system

Abstract

We provide the system. [Solution] Means of obtaining information, A means for generating questions for memory enhancement based on the aforementioned information, A means for presenting the aforementioned problem to the user, A means for receiving and evaluating the user's response, A means of providing the aforementioned evaluation results as feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In dementia patients, memory decline and accompanying communication disorders are major problems. Conventional methods have difficulty in flexibly adapting to the individual lifestyles and interests of patients, and there are limited means to effectively strengthen memory. Therefore, it is necessary to provide a system that can make the most of the individual background information of patients and activate memory in an enjoyable way.

Means for Solving the Problems

[0005] The present invention provides a system that generates memory-enhancing problems based on individually provided information and presents them to the user. The system includes means for acquiring information, means for generating memory-enhancing problems based on the information, means for presenting the generated problems to the user, means for receiving and evaluating the user's answers, and means for providing the evaluation results as feedback, thereby aiming to improve memory and promote communication.

[0006] "Information" refers to data related to the user, including the user's hobbies, background, photos, favorite music and TV shows, etc.

[0007] "Questions for improving memory" are quizzes and questions designed to train a user's memory, and are structured based on the user's individual information.

[0008] "Generating means" refers to a technology or process used to create questions for memory enhancement from collected information, and in this invention, this includes generating AI.

[0009] "Presentation means" refers to a method or device for displaying or providing a user with a generated memory-enhancing problem.

[0010] "Evaluation means" refers to a process or device used to receive user responses and determine their correctness.

[0011] "Feedback" refers to information provided based on evaluation results of user responses, including confirmation of accuracy and additional advice. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system of this invention acquires user information and generates and provides memory-enhancing problems based on that information. This system aims to enhance the memory of dementia patients by registering various information related to the user and using it to generate personalized problems.

[0034] First, the user uses a device to input information about the patient's hobbies, history, favorite TV shows and music, photos, etc. This information serves as the basis for generating problems based on the patient's individual background and interests.

[0035] Once this information is entered, the terminal sends it to the server. The server stores the received information in a database and starts the process of calling a generative AI to generate questions. The generative AI generates questions designed to stimulate the patient's memory, and these questions may be in the form of text, images, music, etc.

[0036] Next, the generated question is presented to the user via the device. The user answers the question and sends the answer to the server via the device. The server evaluates the answer and determines whether it is correct or not. Based on the result, feedback is generated and provided to the user via the device. This feedback includes praise for correct answers and the correct answer for incorrect answers, and serves to further stimulate the user's memory.

[0037] As a concrete example, consider a patient who enjoys gardening and classical music. In this case, the server generates questions using a generative AI, such as "What is the name of this plant?" or "Who composed this classical piece?" The user answers these questions on their device, and feedback such as the accuracy rate and additional information is provided. This enhances the patient's memory in a game-like manner and also promotes natural communication with family members.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user enters information about the dementia patient into the terminal. Specifically, they enter information such as the patient's hobbies, history, favorite music, TV shows, and photos, and prepare to send it.

[0041] Step 2:

[0042] The terminal formats the collected user information and sends it to the server. To ensure data consistency and security, the data is encrypted and checksums are added.

[0043] Step 3:

[0044] The server receives user information sent from the terminal and efficiently stores it in the database. Before saving, it performs data integrity and duplicate checks.

[0045] Step 4:

[0046] The server uses a generative AI to generate personalized memory-enhancing questions based on stored user information. The generation process analyzes the input information to determine appropriate quiz themes and formats.

[0047] Step 5:

[0048] Problems generated by a generation AI are sent from the server to the terminal. These problems may include text, images, music clips, and more.

[0049] Step 6:

[0050] The terminal presents the received problem to the user. The user reads the problem on the screen and enters their answer according to the instructions.

[0051] Step 7:

[0052] When a user enters a response, the device sends that response to the server. To reduce latency, the data is processed simultaneously by multiple server processes.

[0053] Step 8:

[0054] The server receives the user's response, compares it against a database of correct answers, and evaluates it. The evaluation results, including the accuracy rate and reaction time, are recorded, and a feedback message is generated.

[0055] Step 9:

[0056] The server sends the generated feedback to the terminal. The feedback includes confirmation of whether the answer is correct or incorrect, as well as additional information.

[0057] Step 10:

[0058] The device displays feedback sent from the server to the user, allowing them to understand their progress and areas for improvement in their memory.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] The present invention aims to provide an efficient and personalized method for improving the memory of dementia patients. Conventional methods only provide general problems, making it difficult to generate problems tailored to the individual patient's interests and background. As a result, it is difficult to capture the patient's attention and effectively improve their memory.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring information and transmitting said information to a central processing unit, a storage medium for storing the acquired information, means for calling a generation engine that generates problems for memory enhancement based on said information, and means for presenting the generated problems to the user via an output device. This makes it possible to generate and present problems based on the individual's hobbies and preferences, and can effectively support the improvement of the patient's memory.

[0064] "Means for acquiring information and transmitting said information to the central processing unit" refers to a function for collecting input information from users and transmitting it to the central processing unit.

[0065] "Storage medium for storing acquired information" refers to a physical or logical storage device that holds information acquired from users and allows it to be retrieved and used as needed.

[0066] "Means of calling a generation engine that generates questions for memory enhancement" refers to the function of starting and executing a program or algorithm unit that automatically creates questions tailored to individual users.

[0067] "Means of presenting the generated problem to the user via an output device" refers to a notification function through an interface device such as a display or speaker to show the created problem to the user visually or audibly.

[0068] A "prompt statement that instructs problem generation" refers to an instruction or command statement used to cause a generation engine or artificial intelligence to generate a specific problem.

[0069] This invention provides a system to help enhance memory. The user uses a terminal to input information about the dementia patient, such as their hobbies, history, favorite TV programs, music, and photographs. This input serves as the basis for creating personalized questions for the patient.

[0070] The terminal sends the information entered by the user to the server. The data is transmitted using a secure protocol such as HTTPS. The server stores this information in a database.

[0071] After the information is saved, the server invokes a generative AI model to generate questions for memory enhancement. This generative AI uses deep learning and natural language processing techniques to generate questions that stimulate the patient's memory in various media formats such as text, images, and music.

[0072] The generated questions are presented to the user via the device. The user can answer the questions on the device, and the answers are sent back to the server. The server evaluates the answers and generates feedback, along with a determination of whether they are correct or incorrect. The feedback includes praise for correct answers and the correct answer for incorrect answers.

[0073] As a concrete example, if a patient enjoys gardening and classical music, the prompt could be phrased as follows: "The user's hobbies are gardening and classical music. Based on this information, please generate questions to stimulate memory." This prompt would then prompt the AI ​​to create questions such as, "What is the name of this plant?" or "Who composed this classical piece?" By answering these questions, the user can improve their memory in a game-like manner and also facilitate communication with family members.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user uses a terminal to input information about the dementia patient, such as their hobbies, history, favorite TV shows, music, and photos. This input information is treated as foundational data for the system to generate personalized problems. In practice, the user enters the necessary information into a form via a GUI and clicks the "Submit" button.

[0077] Step 2:

[0078] The terminal sends information obtained from the user to the server. The input data, such as text information about hobbies and preferences, is securely transmitted to the server using the HTTPS protocol. This protocol ensures the confidentiality of the data.

[0079] Step 3:

[0080] The server stores the received information in a database. The received data is stored in temporary variables for processing and then added to the database using SQL queries. The database is designed to allow for quick retrieval and searching of information.

[0081] Step 4:

[0082] The server invokes a generation AI model based on information stored in the database. It generates a prompt from the stored data and sends instructions to the generation AI such as, "The user's hobbies are gardening and they like classical music. Based on this information, please generate a question to stimulate their memory." The AI ​​then processes the question generation based on this prompt.

[0083] Step 5:

[0084] The generative AI model analyzes the prompt text and generates personalized problems. This prompt-based problem generation process involves creating problems in various formats, such as text, images, and music, through natural language processing algorithms. The generated problem data is returned to the server.

[0085] Step 6:

[0086] The server sends the generated problem to the terminal. The generated result data is sent to the terminal as an HTTP response, making it accessible to the user.

[0087] Step 7:

[0088] The terminal presents the received problem to the user. The problem is displayed directly on the terminal's screen as text or an image, and the user inputs their answer. The user interface for solving the problem is crucial here.

[0089] Step 8:

[0090] The user answers the presented questions and sends their answers to the server via their device. Users can do so by clicking on the options or by entering their answers in the input form and clicking the "Submit Answer" button.

[0091] Step 9:

[0092] The server receives responses from users and evaluates whether they are correct. It processes the received response data using an evaluation algorithm and generates feedback data based on the results.

[0093] Step 10:

[0094] Feedback from the server is sent to the device, which then provides this feedback to the user. The feedback is displayed on the screen as text or sound, which the user can review, and is praised for correct answers and given the correct answer for incorrect answers.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] While numerous training tools exist today aimed at improving individual memory, few offer personalized experiences tailored to individual interests. Furthermore, there is a growing need to integrate memory training with customer experiences in physical spaces such as retail stores and commercial facilities to provide a richer and more valuable experience.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for acquiring information, means for providing products and experiences tailored to an individual's interests, and means for providing measurements related to the products and experiences and enhancing the experience. This makes it possible to provide personalized memory-enhancing experiences based on an individual's interests in a physical store.

[0100] "Means of acquiring information" refers to devices or methods for collecting data related to an individual's hobbies and interests.

[0101] "Means for generating measurements" refers to a process or apparatus for creating personalized memory improvement questions or quizzes based on acquired information.

[0102] "Means of presenting to an individual" refers to a method or device for displaying generated measurements to a user and soliciting their response.

[0103] "Means of evaluation" refers to methods or devices for collecting individual responses and analyzing their accuracy and quality.

[0104] "Means of providing feedback" refers to a process or device that provides additional information or comments to an individual based on the evaluation results.

