementia based on his gardening hobby and past experience as a teacher") and inputs this prompt into the AI ​​model to generate the task. The generated task is then sent to the device via the server's API.

[1458] * Input: Basic information, prompt text

[1459] * Output: Generated training tasks

[1460] Step 5:

[1461] Device: Generated training tasks are displayed on the device. The user interface dashboard lists new tasks and provides them in a format for the patient to complete (e.g., quiz or open-ended).

[1462] * Input: Assignment data sent from the server

[1463] * Output: Assignment displayed on the device

[1464] Step 6:

[1465] User: The patient works on tasks displayed on the device and inputs their answers. For example, to the task, "Remember the names of the students you have taught and reflect on their personalities and memorable moments," the patient uses the device's keyboard to input their answers.

[1466] * Input: Patient's response

[1467] * Output: Answer data recorded on the device

[1468] Step 7:

[1469] Terminal: The patient's answers and reactions to the tasks are recorded on the terminal, temporarily saved, and then sent to the server.

[1470] * Input: Patient response data

[1471] *Output: Reaction data sent to the server

[1472] Step 8:

[1473] Server: The received response data is stored in a database (e.g., "INSERT INTO responses (user_id, response) VALUES (...)"). The collected data is then analyzed and feedback is generated to adjust the next training content. At this time, the response data and a supplementary prompt (e.g., "Please rate this response and suggest the next training content") are input to the AI ​​model. The generated feedback is then sent back to the device via the API.

[1474] * Input: Patient response data, supplementary prompt text

[1475] * Output: Generated feedback

[1476] Step 9:

[1477] Device: The generated feedback is displayed to the patient. The feedback message is displayed on the dashboard of the user interface and is reflected in the next training challenge. For example, a positive message such as "Great! Let's try a more difficult challenge next time" is displayed.

[1478] * Input: Feedback sent from the server

[1479] * Output: Feedback displayed on the device

[1480] (Application example 1)

[1481] 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."

[1482] Conventionally, cognitive training for patients with cognitive decline has often not been individually optimized, making effective training difficult. In particular, there has been a lack of systems for dementia prevention in brick-and-mortar stores, resulting in a lack of environments where patients can easily train in their daily lives. Furthermore, it has been difficult to generate personalized tasks based on the patient's past experiences and preferences, making it difficult to maintain motivation.

[1483] 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.

[1484] In this invention, the server includes: means for inputting basic information of a patient suffering from cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and responses from the patient; means for analyzing the collected answers and responses and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; and means for providing the patient with personalized cognitive training tasks via a terminal installed in a physical store and recording the patient's responses. This enables patients suffering from cognitive decline to engage in individually optimized cognitive training tasks even in a physical store, providing an environment where they can easily train in their daily lives.

[1485] "Cognitive decline" refers to a decline in cognitive abilities such as memory, attention, and comprehension.

[1486] "Basic patient information" refers to information such as the name, age, progression of dementia, hobbies, and past experiences of patients with cognitive decline.

[1487] "Artificial intelligence" refers to technology that mimics human intelligence on a computer, independently learning from past data to solve problems.

[1488] "Personalized cognitive training tasks" refer to tasks designed to improve cognitive function that are optimized for a specific patient based on basic information about the individual patient.

[1489] "Generating means" refers to the process or device that uses artificial intelligence to create specific tasks or content.

[1490] "Presenting means" refers to the process or device for displaying or communicating the generated tasks and feedback to the patient.

[1491] "Patient response" refers to the responses or behaviors that a patient exhibits in response to cognitive training tasks.

[1492] "Means of collection" refers to the process or device used to record and store patient responses.

[1493] "Feedback" refers to providing information to adjust the next training session based on the collected data.

[1494] "Brick and mortar store" means a physical retail establishment that is open to the public.

[1495] "Terminal" refers to an electronic device or computer used to input or output information.

[1496] This invention is a system for providing personalized cognitive training exercises to patients with cognitive decline. This system utilizes artificial intelligence to generate cognitive training exercises optimized for each patient based on basic information about the patient, and provides them via a terminal installed in a physical store. The configuration and operation of this system are described in detail below.

[1497] server

[1498] The server is responsible for the core functions of this system, mainly fulfilling the following roles:

[1499] Save basic information

[1500] The server stores basic information about the patient (such as name, age, hobbies, and past experiences) received through the terminal in a database. This data is used to generate future training tasks.