[0105] "Means of providing goods or experiences" refers to methods or devices that provide relevant products or experiential value based on an individual's interests.

[0106] "Means of enhancing experience" refers to the process or device of designing an experience to contribute to improving an individual's memory through the products or experiences provided.

[0107] This invention is a system for enhancing individual memory in a physical store environment, collecting information and providing a personalized experience. The system consists of three elements: a server, a terminal, and a user.

[0108] Users input information about their hobbies and interests using devices such as smartphones or smart glasses. This input information is sent from the device to the server. The server uses a database and a generative AI model to generate personalized questions based on the received information. This generative AI model could be, for example, GPT, which uses natural language processing technology. An example of a prompt would be, "Your interest is art. Please create a quiz related to the paintings in the store. Example: Who painted this artwork?"

[0109] The generated questions are returned to the terminal and presented to the user. The user answers the presented quiz, and the results are sent back to the server. The server evaluates the received answers and determines correctness using data calculations such as CT. This evaluation result is provided to the user as feedback via the terminal, and the correct answers and additional information are displayed to enhance memory.

[0110] As a concrete example, an art-loving user could scan a specific painting in the store, and then be presented with a question related to that painting. By answering, the user would immediately receive relevant information and praise, enriching their in-store experience.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users use a device to input information about their hobbies and interests. Specifically, they use the input interface of a smartphone or smart glasses to enter information about their interests such as music, art, and fashion, and the device collects this as digital data. This data is treated as text information that embodies the user's interests.

[0114] Step 2:

[0115] The terminal sends the entered information to the cloud server. Here, the digital information (input text) is converted into prompt data and securely sent to the server via a communication protocol. This prompt data is then ready to be input into the generating AI model.

[0116] Step 3:

[0117] The server uses a generative AI model based on the received data to generate personalized questions. The generative AI model uses a question generation algorithm to perform natural language processing according to the received prompt data and generates highly relevant quizzes in text format. This generation process may produce specific questions such as, "Your interest is music. Who composed this song?"

[0118] Step 4:

[0119] The server sends the generated problems to the terminal. The output problem data is converted into a format for display on the terminal and sent to the user's terminal.

[0120] Step 5:

[0121] The device displays the received question on the screen and prompts the user to answer. The user enters their answer to the quiz and submits the result. This input data is compiled within the device as the user's answer.

[0122] Step 6:

[0123] The terminal sends the user's response to the server. The response data is then sent back to the server via a communication protocol.

[0124] Step 7:

[0125] The server evaluates the received response. It analyzes the input data using an algorithm that compares it against a database of correct answers and determines whether it is correct or incorrect. For example, if the answer is correct, a feedback message such as "Correct!" is generated; if it is incorrect, a message such as "Unfortunately, the correct answer is XX" is generated.

[0126] Step 8:

[0127] The server sends the generated feedback along with the evaluation results to the terminal. This feedback data includes suggestions for future actions, praise, and correction information based on the evaluation results.

[0128] Step 9:

[0129] The device presents feedback to the user. The results are displayed on the user's screen, and actions, including additional information and praise, are recommended. This feedback allows the user to enhance their memory while simultaneously acquiring product information and related experiences.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention aims to improve memory enhancement and user experience by combining a system that acquires user information, generates and provides memory-enhancing problems, and further incorporates an emotion engine that recognizes the user's emotions.

[0132] First, the user uses a terminal to input detailed information about the dementia patient. This includes hobbies, background, photos, favorite music and TV shows, etc. This information forms the basis for the system to personalize memory enhancement tasks.

[0133] The terminal sends information obtained from the user to the server. The server stores the transmitted information in a database and uses a generative AI to generate questions for memory enhancement. The generative AI analyzes the input information and determines the optimal quiz theme and format.

[0134] Furthermore, an emotion engine built into the device recognizes the user's emotions. The emotion engine determines the user's current emotional state using biometric information, voice analysis, facial expression analysis, and other methods. This emotional information is processed on the server and reflected in the generated problems and feedback.

[0135] The generated memory-enhancing questions are provided to the user via a device. The user answers the presented questions and sends their answers to the server via the device. The server evaluates the answers and generates appropriate feedback based on the user's emotional information.

[0136] For example, if the server determines that a user is experiencing stress, it can adjust the difficulty level of the problem based on emotional information and provide relaxing feedback. This allows for flexible responses tailored to the user's mood, enhancing memory enhancement and improving the user experience.

[0137] Thus, the system of the present invention provides a more personalized experience by offering problems and feedback aimed at enhancing memory based on the user's individual information and real-time emotional state.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users input information about dementia patients using a terminal. The terminal provides an interface for inputting data such as the patient's hobbies, history, favorite music and TV shows, and photos.

[0141] Step 2:

[0142] The terminal formats the information entered by the user and sends it to the server. The information is transmitted in an encrypted format using a secure communication protocol.

[0143] Step 3:

[0144] The server stores the received information in a database. The database is used to efficiently manage information for each patient and utilize it for subsequent processing.

[0145] Step 4:

[0146] The server uses information retrieved from the database to activate a generative AI and begin the process of generating memory enhancement problems. The generative AI creates problems tailored to the individual patient's background.

[0147] Step 5:

[0148] The emotion engine built into the device collects data to recognize the user's emotions. This is done by analyzing the user's voice and facial expressions using biosensors, cameras, and other devices.

[0149] Step 6:

[0150] The device sends emotional information collected by the emotion engine to the server. This emotional data is also encrypted before being sent to the server.

[0151] Step 7:

[0152] The server combines generated problems with user sentiment information to make adjustments. Specifically, it adjusts the difficulty level of the problems according to the user's mental state and selects an appropriate set of problems.

[0153] Step 8:

[0154] The device presents the user with tailored problems, allowing them to engage with interesting problems in a relaxed manner.

[0155] Step 9:

[0156] The user enters their answers to questions displayed on the device. Answers can be entered in multiple-choice or free-text format.

[0157] Step 10:

[0158] The terminal sends the user's response to the server. The response data is recorded on the server and used for evaluation.

[0159] Step 11:

[0160] The server evaluates the responses and generates appropriate feedback based on the user's emotional information obtained from the emotion engine. This feedback includes encouraging messages and guidance for the next steps.

[0161] Step 12:

[0162] The terminal displays feedback from the server to the user. Based on this feedback, the user can check their progress and move on to the next problem.

[0163] (Example 2)

[0164] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0165] There is a need to maximize the effectiveness of memory enhancement and improve the user experience by providing memory enhancement tasks based on individual user information and responding flexibly according to the user's emotional state. Conventional technologies have difficulty providing tasks and feedback that adequately consider individual user information and emotional states, resulting in a lack of highly effective memory enhancement methods.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0167] In this invention, the server includes means for acquiring information, means for generating tasks for memory enhancement, means for recognizing the user's emotions, means for adjusting the difficulty of the tasks using emotional information, means for receiving and evaluating the user's responses, and means for providing the evaluation results as feedback. This enables effective and personalized memory enhancement based on individual user information and real-time emotional states.

[0168] "Means of acquiring information" refers to functions for collecting data provided by users and inputting it into the system.

[0169] "Means for generating tasks to enhance memory" refers to a function that creates questions and quizzes tailored to the user based on the information provided by the user.

[0170] "Means of recognizing user emotions" refers to technologies that analyze a user's voice, facial expressions, and biometric information to determine their current emotional state.

[0171] "Means of adjusting problem difficulty using emotional information" refers to a function that takes the user's emotional state into consideration and changes the difficulty level of a problem to an appropriate level.

[0172] "Means for receiving and evaluating user responses" refers to a function that allows the system to receive responses submitted by users to assigned tasks and to determine their accuracy and relevance.

[0173] "Means of providing evaluation results as feedback" refers to a function that allows the system to return appropriate information and advice to the user based on the evaluation of the user's responses.

[0174] This system aims to provide memory enhancement tasks tailored to each user's individual information, and to offer feedback that takes emotions into account. By utilizing advanced generative AI models and emotion recognition technology, it provides a learning experience that matches the user's characteristics.

[0175] Specifically, users input personal information required by cognitive functions via their devices. This includes hobbies, past experiences, visual materials, and favorite music and videos. This information serves as foundational data for highly personalized user experiences.

[0176] The terminal organizes the input information and sends it to the server using a secure communication method. The server stores the information in a database and dynamically generates tasks for memory enhancement using a generative AI model. In this generation process, for example, natural language processing technology is used to determine the task content using prompt sentences that are aligned with the user's interests.

[0177] Furthermore, the device's built-in emotion recognition technology analyzes the user's voice and visual information to identify their emotional state in real time. This emotional information is sent to a server and used to generate tasks and feedback.

[0178] The generative AI model has the ability to detect the user's emotional state and adjust the difficulty and content of the task accordingly. For example, if the user is feeling stressed, the server will reduce the complexity of the problem and generate feedback that helps the user relax.

[0179] As a concrete example, a prompt might be an instruction such as, "Generate a memory problem on a topic of interest to the user, with relaxing content." This prompt is processed by a generative AI model, resulting in the creation of personalized problems.

[0180] In this way, the system creates a personalized learning and feedback experience based on the information entered by the user and real-time sentiment data.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] Users enter personal information using their devices. This information includes their hobbies, background, visual data, and favorite music and videos. This data serves as the basis for personalizing memory enhancement tasks.

[0184] Step 2:

[0185] The terminal transmits information obtained from the user to the server using a secure protocol. This process converts the input information into an appropriate format to ensure smooth data processing on the server side. The output is user information converted into a format that the server can accept.

[0186] Step 3:

[0187] The server stores the received user information in a database. This stored information becomes the data source used by the generating AI model. This data is used to reflect the individual needs of the user in order to generate memory enhancement tasks.

[0188] Step 4:

[0189] The server generates memory enhancement tasks using a generative AI model based on stored information. Specifically, the AI ​​model analyzes prompts based on the user's interests and determines the task content. The input is user information from the database, and the output is a task tailored to the user.

[0190] Step 5:

[0191] The device's built-in emotion engine recognizes the user's emotions. The device acquires the user's voice and facial expression data and analyzes their emotional state. This analysis result is sent to the server as the user's most recent emotional state. The results of the emotion analysis influence the generated tasks and feedback. The output is data indicating the emotional state.

[0192] Step 6:

[0193] The server sends the generated task to the terminal. The terminal displays this task to the user and accepts the user's response. The output is a presentation of the task with an interface for the user to input their answer.

[0194] Step 7:

[0195] The user's response is sent from the terminal to the server, which then evaluates the response. The server further considers the user's sentiment information to generate optimal feedback. The input is the user's response and sentiment information, and the output is the feedback content.

[0196] Step 8:

[0197] The terminal displays feedback provided by the server to the user. By receiving this feedback, the user can understand areas for further learning and improvement. The output is a display of feedback to the user.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0200] In modern work environments, worker stress and fatigue are factors that lead to decreased productivity and safety risks. To prevent this and improve the work environment, a system is needed that can recognize workers' emotional states in real time and respond appropriately to individual situations. However, current technology does not adequately provide real-time responses based on emotional states, and a more effective solution is needed.

[0201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0202] In this invention, the server includes a device for acquiring information, a device for recognizing emotional states in real time, and a device for adjusting the difficulty level of tasks based on the emotional states. This enables dynamic adjustment of tasks and appropriate feedback in accordance with the emotional states of individual workers in the work environment.

[0203] A "device for acquiring information" is a device for accumulating individual user information and has the function of collecting diverse information, including hobbies, background, and biometric data.

[0204] A "device for generating tasks to enhance memory" is a device that, based on acquired information, presents customized tasks aimed at improving the user's memory, utilizing AI to design optimal tasks.

[0205] A "device for presenting tasks to users" is a device that presents generated tasks to users through means such as sight and hearing, enabling them to effectively tackle the tasks.

[0206] A "device that receives and analyzes user responses" is a device that receives user answers to presented tasks and analyzes their accuracy, speed, and content.

[0207] A "feedback device" is a device that provides appropriate comments and advice tailored to the user based on the analysis results, contributing to the improvement of the user's understanding and memory.

[0208] A "device that recognizes emotional states in real time" is a device that detects the user's facial expressions, voice tone, and biosignals, analyzes that data, and determines the user's emotional state in real time.

[0209] A "task difficulty adjustment device" is a device that appropriately changes the difficulty and content of a task while taking into account the user's emotional state and stress level, so that the user can work on the task without difficulty.

[0210] The system for realizing this invention mainly consists of a server, a terminal, and a user.

[0211] The server uses generative AI models to collect information and generate customized tasks for each user. The server utilizes storage media to accumulate each user's hobbies, background, and past performance data. It also uses an intelligent engine to process user response data for analysis and generate appropriate feedback. This process employs database management systems and specialized generative AI software. Specifically, machine learning libraries are used for data analysis.

[0212] The device is responsible for recognizing the user's emotional state in real time. For emotion recognition, facial recognition software is used for facial expression analysis, and a speech recognition platform is used for voice analysis. The device has built-in sensors and a camera, and it sends the data acquired through these to a server, where the analysis results are provided to the user via the display.

[0213] Users work on tasks presented through their devices and provide answers. The device monitors the user's reactions, performance, and emotional state. For example, if the server detects high stress levels in the user, it uses a generative AI model to generate feedback such as, "We recommend you reduce the workload or take a break."

[0214] A concrete example of a prompt message is, "Based on the worker's facial expression data and biometric information, fatigue has been detected. Suggest an appropriate method for refreshing the worker." This prompt message is input into a generative AI model and used to automatically generate appropriate countermeasures.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The device collects personal information from the user. This information includes hobbies, background, and past performance data. The collected information is obtained through sensors and input interfaces and transmitted to a server. This information processing prepares the foundational data for generating tasks tailored to the user's characteristics.

[0218] Step 2:

[0219] The device recognizes the user's emotions in real time. It utilizes a voice recognition platform and facial recognition software to acquire facial expressions, voice tone, and biometric information via sensors. The acquired emotional data is processed on the device and sent to the server as the user's current emotional state. This prepares the device for adjusting tasks according to the user's emotional state.

[0220] Step 3:

[0221] The server stores the received information and generates tasks optimized for each user. A generation AI model is used to determine the task content and difficulty level based on the input information. Data analysis is then used to evaluate the user's performance trends and design appropriate tasks. The generated tasks are sent to the terminal.

[0222] Step 4:

[0223] The device presents the user with a generated task. The content of the task is clearly communicated to the user through the display and audio output. The user begins working on the presented task, and the device monitors the user's response at this point.

[0224] Step 5:

[0225] The user works on a task and enters their answer into the device. Data about the user's activity (accuracy of the answer, time taken, etc.) is also acquired at the same time. The device sends this data to the server.

[0226] Step 6:

[0227] The server analyzes the user's responses and evaluates the results. An intelligent engine is used to calculate the accuracy and time taken for the responses, and compare them to expected performance. The evaluation results are then sent back to the terminal.

[0228] Step 7:

[0229] The device generates appropriate feedback based on the evaluation results and provides it to the user. This includes suggestions for refreshing the user based on their emotional state and advice for the next steps. Using prompts generated by an AI model, the system advises the user on the best course of action.

[0230] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0231] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0236] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0237] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0238] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0239] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0240] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0241] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0242] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0243] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0244] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0245] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0246] The system of this invention acquires user information and generates and provides memory-enhancing problems based on that information. This system aims to enhance the memory of dementia patients by registering various information related to the user and using it to generate personalized problems.

[0247] First, the user uses a device to input information about the patient's hobbies, history, favorite TV shows and music, photos, etc. This information serves as the basis for generating problems based on the patient's individual background and interests.

[0248] Once this information is entered, the terminal sends it to the server. The server stores the received information in a database and starts the process of calling a generative AI to generate questions. The generative AI generates questions designed to stimulate the patient's memory, and these questions may be in the form of text, images, music, etc.

[0249] Next, the generated question is presented to the user via the device. The user answers the question and sends the answer to the server via the device. The server evaluates the answer and determines whether it is correct or not. Based on the result, feedback is generated and provided to the user via the device. This feedback includes praise for correct answers and the correct answer for incorrect answers, and serves to further stimulate the user's memory.

[0250] As a concrete example, consider a patient who enjoys gardening and classical music. In this case, the server generates questions using a generative AI, such as "What is the name of this plant?" or "Who composed this classical piece?" The user answers these questions on their device, and feedback such as the accuracy rate and additional information is provided. This enhances the patient's memory in a game-like manner and also promotes natural communication with family members.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The user enters information about the dementia patient into the terminal. Specifically, they enter information such as the patient's hobbies, history, favorite music, TV shows, and photos, and prepare to send it.

[0254] Step 2:

[0255] The terminal formats the collected user information and sends it to the server. To ensure data consistency and security, the data is encrypted and checksums are added.

[0256] Step 3:

[0257] The server receives user information sent from the terminal and efficiently stores it in the database. Before saving, it performs data integrity and duplicate checks.

[0258] Step 4:

[0259] The server uses a generative AI to generate personalized memory-enhancing questions based on stored user information. The generation process analyzes the input information to determine appropriate quiz themes and formats.

[0260] Step 5:

[0261] Problems generated by a generation AI are sent from the server to the terminal. These problems may include text, images, music clips, and more.

[0262] Step 6:

[0263] The terminal presents the received problem to the user. The user reads the problem on the screen and enters their answer according to the instructions.

[0264] Step 7:

[0265] When a user enters a response, the device sends that response to the server. To reduce latency, the data is processed simultaneously by multiple server processes.

[0266] Step 8:

[0267] The server receives the user's response, compares it against a database of correct answers, and evaluates it. The evaluation results, including the accuracy rate and reaction time, are recorded, and a feedback message is generated.

[0268] Step 9:

[0269] The server sends the generated feedback to the terminal. The feedback includes confirmation of whether the answer is correct or incorrect, as well as additional information.

[0270] Step 10:

[0271] The device displays feedback sent from the server to the user, allowing them to understand their progress and areas for improvement in their memory.

[0272] (Example 1)

[0273] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] The present invention aims to provide an efficient and personalized method for improving the memory of dementia patients. Conventional methods only provide general problems, making it difficult to generate problems tailored to the individual patient's interests and background. As a result, it is difficult to capture the patient's attention and effectively improve their memory.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0276] In this invention, the server includes means for acquiring information and transmitting said information to a central processing unit, a storage medium for storing the acquired information, means for calling a generation engine that generates problems for memory enhancement based on said information, and means for presenting the generated problems to the user via an output device. This makes it possible to generate and present problems based on the individual's hobbies and preferences, and can effectively support the improvement of the patient's memory.

[0277] "Means for acquiring information and transmitting said information to the central processing unit" refers to a function for collecting input information from users and transmitting it to the central processing unit.

[0278] "Memory medium for storing acquired information" refers to a physical or logical storage device that holds information acquired from a user and can retrieve and use it as needed.

[0279] "Means for calling a generation engine that generates problems for memory enhancement" refers to the function of starting and executing a program or algorithm unit that automatically creates problems according to individual users.

[0280] "Means for presenting the generated problems to the user via an output device" refers to the notification function through interface devices such as displays and speakers that visually or auditorily show the created problems to the user.

[0281] "Prompt sentence for instructing problem generation" refers to an instruction sentence or command sentence used to cause a generation engine or artificial intelligence to generate specific problems.

[0282] This invention provides a system for assisting in memory enhancement. The user uses a terminal to input information such as the hobbies, experiences, favorite TV shows, music, photos, etc. of a dementia patient. This input act serves as the basic data for creating problems individualized for the patient.