[1501] Generation of cognitive training tasks

[1502] The server generates personalized cognitive training tasks using a generative AI model based on the stored basic patient information. The generative AI model is equipped with natural language processing and image recognition technologies, and is capable of generating appropriate tasks based on the input information.

[1503] Terminal

[1504] The terminal is a device installed in a physical store that provides an interface between the patient and the system. Its main functions are as follows:

[1505] Presentation of cognitive training tasks

[1506] The device presents the cognitive training tasks sent from the server to the patient, for example, using a tablet or dedicated smart display.

[1507] Collecting patient responses

[1508] The device records the answers and responses the patient gives to the training tasks and sends them to a server, where they are used to generate feedback.

[1509] feedback

[1510] Generate feedback

[1511] The server analyzes the collected responses from the patient and generates feedback to adjust the content of the next training session, which is then presented to the patient via their device.

[1512] Providing feedback

[1513] The terminal displays the feedback sent from the server to the patient, which provides guidance for the next training session.

[1514] Specific examples

[1515] For example, a 70-year-old patient might use a terminal in a brick-and-mortar store and enter the following information:

[1516] Name: Taro

[1517] Age: 70

[1518] Hobbies: Gardening

[1519] Previous experience: Teacher

[1520] This information is sent to a server, which uses a generative AI model to generate challenges like this:

[1521] Think back to places you've traveled to in the past.

[1522] Think back to the names of your friends from school.

[1523] Think back to the title of your favorite movie.

[1524] The device presents the task to the patient, and when the patient enters their answer, the response is sent back to the server, which analyzes it and generates feedback such as, "In your next training session, try a more detailed task based on your past experiences," which is displayed on the device.

[1525] This series of processes allows patients to continuously engage in personalized cognitive training tasks, which is expected to maintain and improve cognitive function.

[1526] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1527] Step 1:

[1528] The device accepts basic information from the user. This information includes name, age, hobbies, past experiences, etc. The device then sends this input data to the server. The input data is in JSON format, for example. Specifically, the user enters information into a form on the tablet and presses the send button, which sends the data to the server.

[1529] Step 2:

[1530] The server saves the received basic information in the database. In this step, the data is formatted to store the received data in the database in the appropriate format. Specifically, the server connects to the database and saves the received data using an INSERT statement.

[1531] Step 3:

[1532] The device requests the server to generate cognitive training tasks. The server uses the generative AI model to generate personalized cognitive training tasks based on the stored basic information. This uses natural language processing and image recognition technologies. The generated tasks are sent to the device in JSON format. Specifically, the server inputs prompt sentences into the AI ​​model and retrieves the generated tasks.

[1533] Step 4:

[1534] The device presents the cognitive training tasks received from the server to the user. Specifically, the tasks are displayed on the tablet's display. The user then works on the presented tasks and inputs their answers.

[1535] Step 5:

[1536] The device collects the answers and reactions entered by the user and sends them to the server. At this time, the user's answer data is sent in JSON format. Specifically, the device collects the data entered in the input form and sends it to the server when the send button is pressed.

[1537] Step 6:

[1538] The server analyzes the collected answers and reactions and generates feedback to adjust the next training content. This uses natural language processing technology. The generated feedback is sent to the device in JSON format. Specifically, the server analyzes the answer data, generates appropriate feedback, and sends it.

[1539] Step 7:

[1540] The device then presents the feedback received from the server to the user, allowing the user to work on the next assignment based on this feedback. Specifically, the feedback message is displayed on the tablet screen.

[1541] 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.

[1542] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and further combines it with an emotion engine that recognizes the user's emotions. This system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[1543] System Overview

[1544] Server: This performs the core processing of the system. It uses artificial intelligence to generate personalized cognitive training tasks and feedback based on the user's basic information. It also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[1545] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[1546] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[1547] Program processing

[1548] 1. Initial setup and user information entry

[1549] Terminal: Through the user interface, the caregiver inputs basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.). The terminal confirms the input information and sends it as a request to the server.

[1550] 2. Storage of User Information

[1551] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal to inform the caregiver that the information has been saved successfully.

[1552] 3. Generation of cognitive training tasks

[1553] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. The request includes the patient's basic information.