[0283] The terminal transmits the information input by the user to the server. At that time, the data is communicated using a secure protocol such as HTTPS. The server stores this information in a database.

[0284] After the information is stored, the server calls a generation AI model and generates problems for memory enhancement. This generation AI uses deep learning and natural language processing technologies to generate problems that stimulate the patient's memory in various media forms such as text, images, and music.

[0285] The generated questions are presented to the user via the terminal. The user can answer the questions on the terminal, and the answer results are sent back to the server again. The server evaluates the answer and generates feedback along with a determination of whether it is correct. The feedback includes praise when it is correct and the correct answer when it is incorrect.

[0286] As a specific example, when a patient has hobbies such as gardening and classical music, the generation AI is instructed in the form of "The user's hobbies are gardening and they like classical music. Please generate questions to stimulate memory based on this information." With this prompt, the generation AI creates questions such as "What is the name of this plant?" or "Who composed this classical music?" By answering these questions, the user can improve their memory in a game-like manner and also promote communication with their family.

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The user uses the terminal to input information such as the hobbies, experiences, favorite TV shows, music, photos, etc. of the dementia patient. This input information is treated as the basic data for the system to generate individualized questions. As an actual operation, the user inputs the necessary information into a form via the GUI and clicks the "Send" button.

[0290] Step 2:

[0291] The terminal sends the information obtained from the user to the server. The input data is text information regarding hobbies and preferences, etc., and is securely sent to the server using the HTTPS protocol. This protocol maintains the confidentiality of the data.

[0292] Step 3:

[0293] The server stores the received information in a database. The received data is stored in temporary variables for processing and then added to the database using SQL queries. The database is designed to allow for quick retrieval and searching of information.

[0294] Step 4:

[0295] The server invokes a generation AI model based on information stored in the database. It generates a prompt from the stored data and sends instructions to the generation AI such as, "The user's hobbies are gardening and they like classical music. Based on this information, please generate a question to stimulate their memory." The AI ​​then processes the question generation based on this prompt.

[0296] Step 5:

[0297] The generative AI model analyzes the prompt text and generates personalized problems. This prompt-based problem generation process involves creating problems in various formats, such as text, images, and music, through natural language processing algorithms. The generated problem data is returned to the server.

[0298] Step 6:

[0299] The server sends the generated problem to the terminal. The generated result data is sent to the terminal as an HTTP response, making it accessible to the user.

[0300] Step 7:

[0301] The terminal presents the received problem to the user. The problem is displayed directly on the terminal's screen as text or an image, and the user inputs their answer. The user interface for solving the problem is crucial here.

[0302] Step 8:

[0303] The user answers the presented question and sends the answer to the server through the terminal. The user operates by clicking on the options, entering the answer in the input form, and clicking the "Answer Submission" button.

[0304] Step 9:

[0305] The server receives the answer from the user and evaluates whether it is correct. The received answer data is judged by applying it to an evaluation algorithm, and feedback data is generated based on the result.

[0306] Step 10:

[0307] The feedback from the server is sent to the terminal, and the terminal provides this feedback to the user. The feedback is displayed as text or sound on the display, and the user can confirm it. When the answer is correct, praise is given, and when the answer is incorrect, the correct answer is provided.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In modern times, there are many training tools aimed at improving an individual's memory, but few that provide personalized experiences according to individual interests. Also, there is a demand to provide richer experiential value by linking the customer experience in physical spaces such as actual stores and commercial facilities with memory training.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0312] In this invention, the server includes means for acquiring information, means for providing products and experiences tailored to an individual's interests, and means for providing measurements related to the products and experiences and enhancing the experience. This makes it possible to provide personalized memory-enhancing experiences based on an individual's interests in a physical store.

[0313] "Means of acquiring information" refers to devices or methods for collecting data related to an individual's hobbies and interests.

[0314] "Means for generating measurements" refers to a process or apparatus for creating personalized memory improvement questions or quizzes based on acquired information.

[0315] "Means of presenting to an individual" refers to a method or device for displaying generated measurements to a user and soliciting their response.

[0316] "Means of evaluation" refers to methods or devices for collecting individual responses and analyzing their accuracy and quality.

[0317] "Means of providing feedback" refers to a process or device that provides additional information or comments to an individual based on the evaluation results.

[0318] "Means of providing goods or experiences" refers to methods or devices that provide relevant products or experiential value based on an individual's interests.

[0319] "Means of enhancing experience" refers to the process or device of designing an experience to contribute to improving an individual's memory through the products or experiences provided.

[0320] This invention is a system for enhancing individual memory in a physical store environment, collecting information and providing a personalized experience. The system consists of three elements: a server, a terminal, and a user.

[0321] Users input information about their hobbies and interests using devices such as smartphones or smart glasses. This input information is sent from the device to the server. The server uses a database and a generative AI model to generate personalized questions based on the received information. This generative AI model could be, for example, GPT, which uses natural language processing technology. An example of a prompt would be, "Your interest is art. Please create a quiz related to the paintings in the store. Example: Who painted this artwork?"

[0322] The generated questions are returned to the terminal and presented to the user. The user answers the presented quiz, and the results are sent back to the server. The server evaluates the received answers and determines correctness using data calculations such as CT. This evaluation result is provided to the user as feedback via the terminal, and the correct answers and additional information are displayed to enhance memory.

[0323] As a concrete example, an art-loving user could scan a specific painting in the store, and then be presented with a question related to that painting. By answering, the user would immediately receive relevant information and praise, enriching their in-store experience.

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] Users use a device to input information about their hobbies and interests. Specifically, they use the input interface of a smartphone or smart glasses to enter information about their interests such as music, art, and fashion, and the device collects this as digital data. This data is treated as text information that embodies the user's interests.

[0327] Step 2:

[0328] The terminal sends the entered information to the cloud server. Here, the digital information (input text) is converted into prompt data and securely sent to the server via a communication protocol. This prompt data is then ready to be input into the generating AI model.

[0329] Step 3:

[0330] The server uses a generative AI model based on the received data to generate personalized questions. The generative AI model uses a question generation algorithm to perform natural language processing according to the received prompt data and generates highly relevant quizzes in text format. This generation process may produce specific questions such as, "Your interest is music. Who composed this song?"

[0331] Step 4:

[0332] The server sends the generated problems to the terminal. The output problem data is converted into a format for display on the terminal and sent to the user's terminal.

[0333] Step 5:

[0334] The device displays the received question on the screen and prompts the user to answer. The user enters their answer to the quiz and submits the result. This input data is compiled within the device as the user's answer.

[0335] Step 6:

[0336] The terminal sends the user's response to the server. The response data is then sent back to the server via a communication protocol.

[0337] Step 7:

[0338] The server evaluates the received response. It analyzes the input data using an algorithm that compares it against a database of correct answers and determines whether it is correct or incorrect. For example, if the answer is correct, a feedback message such as "Correct!" is generated; if it is incorrect, a message such as "Unfortunately, the correct answer is XX" is generated.

[0339] Step 8:

[0340] The server sends the generated feedback along with the evaluation results to the terminal. This feedback data includes suggestions for future actions, praise, and correction information based on the evaluation results.

[0341] Step 9:

[0342] The device presents feedback to the user. The results are displayed on the user's screen, and actions, including additional information and praise, are recommended. This feedback allows the user to enhance their memory while simultaneously acquiring product information and related experiences.

[0343] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0344] This invention aims to improve memory enhancement and user experience by combining a system that acquires user information, generates and provides memory-enhancing problems, and further incorporates an emotion engine that recognizes the user's emotions.

[0345] First, the user uses a terminal to input detailed information about the dementia patient. This includes hobbies, background, photos, favorite music and TV shows, etc. This information forms the basis for the system to personalize memory enhancement tasks.

[0346] The terminal sends information obtained from the user to the server. The server stores the transmitted information in a database and uses a generative AI to generate questions for memory enhancement. The generative AI analyzes the input information and determines the optimal quiz theme and format.

[0347] Furthermore, an emotion engine built into the device recognizes the user's emotions. The emotion engine determines the user's current emotional state using biometric information, voice analysis, facial expression analysis, and other methods. This emotional information is processed on the server and reflected in the generated problems and feedback.

[0348] The generated memory-enhancing questions are provided to the user via a device. The user answers the presented questions and sends their answers to the server via the device. The server evaluates the answers and generates appropriate feedback based on the user's emotional information.

[0349] For example, if the server determines that a user is experiencing stress, it can adjust the difficulty level of the problem based on emotional information and provide relaxing feedback. This allows for flexible responses tailored to the user's mood, enhancing memory enhancement and improving the user experience.

[0350] Thus, the system of the present invention provides a more personalized experience by offering problems and feedback aimed at enhancing memory based on the user's individual information and real-time emotional state.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] Users input information about dementia patients using a terminal. The terminal provides an interface for inputting data such as the patient's hobbies, history, favorite music and TV shows, and photos.

[0354] Step 2:

[0355] The terminal formats the information entered by the user and sends it to the server. The information is transmitted in an encrypted format using a secure communication protocol.

[0356] Step 3:

[0357] The server stores the received information in a database. The database is used to efficiently manage information for each patient and utilize it for subsequent processing.

[0358] Step 4:

[0359] The server uses information retrieved from the database to activate a generative AI and begin the process of generating memory enhancement problems. The generative AI creates problems tailored to the individual patient's background.

[0360] Step 5:

[0361] The emotion engine built into the device collects data to recognize the user's emotions. This is done by analyzing the user's voice and facial expressions using biosensors, cameras, and other devices.

[0362] Step 6:

[0363] The device sends emotional information collected by the emotion engine to the server. This emotional data is also encrypted before being sent to the server.

[0364] Step 7:

[0365] The server combines generated problems with user sentiment information to make adjustments. Specifically, it adjusts the difficulty level of the problems according to the user's mental state and selects an appropriate set of problems.

[0366] Step 8:

[0367] The device presents the user with tailored problems, allowing them to engage with interesting problems in a relaxed manner.

[0368] Step 9:

[0369] The user enters their answers to questions displayed on the device. Answers can be entered in multiple-choice or free-text format.

[0370] Step 10:

[0371] The terminal sends the user's response to the server. The response data is recorded on the server and used for evaluation.

[0372] Step 11:

[0373] The server evaluates the responses and generates appropriate feedback based on the user's emotional information obtained from the emotion engine. This feedback includes encouraging messages and guidance for the next steps.

[0374] Step 12:

[0375] The terminal displays feedback from the server to the user. Based on this feedback, the user can check their progress and move on to the next problem.

[0376] (Example 2)

[0377] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0378] There is a need to maximize the effectiveness of memory enhancement and improve the user experience by providing memory enhancement tasks based on individual user information and responding flexibly according to the user's emotional state. Conventional technologies have difficulty providing tasks and feedback that adequately consider individual user information and emotional states, resulting in a lack of highly effective memory enhancement methods.

[0379] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0380] In this invention, the server includes means for acquiring information, means for generating tasks for memory enhancement, means for recognizing the user's emotions, means for adjusting the difficulty of the tasks using emotional information, means for receiving and evaluating the user's responses, and means for providing the evaluation results as feedback. This enables effective and personalized memory enhancement based on individual user information and real-time emotional states.

[0381] "Means of acquiring information" refers to functions for collecting data provided by users and inputting it into the system.

[0382] "Means for generating tasks to enhance memory" refers to a function that creates questions and quizzes tailored to the user based on the information provided by the user.

[0383] "Means of recognizing user emotions" refers to technologies that analyze a user's voice, facial expressions, and biometric information to determine their current emotional state.

[0384] "Means of adjusting problem difficulty using emotional information" refers to a function that takes the user's emotional state into consideration and changes the difficulty level of a problem to an appropriate level.

[0385] "Means for receiving and evaluating user responses" refers to a function that allows the system to receive responses submitted by users to assigned tasks and to determine their accuracy and relevance.

[0386] "Means of providing evaluation results as feedback" refers to a function that allows the system to return appropriate information and advice to the user based on the evaluation of the user's responses.

[0387] This system aims to provide memory enhancement tasks tailored to each user's individual information, and to offer feedback that takes emotions into account. By utilizing advanced generative AI models and emotion recognition technology, it provides a learning experience that matches the user's characteristics.

[0388] Specifically, users input personal information required by cognitive functions via their devices. This includes hobbies, past experiences, visual materials, and favorite music and videos. This information serves as foundational data for highly personalized user experiences.

[0389] The terminal organizes the input information and sends it to the server using a secure communication method. The server stores the information in a database and dynamically generates tasks for memory enhancement using a generative AI model. In this generation process, for example, natural language processing technology is used to determine the task content using prompt sentences that are aligned with the user's interests.

[0390] Furthermore, the device's built-in emotion recognition technology analyzes the user's voice and visual information to identify their emotional state in real time. This emotional information is sent to a server and used to generate tasks and feedback.

[0391] The generative AI model has the ability to detect the user's emotional state and adjust the difficulty and content of the task accordingly. For example, if the user is feeling stressed, the server will reduce the complexity of the problem and generate feedback that helps the user relax.

[0392] As a concrete example, a prompt might be an instruction such as, "Generate a memory problem on a topic of interest to the user, with relaxing content." This prompt is processed by a generative AI model, resulting in the creation of personalized problems.

[0393] In this way, the system creates a personalized learning and feedback experience based on the information entered by the user and real-time sentiment data.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] Users enter personal information using their devices. This information includes their hobbies, background, visual data, and favorite music and videos. This data serves as the basis for personalizing memory enhancement tasks.

[0397] Step 2:

[0398] The terminal transmits information obtained from the user to the server using a secure protocol. This process converts the input information into an appropriate format to ensure smooth data processing on the server side. The output is user information converted into a format that the server can accept.

[0399] Step 3:

[0400] The server stores the received user information in a database. This stored information becomes the data source used by the generating AI model. This data is used to reflect the individual needs of the user in order to generate memory enhancement tasks.

[0401] Step 4:

[0402] The server generates memory enhancement tasks using a generative AI model based on stored information. Specifically, the AI ​​model analyzes prompts based on the user's interests and determines the task content. The input is user information from the database, and the output is a task tailored to the user.

[0403] Step 5:

[0404] The device's built-in emotion engine recognizes the user's emotions. The device acquires the user's voice and facial expression data and analyzes their emotional state. This analysis result is sent to the server as the user's most recent emotional state. The results of the emotion analysis influence the generated tasks and feedback. The output is data indicating the emotional state.

[0405] Step 6:

[0406] The server sends the generated task to the terminal. The terminal displays this task to the user and accepts the user's response. The output is a presentation of the task with an interface for the user to input their answer.

[0407] Step 7:

[0408] The user's response is sent from the terminal to the server, which then evaluates the response. The server further considers the user's sentiment information to generate optimal feedback. The input is the user's response and sentiment information, and the output is the feedback content.

[0409] Step 8:

[0410] The terminal displays feedback provided by the server to the user. By receiving this feedback, the user can understand areas for further learning and improvement. The output is a display of feedback to the user.

[0411] (Application Example 2)

[0412] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0413] In modern work environments, worker stress and fatigue are factors that lead to decreased productivity and safety risks. To prevent this and improve the work environment, a system is needed that can recognize workers' emotional states in real time and respond appropriately to individual situations. However, current technology does not adequately provide real-time responses based on emotional states, and a more effective solution is needed.

[0414] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0415] In this invention, the server includes a device for acquiring information, a device for recognizing emotional states in real time, and a device for adjusting the difficulty level of tasks based on the emotional states. This enables dynamic adjustment of tasks and appropriate feedback in accordance with the emotional states of individual workers in the work environment.

[0416] A "device for acquiring information" is a device for accumulating individual user information and has the function of collecting diverse information, including hobbies, background, and biometric data.

[0417] A "device for generating tasks to enhance memory" is a device that, based on acquired information, presents customized tasks aimed at improving the user's memory, utilizing AI to design optimal tasks.

[0418] A "device for presenting tasks to users" is a device that presents generated tasks to users through means such as sight and hearing, enabling them to effectively tackle the tasks.

[0419] A "device that receives and analyzes user responses" is a device that receives user answers to presented tasks and analyzes their accuracy, speed, and content.

[0420] A "feedback device" is a device that provides appropriate comments and advice tailored to the user based on the analysis results, contributing to the improvement of the user's understanding and memory.

[0421] A "device that recognizes emotional states in real time" is a device that detects the user's facial expressions, voice tone, and biosignals, analyzes that data, and determines the user's emotional state in real time.

[0422] A "task difficulty adjustment device" is a device that appropriately changes the difficulty and content of a task while taking into account the user's emotional state and stress level, so that the user can work on the task without difficulty.

[0423] The system for realizing this invention mainly consists of a server, a terminal, and a user.

[0424] The server uses generative AI models to collect information and generate customized tasks for each user. The server utilizes storage media to accumulate each user's hobbies, background, and past performance data. It also uses an intelligent engine to process user response data for analysis and generate appropriate feedback. This process employs database management systems and specialized generative AI software. Specifically, machine learning libraries are used for data analysis.

[0425] The device is responsible for recognizing the user's emotional state in real time. For emotion recognition, facial recognition software is used for facial expression analysis, and a speech recognition platform is used for voice analysis. The device has built-in sensors and a camera, and it sends the data acquired through these to a server, where the analysis results are provided to the user via the display.

[0426] Users work on tasks presented through their devices and provide answers. The device monitors the user's reactions, performance, and emotional state. For example, if the server detects high stress levels in the user, it uses a generative AI model to generate feedback such as, "We recommend you reduce the workload or take a break."

[0427] A concrete example of a prompt message is, "Based on the worker's facial expression data and biometric information, fatigue has been detected. Suggest an appropriate method for refreshing the worker." This prompt message is input into a generative AI model and used to automatically generate appropriate countermeasures.

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The device collects personal information from the user. This information includes hobbies, background, and past performance data. The collected information is obtained through sensors and input interfaces and transmitted to a server. This information processing prepares the foundational data for generating tasks tailored to the user's characteristics.

[0431] Step 2:

[0432] The device recognizes the user's emotions in real time. It utilizes a voice recognition platform and facial recognition software to acquire facial expressions, voice tone, and biometric information via sensors. The acquired emotional data is processed on the device and sent to the server as the user's current emotional state. This prepares the device for adjusting tasks according to the user's emotional state.

[0433] Step 3:

[0434] The server stores the received information and generates tasks optimized for each user. A generation AI model is used to determine the task content and difficulty level based on the input information. Data analysis is then used to evaluate the user's performance trends and design appropriate tasks. The generated tasks are sent to the terminal.

[0435] Step 4:

[0436] The device presents the user with a generated task. The content of the task is clearly communicated to the user through the display and audio output. The user begins working on the presented task, and the device monitors the user's response at this point.

[0437] Step 5:

[0438] The user works on a task and enters their answer into the device. Data about the user's activity (accuracy of the answer, time taken, etc.) is also acquired at the same time. The device sends this data to the server.

[0439] Step 6:

[0440] The server analyzes the user's responses and evaluates the results. An intelligent engine is used to calculate the accuracy and time taken for the responses, and compare them to expected performance. The evaluation results are then sent back to the terminal.

[0441] Step 7:

[0442] The device generates appropriate feedback based on the evaluation results and provides it to the user. This includes suggestions for refreshing the user based on their emotional state and advice for the next steps. Using prompts generated by an AI model, the system advises the user on the best course of action.