[1554] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[1555] 4. Present and execute the task

[1556] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[1557] User: Works on the tasks presented to them. For example, they train by answering quizzes or recalling nostalgic places and events.

[1558] 5. Emotional state analysis using the emotion engine

[1559] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends it to the server.

[1560] Server: Analyzes the collected data using an emotion engine and recognizes the patient's emotional state (e.g., joy, sadness, excitement, etc.).

[1561] 6. Collecting user reaction and sentiment data

[1562] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[1563] 7. Generate feedback

[1564] Server: Analyzes the user's responses, reactions, and emotional state and generates feedback to adjust the next training content. The feedback includes appropriate advice based on the patient's cognitive and emotional state using artificial intelligence and emotion analysis technologies.

[1565] Server: Sends the generated feedback to the device.

[1566] 8. Viewing Feedback

[1567] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[1568] Specific examples

[1569] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[1570] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1571] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[1572] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[1573] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[1574] The processing flow will be explained below.

[1575] Step 1:

[1576] Terminal: Through the user interface, the caregiver inputs basic information about the dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.), allowing the system to collect detailed data related to the patient. The input information is verified and sent as a request to the server.

[1577] Step 2:

[1578] Server: Saves the received user information in the database. If the save is successful, it sends a success message to the terminal, thereby informing the caregiver that the information has been saved successfully.

[1579] Step 3:

[1580] Terminal: Requests the server to generate cognitive training tasks based on the patient's basic information. This request includes the patient's basic information.

[1581] Step 4:

[1582] Server: Using artificial intelligence (natural language processing and image recognition technology), the server generates personalized cognitive training tasks based on user information. The generated tasks are sent from the server to the device.

[1583] Step 5:

[1584] Device: Presents the generated cognitive training tasks to the patient, allowing them to complete the tasks with the support of their caregiver.

[1585] Step 6:

[1586] User: Works on presented cognitive training tasks, such as recalling a student's name or answering questions about past events.

[1587] Step 7:

[1588] Device: While the patient is working on the task, it collects voice and facial expression data and sends them to the server. This data is used to analyze the patient's emotional state.

[1589] Step 8:

[1590] Server: Analyzes collected voice and facial expression data using an emotion engine to recognize the patient's emotional state (e.g., joy, sadness, excitement, etc.). Stores the recognition results for further processing.

[1591] Step 9:

[1592] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends them to the server.

[1593] Step 10:

[1594] Server: Analyzes the user's responses, reactions, and emotional state using artificial intelligence and emotion analysis technology, and generates feedback to adjust the next training content based on the patient's cognitive and emotional state.

[1595] Step 11:

[1596] Server: Sends the generated feedback to the device. The feedback includes advice based on the cognitive training content and emotional state.

[1597] Step 12:

[1598] The device presents the received feedback to the patient, providing guidance for the patient and caregiver on the next training session.

[1599] Specific examples

[1600] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. The patient's information is entered into a terminal and sent to a server. Based on this information, the server uses artificial intelligence to generate the following tasks:

[1601] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1602] The patient works on the task displayed on the device, recalling and entering the names of students and episodes. While the patient works on the task, voice and facial expression data are also collected by the device and sent to the server. The emotion engine analyzes this data and recognizes the patient's emotional state while working on the task.

[1603] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and is used to improve the next training task.

[1604] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[1605] Example 2

[1606] 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."

[1607] It has been difficult with current technologies to provide individually optimized cognitive training tasks for patients with cognitive decline, and to properly understand the patient's emotional state and provide feedback based on that. Furthermore, a system that can be easily used by caregivers and family members is needed.

[1608] 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.

[1609] In this invention, the server includes means for inputting basic information of a patient with cognitive decline, means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information, means for presenting the generated cognitive training tasks to the patient, means for collecting the patient's answers, reactions, voice data, and facial expression data, means for analyzing the collected answers, reactions, voice data, and facial expression data to recognize the patient's emotional state, means for adjusting the next training content based on the analysis results and generating feedback, and means for presenting the generated feedback to the patient. This makes it possible to provide individually optimized cognitive training tasks and feedback based on the patient's emotional state.

[1610] "Basic patient information" refers to the name, age, stage of dementia, preferences, past experiences, etc. of the patient whose cognitive function is declining.