[0443] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0444] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0449] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0450] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0451] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0452] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0453] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0454] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0455] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0456] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0457] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0458] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0459] The system of this invention acquires user information and generates and provides memory-enhancing problems based on that information. This system aims to enhance the memory of dementia patients by registering various information related to the user and using it to generate personalized problems.

[0460] First, the user uses a device to input information about the patient's hobbies, history, favorite TV shows and music, photos, etc. This information serves as the basis for generating problems based on the patient's individual background and interests.

[0461] Once this information is entered, the terminal sends it to the server. The server stores the received information in a database and starts the process of calling a generative AI to generate questions. The generative AI generates questions designed to stimulate the patient's memory, and these questions may be in the form of text, images, music, etc.

[0462] Next, the generated question is presented to the user via the device. The user answers the question and sends the answer to the server via the device. The server evaluates the answer and determines whether it is correct or not. Based on the result, feedback is generated and provided to the user via the device. This feedback includes praise for correct answers and the correct answer for incorrect answers, and serves to further stimulate the user's memory.

[0463] As a concrete example, consider a patient who enjoys gardening and classical music. In this case, the server generates questions using a generative AI, such as "What is the name of this plant?" or "Who composed this classical piece?" The user answers these questions on their device, and feedback such as the accuracy rate and additional information is provided. This enhances the patient's memory in a game-like manner and also promotes natural communication with family members.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The user enters information about the dementia patient into the terminal. Specifically, they enter information such as the patient's hobbies, history, favorite music, TV shows, and photos, and prepare to send it.

[0467] Step 2:

[0468] The terminal formats the collected user information and sends it to the server. To ensure data consistency and security, the data is encrypted and checksums are added.

[0469] Step 3:

[0470] The server receives user information sent from the terminal and efficiently stores it in the database. Before saving, it performs data integrity and duplicate checks.

[0471] Step 4:

[0472] The server uses a generative AI to generate personalized memory-enhancing questions based on stored user information. The generation process analyzes the input information to determine appropriate quiz themes and formats.

[0473] Step 5:

[0474] Problems generated by a generation AI are sent from the server to the terminal. These problems may include text, images, music clips, and more.

[0475] Step 6:

[0476] The terminal presents the received problem to the user. The user reads the problem on the screen and enters their answer according to the instructions.

[0477] Step 7:

[0478] When a user enters a response, the device sends that response to the server. To reduce latency, the data is processed simultaneously by multiple server processes.

[0479] Step 8:

[0480] The server receives the user's response, compares it against a database of correct answers, and evaluates it. The evaluation results, including the accuracy rate and reaction time, are recorded, and a feedback message is generated.

[0481] Step 9:

[0482] The server sends the generated feedback to the terminal. The feedback includes confirmation of whether the answer is correct or incorrect, as well as additional information.

[0483] Step 10:

[0484] The device displays feedback sent from the server to the user, allowing them to understand their progress and areas for improvement in their memory.

[0485] (Example 1)

[0486] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] The present invention aims to provide an efficient and personalized method for improving the memory of dementia patients. Conventional methods only provide general problems, making it difficult to generate problems tailored to the individual patient's interests and background. As a result, it is difficult to capture the patient's attention and effectively improve their memory.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0489] In this invention, the server includes means for acquiring information and transmitting said information to a central processing unit, a storage medium for storing the acquired information, means for calling a generation engine that generates problems for memory enhancement based on said information, and means for presenting the generated problems to the user via an output device. This makes it possible to generate and present problems based on the individual's hobbies and preferences, and can effectively support the improvement of the patient's memory.

[0490] "Means for acquiring information and transmitting said information to the central processing unit" refers to a function for collecting input information from users and transmitting it to the central processing unit.

[0491] "Storage medium for storing acquired information" refers to a physical or logical storage device that holds information acquired from users and allows it to be retrieved and used as needed.

[0492] "Means of calling a generation engine that generates questions for memory enhancement" refers to the function of starting and executing a program or algorithm unit that automatically creates questions tailored to individual users.

[0493] "Means of presenting the generated problem to the user via an output device" refers to a notification function through an interface device such as a display or speaker to show the created problem to the user visually or audibly.

[0494] A "prompt statement that instructs problem generation" refers to an instruction or command statement used to cause a generation engine or artificial intelligence to generate a specific problem.

[0495] This invention provides a system to help enhance memory. The user uses a terminal to input information about the dementia patient, such as their hobbies, history, favorite TV programs, music, and photographs. This input serves as the basis for creating personalized questions for the patient.

[0496] The terminal sends the information entered by the user to the server. The data is transmitted using a secure protocol such as HTTPS. The server stores this information in a database.

[0497] After the information is saved, the server invokes a generative AI model to generate questions for memory enhancement. This generative AI uses deep learning and natural language processing techniques to generate questions that stimulate the patient's memory in various media formats such as text, images, and music.

[0498] The generated questions are presented to the user via the device. The user can answer the questions on the device, and the answers are sent back to the server. The server evaluates the answers and generates feedback, along with a determination of whether they are correct or incorrect. The feedback includes praise for correct answers and the correct answer for incorrect answers.

[0499] As a concrete example, if a patient enjoys gardening and classical music, the prompt could be phrased as follows: "The user's hobbies are gardening and classical music. Based on this information, please generate questions to stimulate memory." This prompt would then prompt the AI ​​to create questions such as, "What is the name of this plant?" or "Who composed this classical piece?" By answering these questions, the user can improve their memory in a game-like manner and also facilitate communication with family members.

[0500] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0501] Step 1:

[0502] The user uses a terminal to input information about the dementia patient, such as their hobbies, history, favorite TV shows, music, and photos. This input information is treated as foundational data for the system to generate personalized problems. In practice, the user enters the necessary information into a form via a GUI and clicks the "Submit" button.

[0503] Step 2:

[0504] The terminal sends information obtained from the user to the server. The input data, such as text information about hobbies and preferences, is securely transmitted to the server using the HTTPS protocol. This protocol ensures the confidentiality of the data.

[0505] Step 3:

[0506] The server stores the received information in a database. The received data is stored in temporary variables for processing and then added to the database using SQL queries. The database is designed to allow for quick retrieval and searching of information.

[0507] Step 4:

[0508] The server invokes a generation AI model based on information stored in the database. It generates a prompt from the stored data and sends instructions to the generation AI such as, "The user's hobbies are gardening and they like classical music. Based on this information, please generate a question to stimulate their memory." The AI ​​then processes the question generation based on this prompt.

[0509] Step 5:

[0510] The generative AI model analyzes the prompt text and generates personalized problems. This prompt-based problem generation process involves creating problems in various formats, such as text, images, and music, through natural language processing algorithms. The generated problem data is returned to the server.

[0511] Step 6:

[0512] The server sends the generated problem to the terminal. The generated result data is sent to the terminal as an HTTP response, making it accessible to the user.

[0513] Step 7:

[0514] The terminal presents the received problem to the user. The problem is displayed directly on the terminal's screen as text or an image, and the user inputs their answer. The user interface for solving the problem is crucial here.

[0515] Step 8:

[0516] The user answers the presented questions and sends their answers to the server via their device. Users can do so by clicking on the options or by entering their answers in the input form and clicking the "Submit Answer" button.

[0517] Step 9:

[0518] The server receives responses from users and evaluates whether they are correct. It processes the received response data using an evaluation algorithm and generates feedback data based on the results.

[0519] Step 10:

[0520] Feedback from the server is sent to the device, which then provides this feedback to the user. The feedback is displayed on the screen as text or sound, which the user can review, and is praised for correct answers and given the correct answer for incorrect answers.

[0521] (Application Example 1)

[0522] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] While numerous training tools exist today aimed at improving individual memory, few offer personalized experiences tailored to individual interests. Furthermore, there is a growing need to integrate memory training with customer experiences in physical spaces such as retail stores and commercial facilities to provide a richer and more valuable experience.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0525] In this invention, the server includes means for acquiring information, means for providing products and experiences tailored to an individual's interests, and means for providing measurements related to the products and experiences and enhancing the experience. This makes it possible to provide personalized memory-enhancing experiences based on an individual's interests in a physical store.

[0526] "Means of acquiring information" refers to devices or methods for collecting data related to an individual's hobbies and interests.

[0527] "Means for generating measurements" refers to a process or apparatus for creating personalized memory improvement questions or quizzes based on acquired information.

[0528] "Means of presenting to an individual" refers to a method or device for displaying generated measurements to a user and soliciting their response.

[0529] "Means of evaluation" refers to methods or devices for collecting individual responses and analyzing their accuracy and quality.

[0530] "Means of providing feedback" refers to a process or device that provides additional information or comments to an individual based on the evaluation results.

[0531] "Means of providing goods or experiences" refers to methods or devices that provide relevant products or experiential value based on an individual's interests.

[0532] "Means of enhancing experience" refers to the process or device of designing an experience to contribute to improving an individual's memory through the products or experiences provided.

[0533] This invention is a system for enhancing individual memory in a physical store environment, collecting information and providing a personalized experience. The system consists of three elements: a server, a terminal, and a user.

[0534] Users input information about their hobbies and interests using devices such as smartphones or smart glasses. This input information is sent from the device to the server. The server uses a database and a generative AI model to generate personalized questions based on the received information. This generative AI model could be, for example, GPT, which uses natural language processing technology. An example of a prompt would be, "Your interest is art. Please create a quiz related to the paintings in the store. Example: Who painted this artwork?"

[0535] The generated questions are returned to the terminal and presented to the user. The user answers the presented quiz, and the results are sent back to the server. The server evaluates the received answers and determines correctness using data calculations such as CT. This evaluation result is provided to the user as feedback via the terminal, and the correct answers and additional information are displayed to enhance memory.

[0536] As a concrete example, an art-loving user could scan a specific painting in the store, and then be presented with a question related to that painting. By answering, the user would immediately receive relevant information and praise, enriching their in-store experience.