[1611] "Personalized cognitive training tasks" refer to tasks for improving cognitive function that are individually optimized using artificial intelligence based on the patient's basic information.

[1612] "Artificial intelligence" refers to technologies that enable computers to mimic human intelligence, including natural language processing and image recognition techniques.

[1613] "Natural language processing" refers to the technology that enables computers to understand, generate, and respond to human language.

[1614] "Image recognition technology" refers to the technology that allows computers to analyze image and video data and identify patterns and objects.

[1615] "Means for presenting training tasks" refers to a mechanism for displaying cognitive training tasks generated through a terminal to a patient.

[1616] "Means for collecting responses" refers to a mechanism for recording and storing the responses or responses provided by the patient to the training tasks.

[1617] "Audio data" refers to data that records the sounds made when a patient is performing a task.

[1618] "Facial expression data" refers to data recording the facial expressions observed when a patient is performing a task.

[1619] "Means for recognizing emotional state" refers to a mechanism for analyzing collected voice data and facial expression data to identify the patient's emotional state (happiness, sadness, excitement, etc.).

[1620] "Means for generating feedback" refers to a mechanism for adjusting the next training content and providing appropriate advice and guidance based on the patient's answers, reactions, and emotional state.

[1621] This invention is a system that provides individually optimized cognitive training tasks for patients with cognitive decline, and also combines an emotion engine that recognizes the user's emotions. The system is easy to use for caregivers and family members, and supports the maintenance and improvement of patients' cognitive functions and understanding of their emotional states.

[1622] System Overview

[1623] Server: This is the core processing part of the system, and uses artificial intelligence to generate personalized cognitive training tasks based on the user's basic information. The server also uses an emotion engine to analyze the patient's emotional state and reflects the results in the training content.

[1624] Terminal: Functions as a user interface, collecting information from dementia patients and caregivers, displaying training tasks, recording user responses, and displaying analysis results of emotional state.

[1625] Users: Dementia patients and their caregivers use the system to complete training tasks and analyze their emotional state.

[1626] Program processing

[1627] The system works through the following steps:

[1628] 1. Initial setup and user information entry: Using the terminal, the caregiver enters basic information about the new dementia patient (such as name, age, dementia progression, preferences, past experiences, etc.) and sends the entered information to the server.

[1629] 2. Save user information: The server saves the received user information in the database and sends a success message to the terminal.

[1630] 3. Generation of cognitive training tasks: Based on requests from the device, the server generates personalized cognitive training tasks using artificial intelligence (natural language processing and image recognition technology). The generated tasks are sent from the server to the device.

[1631] 4. Presentation and execution of the task: The device presents the generated cognitive training task to the patient, who then completes the task with the support of a caregiver.

[1632] 5. Analysis of emotional state using emotion engine: The device collects voice and facial expression data while the patient is working on the task and sends it to the server. The server analyzes this data using the emotion engine and recognizes the patient's emotional state.

[1633] 6. Collecting user response and emotional data: The device records the patient's answers and responses, as well as their emotional state analyzed by the emotion engine, and sends them to the server.

[1634] 7. Feedback Generation: The server analyzes the received data and generates feedback to adjust the next training content. The generated feedback is sent to the device.

[1635] 8. Displaying feedback: The device presents the received feedback to the patient and provides guidance for the next training session.

[1636] Hardware and software used

[1637] Hardware: A typical computer, tablet, or smartphone can be used as the device. The device's built-in microphone and camera are used to collect voice and facial expression data.

[1638] Software: The server is built using programming languages ​​such as Python or Java, and the database uses MySQL or PostgreSQL, etc. For artificial intelligence, TensorFlow or PyTorch is used for natural language processing, and Amazon Rekognition or Google Cloud Vision is used for image recognition.

[1639] Specific examples

[1640] For example, consider a 60-year-old patient with early-stage dementia. His hobby is gardening, and his past work experience was as a teacher. This information is entered into a device and sent to a server. The server uses artificial intelligence to generate the following tasks:

[1641] "Recall the names of students you have taught and reflect on their personalities and great moments."

[1642] The patient completes the tasks displayed on the device and inputs the names of their students and other anecdotes. Voice and facial expression data collected while the patient is working on the tasks is sent to a server, where an emotion engine analyzes this data to recognize the patient's emotional state.

[1643] The server analyzes this data and generates feedback such as, "Great! Try a more difficult task next time." This feedback includes not only the content of the cognitive training, but also advice and words of encouragement based on the analyzed emotional state. This feedback is displayed on the device and can be used to improve the next training session.

[1644] Through this series of processes, the system aims to maintain and improve the memory and cognitive functions of dementia patients, and by taking into account the patient's emotional state, it provides more compatible training.

[1645] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1646] Step 1:

[1647] Initial setup and entering user information

[1648] Terminal: The caregiver enters basic information about the patient (name, age, progression of dementia, preferences, past experiences, etc.) through the terminal's user interface. After confirming the entered information, the caregiver presses the "Send" button.

[1649] Input: Patient basic information.

[1650] Output: The request to the server.

[1651] Specific operation: The terminal checks the entered basic information of the patient and sends it as an HTTP request to the server.

[1652] Step 2:

[1653] Saving user information

[1654] Server: Saves the received user information in the database. If the save is successful, generates a success message stating "User information has been saved successfully" and sends it to the terminal.

[1655] Input: Basic patient information sent from the terminal.

[1656] Output: User information saved in database, success message.

[1657] Specific operation: The server analyzes the received basic information of the patient and inserts it into the database in the appropriate format. If the insertion is successful, it generates a message indicating success and sends it to the terminal.

[1658] Step 3:

[1659] Generation of cognitive training tasks

[1660] Terminal: Requests the server to generate cognitive training tasks based on basic information about a specific patient.

[1661] Input: A request based on basic patient information.

[1662] Output: The request to the server.

[1663] Specific operation: The terminal sends a request to the server to generate cognitive training tasks based on the stored basic information of the patient.

[1664] Server: Uses artificial intelligence to generate personalized cognitive training tasks.

[1665] Input: Patient basic information.

[1666] Output: The generated cognitive training task.

[1667] Specific operation: The server uses natural language processing and image recognition technology to generate cognitive training tasks based on the patient's basic information and sends them to the terminal.

[1668] Step 4:

[1669] Presenting and executing the task

[1670] Terminal: Presents the generated cognitive training tasks to the patient and encourages them to complete the tasks.

[1671] Input: Generated cognitive training tasks.

[1672] Output: The issue displayed in the user interface.

[1673] Specific operation: The terminal displays the cognitive training task received from the server on the screen and asks the patient to complete the task.

[1674] User: Works on the assignment and inputs answers and responses via the device.

[1675] Input: Answers and responses to the assignment.

[1676] Output: Answers or responses typed into the terminal.

[1677] Specific operations: The patient enters answers to the questions that are displayed one after another, and these are recorded on the terminal.

[1678] Step 5:

[1679] Emotional state analysis using emotion engine

[1680] Terminal: While the patient is working on the task, it collects voice and facial expression data and sends them to the server.

[1681] Input: Patient's voice and facial expression data.

[1682] Output: Sending voice data and facial expression data to the server.

[1683] Specific operation: The device's built-in microphone and camera collect voice and facial expression data, which are then sent to the server.

[1684] Server: Analyzes these data using an emotion engine to recognize the patient's emotional state.

[1685] Input: Collected speech and facial expression data.

[1686] Output: Parsed emotional state.

[1687] Specific operation: The server uses voice analysis software and image recognition software to recognize the patient's emotional state (happiness, sadness, excitement, etc.) based on the collected data.

[1688] Step 6:

[1689] Collecting user reaction and sentiment data

[1690] Terminal: Records the answers and reactions entered by the patient to the tasks, as well as the emotional state analyzed by the emotion engine, and sends these to the server.

[1691] Input: Patient's answers, reactions, and emotional state.

[1692] Output: Data sent to the server.

[1693] Specific operation: The device records the user's answers and reactions and sends them to the server along with pre-analyzed emotional data.

[1694] Step 7:

[1695] Generate feedback

[1696] Server: Analyzes the received answers, reactions, and emotional state data and generates feedback to adjust the next training content.

[1697] Input: User answers, reactions, and sentiment data.

[1698] Output: The generated feedback.

[1699] How it works: The server uses artificial intelligence technology to adjust the next training session based on the collected data and generate appropriate feedback.