[0537] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0538] Step 1:

[0539] Users use a device to input information about their hobbies and interests. Specifically, they use the input interface of a smartphone or smart glasses to enter information about their interests such as music, art, and fashion, and the device collects this as digital data. This data is treated as text information that embodies the user's interests.

[0540] Step 2:

[0541] The terminal sends the entered information to the cloud server. Here, the digital information (input text) is converted into prompt data and securely sent to the server via a communication protocol. This prompt data is then ready to be input into the generating AI model.

[0542] Step 3:

[0543] The server uses a generative AI model based on the received data to generate personalized questions. The generative AI model uses a question generation algorithm to perform natural language processing according to the received prompt data and generates highly relevant quizzes in text format. This generation process may produce specific questions such as, "Your interest is music. Who composed this song?"

[0544] Step 4:

[0545] The server sends the generated problems to the terminal. The output problem data is converted into a format for display on the terminal and sent to the user's terminal.

[0546] Step 5:

[0547] The device displays the received question on the screen and prompts the user to answer. The user enters their answer to the quiz and submits the result. This input data is compiled within the device as the user's answer.

[0548] Step 6:

[0549] The terminal sends the user's response to the server. The response data is then sent back to the server via a communication protocol.

[0550] Step 7:

[0551] The server evaluates the received response. It analyzes the input data using an algorithm that compares it against a database of correct answers and determines whether it is correct or incorrect. For example, if the answer is correct, a feedback message such as "Correct!" is generated; if it is incorrect, a message such as "Unfortunately, the correct answer is XX" is generated.

[0552] Step 8:

[0553] The server sends the generated feedback along with the evaluation results to the terminal. This feedback data includes suggestions for future actions, praise, and correction information based on the evaluation results.

[0554] Step 9:

[0555] The device presents feedback to the user. The results are displayed on the user's screen, and actions, including additional information and praise, are recommended. This feedback allows the user to enhance their memory while simultaneously acquiring product information and related experiences.

[0556] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0557] This invention aims to improve memory enhancement and user experience by combining a system that acquires user information, generates and provides memory-enhancing problems, and further incorporates an emotion engine that recognizes the user's emotions.

[0558] First, the user uses a terminal to input detailed information about the dementia patient. This includes hobbies, background, photos, favorite music and TV shows, etc. This information forms the basis for the system to personalize memory enhancement tasks.

[0559] The terminal sends information obtained from the user to the server. The server stores the transmitted information in a database and uses a generative AI to generate questions for memory enhancement. The generative AI analyzes the input information and determines the optimal quiz theme and format.

[0560] Furthermore, an emotion engine built into the device recognizes the user's emotions. The emotion engine determines the user's current emotional state using biometric information, voice analysis, facial expression analysis, and other methods. This emotional information is processed on the server and reflected in the generated problems and feedback.

[0561] The generated memory-enhancing questions are provided to the user via a device. The user answers the presented questions and sends their answers to the server via the device. The server evaluates the answers and generates appropriate feedback based on the user's emotional information.

[0562] For example, if the server determines that a user is experiencing stress, it can adjust the difficulty level of the problem based on emotional information and provide relaxing feedback. This allows for flexible responses tailored to the user's mood, enhancing memory enhancement and improving the user experience.

[0563] Thus, the system of the present invention provides a more personalized experience by offering problems and feedback aimed at enhancing memory based on the user's individual information and real-time emotional state.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] Users input information about dementia patients using a terminal. The terminal provides an interface for inputting data such as the patient's hobbies, history, favorite music and TV shows, and photos.

[0567] Step 2:

[0568] The terminal formats the information entered by the user and sends it to the server. The information is transmitted in an encrypted format using a secure communication protocol.

[0569] Step 3:

[0570] The server stores the received information in a database. The database is used to efficiently manage information for each patient and utilize it for subsequent processing.

[0571] Step 4:

[0572] The server uses information retrieved from the database to activate a generative AI and begin the process of generating memory enhancement problems. The generative AI creates problems tailored to the individual patient's background.

[0573] Step 5:

[0574] The emotion engine built into the device collects data to recognize the user's emotions. This is done by analyzing the user's voice and facial expressions using biosensors, cameras, and other devices.

[0575] Step 6:

[0576] The device sends emotional information collected by the emotion engine to the server. This emotional data is also encrypted before being sent to the server.

[0577] Step 7:

[0578] The server combines generated problems with user sentiment information to make adjustments. Specifically, it adjusts the difficulty level of the problems according to the user's mental state and selects an appropriate set of problems.

[0579] Step 8:

[0580] The device presents the user with tailored problems, allowing them to engage with interesting problems in a relaxed manner.

[0581] Step 9:

[0582] The user enters their answers to questions displayed on the device. Answers can be entered in multiple-choice or free-text format.

[0583] Step 10:

[0584] The terminal sends the user's response to the server. The response data is recorded on the server and used for evaluation.

[0585] Step 11:

[0586] The server evaluates the responses and generates appropriate feedback based on the user's emotional information obtained from the emotion engine. This feedback includes encouraging messages and guidance for the next steps.

[0587] Step 12:

[0588] The terminal displays feedback from the server to the user. Based on this feedback, the user can check their progress and move on to the next problem.

[0589] (Example 2)

[0590] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0591] There is a need to maximize the effectiveness of memory enhancement and improve the user experience by providing memory enhancement tasks based on individual user information and responding flexibly according to the user's emotional state. Conventional technologies have difficulty providing tasks and feedback that adequately consider individual user information and emotional states, resulting in a lack of highly effective memory enhancement methods.

[0592] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0593] In this invention, the server includes means for acquiring information, means for generating tasks for memory enhancement, means for recognizing the user's emotions, means for adjusting the difficulty of the tasks using emotional information, means for receiving and evaluating the user's responses, and means for providing the evaluation results as feedback. This enables effective and personalized memory enhancement based on individual user information and real-time emotional states.

[0594] "Means of acquiring information" refers to functions for collecting data provided by users and inputting it into the system.

[0595] "Means for generating tasks to enhance memory" refers to a function that creates questions and quizzes tailored to the user based on the information provided by the user.

[0596] "Means of recognizing user emotions" refers to technologies that analyze a user's voice, facial expressions, and biometric information to determine their current emotional state.

[0597] "Means of adjusting problem difficulty using emotional information" refers to a function that takes the user's emotional state into consideration and changes the difficulty level of a problem to an appropriate level.

[0598] "Means for receiving and evaluating user responses" refers to a function that allows the system to receive responses submitted by users to assigned tasks and to determine their accuracy and relevance.

[0599] "Means of providing evaluation results as feedback" refers to a function that allows the system to return appropriate information and advice to the user based on the evaluation of the user's responses.

[0600] This system aims to provide memory enhancement tasks tailored to each user's individual information, and to offer feedback that takes emotions into account. By utilizing advanced generative AI models and emotion recognition technology, it provides a learning experience that matches the user's characteristics.

[0601] Specifically, users input personal information required by cognitive functions via their devices. This includes hobbies, past experiences, visual materials, and favorite music and videos. This information serves as foundational data for highly personalized user experiences.

[0602] The terminal organizes the input information and sends it to the server using a secure communication method. The server stores the information in a database and dynamically generates tasks for memory enhancement using a generative AI model. In this generation process, for example, natural language processing technology is used to determine the task content using prompt sentences that are aligned with the user's interests.

[0603] Furthermore, the device's built-in emotion recognition technology analyzes the user's voice and visual information to identify their emotional state in real time. This emotional information is sent to a server and used to generate tasks and feedback.

[0604] The generative AI model has the ability to detect the user's emotional state and adjust the difficulty and content of the task accordingly. For example, if the user is feeling stressed, the server will reduce the complexity of the problem and generate feedback that helps the user relax.

[0605] As a concrete example, a prompt might be an instruction such as, "Generate a memory problem on a topic of interest to the user, with relaxing content." This prompt is processed by a generative AI model, resulting in the creation of personalized problems.

[0606] In this way, the system creates a personalized learning and feedback experience based on the information entered by the user and real-time sentiment data.

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] Users enter personal information using their devices. This information includes their hobbies, background, visual data, and favorite music and videos. This data serves as the basis for personalizing memory enhancement tasks.

[0610] Step 2:

[0611] The terminal transmits information obtained from the user to the server using a secure protocol. This process converts the input information into an appropriate format to ensure smooth data processing on the server side. The output is user information converted into a format that the server can accept.

[0612] Step 3:

[0613] The server stores the received user information in a database. This stored information becomes the data source used by the generating AI model. This data is used to reflect the individual needs of the user in order to generate memory enhancement tasks.

[0614] Step 4:

[0615] The server generates memory enhancement tasks using a generative AI model based on stored information. Specifically, the AI ​​model analyzes prompts based on the user's interests and determines the task content. The input is user information from the database, and the output is a task tailored to the user.

[0616] Step 5:

[0617] The device's built-in emotion engine recognizes the user's emotions. The device acquires the user's voice and facial expression data and analyzes their emotional state. This analysis result is sent to the server as the user's most recent emotional state. The results of the emotion analysis influence the generated tasks and feedback. The output is data indicating the emotional state.

[0618] Step 6:

[0619] The server sends the generated task to the terminal. The terminal displays this task to the user and accepts the user's response. The output is a presentation of the task with an interface for the user to input their answer.

[0620] Step 7:

[0621] The user's response is sent from the terminal to the server, which then evaluates the response. The server further considers the user's sentiment information to generate optimal feedback. The input is the user's response and sentiment information, and the output is the feedback content.

[0622] Step 8:

[0623] The terminal displays feedback provided by the server to the user. By receiving this feedback, the user can understand areas for further learning and improvement. The output is a display of feedback to the user.

[0624] (Application Example 2)

[0625] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0626] In modern work environments, worker stress and fatigue are factors that lead to decreased productivity and safety risks. To prevent this and improve the work environment, a system is needed that can recognize workers' emotional states in real time and respond appropriately to individual situations. However, current technology does not adequately provide real-time responses based on emotional states, and a more effective solution is needed.