[1700] Server: Sends the generated feedback to the device.

[1701] Input: Generated feedback.

[1702] Output: Data sent to the terminal.

[1703] Specific operation: The server sends the generated feedback to the device and reflects it in the next training content.

[1704] Step 8:

[1705] View Feedback

[1706] Terminal: Presents the received feedback to the patient.

[1707] Input: Feedback from the server.

[1708] Output: Feedback displayed in the user interface.

[1709] Specific behavior: The device displays the received feedback on the screen and provides guidance to patients and caregivers for the next training session.

[1710] These steps enable the system to provide individually optimized cognitive training tasks and feedback based on emotional state.

[1711] (Application example 2)

[1712] 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."

[1713] Conventional cognitive training systems do not adequately consider the individual cognitive function and emotional state of dementia patients, making it difficult to provide effective training. Furthermore, when it comes to training for individuals engaged in specific occupations, such as security staff, there is a lack of technology to provide cognitive training tailored to the characteristics of their jobs. This has led to a demand for performance improvement through emotional management as well as cognitive function improvement.

[1714] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1715] In this invention, the server includes: means for inputting basic information of a patient with cognitive decline; means for generating personalized cognitive training tasks using artificial intelligence based on the patient's basic information; means for presenting the generated cognitive training tasks to the patient; means for collecting answers and reactions from the patient; means for analyzing the collected answers and reactions and generating feedback for adjusting the next training content; means for presenting the generated feedback to the patient; means for collecting voice data and facial expression data and analyzing them using an emotion engine; and means for adjusting the training content based on the analyzed emotional state. This makes it possible to provide effective training that integrates cognitive function improvement and emotion management to dementia patients and individuals engaged in specific occupations.

[1716] "Cognitive function" is a general term for an individual's intellectual abilities to understand and remember information, solve problems, and make decisions.

[1717] "Basic patient information" refers to information specific to an individual patient, such as name, age, stage of dementia, preferences, and past experiences.

[1718] "Artificial intelligence" is a technology that allows computer systems to mimic human intellectual behavior, analyze complex data, and make decisions, and includes natural language processing and image recognition technologies.

[1719] "Personalized cognitive training tasks" are tasks for improving cognitive function that are optimized for each individual patient based on basic information about that individual patient.

[1720] A "generated cognitive training task" is a specific cognitive training task that is generated by artificial intelligence and presented to a patient.

[1721] "Patient responses and reactions" refer to the answers and behaviors that patients show in response to cognitive training tasks.

[1722] "Feedback" is information that includes guidelines and advice for adjusting the next training content based on collected patient responses and reactions.

[1723] "Audio data" is a recording of the audio collected as the patient performs the task.

[1724] "Facial expression data" refers to facial expression data recorded while the patient is performing the task.

[1725] "Emotion Engine" is an artificial intelligence technology that analyzes voice data and facial expression data to recognize an individual's emotional state.

[1726] "Security staff" refers to security guards and crime prevention officers who work to ensure the safety of facilities and events.

[1727] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The specific roles and processing procedures of each component are described below.

[1728] Server Roles and Operations

[1729] The server is the central processing unit for the entire system, and it uses the following means:

[1730] 1. Management of User Information

[1731] The server stores the basic information submitted by the user (name, age, years of experience, preferences, past experience) in a database, which will be used for further processing.

[1732] 2. Generation of cognitive training tasks

[1733] It uses artificial intelligence to generate personalized cognitive training exercises based on individual user information, and also uses natural language processing and image recognition technology to provide accurate and effective exercises.

[1734] 3. Emotion analysis

[1735] The emotional engine analyzes the user's emotional state based on the voice and facial expression data collected during the task, allowing the system to understand the user's mental state and reflect this in the training content.

[1736] 4. Generate feedback

[1737] Based on the user's responses and the results of sentiment analysis, feedback is generated to adjust the next training task and sent to the device.

[1738] Terminal roles and processing

[1739] The terminal acts as a user interface and sends and receives data to the server using the following means:

[1740] 1. Initial Setup

[1741] Caregivers and users themselves use a terminal to enter basic information and send it to the server.

[1742] 2. Presenting training tasks

[1743] The generated cognitive training tasks are presented to the user and progress is displayed in real time.

[1744] 3. Recording the reaction

[1745] Voice data and facial expression data are collected during task execution and sent to a server.

[1746] 4. Providing feedback

[1747] The feedback sent from the server is displayed to the user to provide guidance for the next training session.

[1748] User Roles and Actions

[1749] The user engages in cognitive training tasks using the following means:

[1750] 1. Provision of information

[1751] Enter basic information during initial setup.

[1752] 2. Execute the assignment

[1753] The person engages in cognitive training tasks presented on the device and provides answers and responses.

[1754] 3. Receiving feedback

[1755] Review the feedback and incorporate it into your next training session.

[1756] Specific examples

[1757] For example, consider a scenario in which a 50-year-old security officer is starting new training. This officer has 20 years of experience, and previously worked as a police officer. His hobby is fishing. This information is entered into a terminal and sent to the server. The server generates the following challenge based on this information:

[1758] Think back to the most memorable incident you encountered in the course of your work. Reflect on how you felt and responded to the incident.

[1759] An example prompt for this assignment is:

[1760] "Prompt: A 50-year-old veteran security officer has 20 years of work experience. He recently underwent new training. His favorite hobby is fishing, and he previously worked as a police officer. Generate a cognitive training task based on his information."

[1761] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1762] Step 1:

[1763] The terminal is started up, and the caregiver or security staff enters basic information (such as name, age, years of experience, preferences, and past experience) through the user interface. The entered basic information is sent from the terminal to the server as a request.

[1764] Input: Basic information such as name, age, years of experience, preferences, past experience, etc.

[1765] Output: The request sent to the server

[1766] Step 2:

[1767] The server saves the received basic information in the database. If the save is successful, it generates a save success message and sends it to the terminal.

[1768] Input: Basic information sent from the device

[1769] Output: Save successful message

[1770] Step 3:

[1771] The device requests the server to generate a cognitive training task based on the basic information. The request includes the basic information.

[1772] Input: Basic information

[1773] Output: Request to server

[1774] Step 4:

[1775] The server uses artificial intelligence (natural language processing and image recognition technology) to generate personalized cognitive training tasks based on user information, and the generated tasks are sent from the server to the device.

[1776] Input: Basic information and request

[1777] Output: Cognitive training task

[1778] Step 5:

[1779] The device presents the generated cognitive training tasks to the user, who then engages in the tasks and inputs their answers and reactions into the device.

[1780] Input: Cognitive training task

[1781] Output: User's response or response

[1782] Step 6:

[1783] While the device is working on the task, it collects voice and facial expression data and sends it to a server.

[1784] Input: User's voice data and facial expression data

[1785] Output: Send data to the server

[1786] Step 7:

[1787] The server analyzes the collected data using an emotion engine to recognize the user's emotional state.

[1788] Input: Voice data and facial expression data

[1789] Output: Emotional state recognition result

[1790] Step 8:

[1791] The server generates feedback based on the user's answers, reactions, and emotional state to adjust the next training session and sends it to the device.

[1792] Input: User answers, reactions, and emotional states

[1793] Output: Feedback

[1794] Step 9:

[1795] The device presents the feedback received from the server to the user and provides guidelines for reflecting it in the next training session.

[1796] Input: Feedback

[1797] Output: Feedback presented to the user

[1798] Through these steps, the system will be able to provide effective training for dementia patients and security staff that integrates cognitive enhancement and emotional state management.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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).

[1806] 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.

[1807] 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."

[1808] 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.

[1809] 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).

[1810] 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.

[1811] 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.

[1812] 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.

[1813] 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.

[1814] 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.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] The following is further disclosed regarding the above embodiment.

[1821] (Claim 1)

[1822] a means for inputting basic information of a patient with cognitive decline;

[1823] A means for generating personalized cognitive training tasks using artificial intelligence based on basic patient information;

[1824] a means for presenting the generated cognitive training task to the patient;

[1825] a means of collecting patient responses and reactions;

[1826] A means of analyzing the collected responses and reactions and generating feedback to adjust the content of future training sessions.

[1827] a means for presenting the generated feedback to the patient;

[1828] A system including:

[1829] (Claim 2)

[1830] The system of claim 1, which uses natural language processing and image recognition technology as artificial intelligence.

[1831] (Claim 3)

[1832] 10. The system of claim 1, configured to generate assignment content based on the patient's past experiences and preferences.

[1833] "Example 1"

[1834] (Claim 1)

[1835] A means to provide a user interface for caregivers to input basic information about the patient (such as name, age, dementia progression, preferences, and past experiences);

[1836] A means for storing the input basic information in a database;

[1837] A means for generating personalized cognitive training tasks based on basic patient information using artificial intelligence;

[1838] a means for presenting the generated training task to the patient;

[1839] a means of collecting patient responses and reactions;

[1840] A means of analyzing the collected answers and reactions and generating feedback to adjust the content of future training sessions;

[1841] a means for presenting the generated feedback to the patient;

[1842] A system including:

[1843] (Claim 2)

[1844] The system of claim 1, which uses natural language processing and image recognition technology as artificial intelligence.

[1845] (Claim 3)

[1846] 10. The system of claim 1, configured to generate assignment content based on the patient's past experiences and preferences.

[1847] "Application Example 1"

[1848] (Claim 1)

[1849] a means for inputting basic information of a patient with cognitive decline;

[1850] A means for generating personalized cognitive training tasks using artificial intelligence based on basic patient information;

[1851] a means for presenting the generated cognitive training task to the patient;

[1852] a means of collecting patient responses and reactions;

[1853] A means of analyzing the collected responses and reactions and generating feedback to adjust the content of future training sessions.

[1854] a means for presenting the generated feedback to the patient;

[1855] A means of providing patients with personalized cognitive training tasks via a device installed in a brick-and-mortar store and recording their responses;

[1856] A system including:

[1857] (Claim 2)

[1858] The system of claim 1, which uses natural language processing and image recognition technology as artificial intelligence.

[1859] (Claim 3)

[1860] 10. The system of claim 1, configured to generate assignment content based on the patient's past experiences and preferences.

[1861] "Example 2: Combining Emotion Engines"

[1862] (Claim 1)

[1863] a means for inputting basic information of a patient with cognitive decline;

[1864] A means for generating personalized cognitive training tasks using artificial intelligence based on basic patient information;

[1865] a means for presenting the generated cognitive training task to the patient;

[1866] a means for collecting patient responses, reactions, and voice and facial expression data;

[1867] A means for analyzing the collected answers, reactions, voice data, and facial expression data to recognize the emotional state of the patient;

[1868] A means to adjust the next training content based on the analysis results and generate feedback.

[1869] a means for presenting the generated feedback to the patient;

[1870] A system including:

[1871] (Claim 2)

[1872] The system of claim 1, which uses natural language processing and image recognition technology as artificial intelligence.

[1873] (Claim 3)

[1874] 10. The system of claim 1, configured to generate task content based on the patient's past experiences and preferences, and to recognize the patient's emotional state while completing the task and reflect this in the feedback.

[1875] "Application example 2 when combining emotion engines"

[1876] (Claim 1)

[1877] a means for inputting basic information of a patient with cognitive decline;

[1878] A means for generating personalized cognitive training tasks using artificial intelligence based on basic patient information;

[1879] a means for presenting the generated cognitive training task to the patient;

[1880] a means of collecting patient responses and reactions;

[1881] A means of analyzing the collected responses and reactions and generating feedback to adjust the content of future training sessions.

[1882] a means for presenting the generated feedback to the patient;

[1883] A means for collecting voice data and facial expression data and analyzing them using an emotion engine;

[1884] A means for adjusting training content based on the analyzed emotional state;

[1885] A system including:

[1886] (Claim 2)

[1887] The system of claim 1, which uses natural language processing and image recognition technology as artificial intelligence.

[1888] (Claim 3)

[1889] 10. The system of claim 1, configured to generate assignment content based on the patient's past experiences and preferences. [Explanation of symbols]

[1890] 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. a means for inputting basic information of a patient with cognitive decline; A means for generating personalized cognitive training tasks using artificial intelligence based on basic patient information; a means for presenting the generated cognitive training task to the patient; a means of collecting patient responses and reactions; A means of analyzing the collected responses and reactions and generating feedback to adjust the content of future training sessions. a means for presenting the generated feedback to the patient; A system including:

2. The system according to claim 1, wherein the artificial intelligence uses natural language processing and image recognition techniques.

3. The system of claim 1 , configured to generate assignment content based on the patient's past experiences and preferences.

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