[0627] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0628] In this invention, the server includes a device for acquiring information, a device for recognizing emotional states in real time, and a device for adjusting the difficulty level of tasks based on the emotional states. This enables dynamic adjustment of tasks and appropriate feedback in accordance with the emotional states of individual workers in the work environment.

[0629] A "device for acquiring information" is a device for accumulating individual user information and has the function of collecting diverse information, including hobbies, background, and biometric data.

[0630] A "device for generating tasks to enhance memory" is a device that, based on acquired information, presents customized tasks aimed at improving the user's memory, utilizing AI to design optimal tasks.

[0631] A "device for presenting tasks to users" is a device that presents generated tasks to users through means such as sight and hearing, enabling them to effectively tackle the tasks.

[0632] A "device that receives and analyzes user responses" is a device that receives user answers to presented tasks and analyzes their accuracy, speed, and content.

[0633] A "feedback device" is a device that provides appropriate comments and advice tailored to the user based on the analysis results, contributing to the improvement of the user's understanding and memory.

[0634] A "device that recognizes emotional states in real time" is a device that detects the user's facial expressions, voice tone, and biosignals, analyzes that data, and determines the user's emotional state in real time.

[0635] A "task difficulty adjustment device" is a device that appropriately changes the difficulty and content of a task while taking into account the user's emotional state and stress level, so that the user can work on the task without difficulty.

[0636] The system for realizing this invention mainly consists of a server, a terminal, and a user.

[0637] The server uses generative AI models to collect information and generate customized tasks for each user. The server utilizes storage media to accumulate each user's hobbies, background, and past performance data. It also uses an intelligent engine to process user response data for analysis and generate appropriate feedback. This process employs database management systems and specialized generative AI software. Specifically, machine learning libraries are used for data analysis.

[0638] The device is responsible for recognizing the user's emotional state in real time. For emotion recognition, facial recognition software is used for facial expression analysis, and a speech recognition platform is used for voice analysis. The device has built-in sensors and a camera, and it sends the data acquired through these to a server, where the analysis results are provided to the user via the display.

[0639] Users work on tasks presented through their devices and provide answers. The device monitors the user's reactions, performance, and emotional state. For example, if the server detects high stress levels in the user, it uses a generative AI model to generate feedback such as, "We recommend you reduce the workload or take a break."

[0640] A concrete example of a prompt message is, "Based on the worker's facial expression data and biometric information, fatigue has been detected. Suggest an appropriate method for refreshing the worker." This prompt message is input into a generative AI model and used to automatically generate appropriate countermeasures.

[0641] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0642] Step 1:

[0643] The device collects personal information from the user. This information includes hobbies, background, and past performance data. The collected information is obtained through sensors and input interfaces and transmitted to a server. This information processing prepares the foundational data for generating tasks tailored to the user's characteristics.

[0644] Step 2:

[0645] The device recognizes the user's emotions in real time. It utilizes a voice recognition platform and facial recognition software to acquire facial expressions, voice tone, and biometric information via sensors. The acquired emotional data is processed on the device and sent to the server as the user's current emotional state. This prepares the device for adjusting tasks according to the user's emotional state.

[0646] Step 3:

[0647] The server stores the received information and generates tasks optimized for each user. A generation AI model is used to determine the task content and difficulty level based on the input information. Data analysis is then used to evaluate the user's performance trends and design appropriate tasks. The generated tasks are sent to the terminal.

[0648] Step 4:

[0649] The device presents the user with a generated task. The content of the task is clearly communicated to the user through the display and audio output. The user begins working on the presented task, and the device monitors the user's response at this point.

[0650] Step 5:

[0651] The user works on a task and enters their answer into the device. Data about the user's activity (accuracy of the answer, time taken, etc.) is also acquired at the same time. The device sends this data to the server.

[0652] Step 6:

[0653] The server analyzes the user's responses and evaluates the results. An intelligent engine is used to calculate the accuracy and time taken for the responses, and compare them to expected performance. The evaluation results are then sent back to the terminal.

[0654] Step 7:

[0655] The device generates appropriate feedback based on the evaluation results and provides it to the user. This includes suggestions for refreshing the user based on their emotional state and advice for the next steps. Using prompts generated by an AI model, the system advises the user on the best course of action.

[0656] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0657] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0658] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0662] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0663] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0664] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0665] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0666] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0667] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0668] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0669] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0670] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0671] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0672] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] The system of this invention acquires user information and generates and provides memory-enhancing problems based on that information. This system aims to enhance the memory of dementia patients by registering various information related to the user and using it to generate personalized problems.

[0674] First, the user uses a device to input information about the patient's hobbies, history, favorite TV shows and music, photos, etc. This information serves as the basis for generating problems based on the patient's individual background and interests.

[0675] Once this information is entered, the terminal sends it to the server. The server stores the received information in a database and starts the process of calling a generative AI to generate questions. The generative AI generates questions designed to stimulate the patient's memory, and these questions may be in the form of text, images, music, etc.

[0676] Next, the generated question is presented to the user via the device. The user answers the question and sends the answer to the server via the device. The server evaluates the answer and determines whether it is correct or not. Based on the result, feedback is generated and provided to the user via the device. This feedback includes praise for correct answers and the correct answer for incorrect answers, and serves to further stimulate the user's memory.

[0677] As a concrete example, consider a patient who enjoys gardening and classical music. In this case, the server generates questions using a generative AI, such as "What is the name of this plant?" or "Who composed this classical piece?" The user answers these questions on their device, and feedback such as the accuracy rate and additional information is provided. This enhances the patient's memory in a game-like manner and also promotes natural communication with family members.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The user enters information about the dementia patient into the terminal. Specifically, they enter information such as the patient's hobbies, history, favorite music, TV shows, and photos, and prepare to send it.

[0681] Step 2:

[0682] The terminal formats the collected user information and sends it to the server. To ensure data consistency and security, the data is encrypted and checksums are added.

[0683] Step 3:

[0684] The server receives user information sent from the terminal and efficiently stores it in the database. Before saving, it performs data integrity and duplicate checks.

[0685] Step 4:

[0686] The server uses a generative AI to generate personalized memory-enhancing questions based on stored user information. The generation process analyzes the input information to determine appropriate quiz themes and formats.

[0687] Step 5:

[0688] Problems generated by a generation AI are sent from the server to the terminal. These problems may include text, images, music clips, and more.

[0689] Step 6:

[0690] The terminal presents the received problem to the user. The user reads the problem on the screen and enters their answer according to the instructions.

[0691] Step 7:

[0692] When a user enters a response, the device sends that response to the server. To reduce latency, the data is processed simultaneously by multiple server processes.

[0693] Step 8:

[0694] The server receives the user's response, compares it against a database of correct answers, and evaluates it. The evaluation results, including the accuracy rate and reaction time, are recorded, and a feedback message is generated.

[0695] Step 9:

[0696] The server sends the generated feedback to the terminal. The feedback includes confirmation of whether the answer is correct or incorrect, as well as additional information.

[0697] Step 10:

[0698] The device displays feedback sent from the server to the user, allowing them to understand their progress and areas for improvement in their memory.

[0699] (Example 1)

[0700] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] The present invention aims to provide an efficient and personalized method for improving the memory of dementia patients. Conventional methods only provide general problems, making it difficult to generate problems tailored to the individual patient's interests and background. As a result, it is difficult to capture the patient's attention and effectively improve their memory.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0703] In this invention, the server includes means for acquiring information and transmitting said information to a central processing unit, a storage medium for storing the acquired information, means for calling a generation engine that generates problems for memory enhancement based on said information, and means for presenting the generated problems to the user via an output device. This makes it possible to generate and present problems based on the individual's hobbies and preferences, and can effectively support the improvement of the patient's memory.

[0704] "Means for acquiring information and transmitting said information to the central processing unit" refers to a function for collecting input information from users and transmitting it to the central processing unit.

[0705] "Storage medium for storing acquired information" refers to a physical or logical storage device that holds information acquired from users and allows it to be retrieved and used as needed.

[0706] "Means of calling a generation engine that generates questions for memory enhancement" refers to the function of starting and executing a program or algorithm unit that automatically creates questions tailored to individual users.

[0707] "Means of presenting the generated problem to the user via an output device" refers to a notification function through an interface device such as a display or speaker to show the created problem to the user visually or audibly.

[0708] A "prompt statement that instructs problem generation" refers to an instruction or command statement used to cause a generation engine or artificial intelligence to generate a specific problem.

[0709] This invention provides a system to help enhance memory. The user uses a terminal to input information about the dementia patient, such as their hobbies, history, favorite TV programs, music, and photographs. This input serves as the basis for creating personalized questions for the patient.

[0710] The terminal sends the information entered by the user to the server. The data is transmitted using a secure protocol such as HTTPS. The server stores this information in a database.

[0711] After the information is saved, the server invokes a generative AI model to generate questions for memory enhancement. This generative AI uses deep learning and natural language processing techniques to generate questions that stimulate the patient's memory in various media formats such as text, images, and music.

[0712] The generated questions are presented to the user via the device. The user can answer the questions on the device, and the answers are sent back to the server. The server evaluates the answers and generates feedback, along with a determination of whether they are correct or incorrect. The feedback includes praise for correct answers and the correct answer for incorrect answers.

[0713] As a concrete example, if a patient enjoys gardening and classical music, the prompt could be phrased as follows: "The user's hobbies are gardening and classical music. Based on this information, please generate questions to stimulate memory." This prompt would then prompt the AI ​​to create questions such as, "What is the name of this plant?" or "Who composed this classical piece?" By answering these questions, the user can improve their memory in a game-like manner and also facilitate communication with family members.

[0714] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0715] Step 1:

[0716] The user uses a terminal to input information about the dementia patient, such as their hobbies, history, favorite TV shows, music, and photos. This input information is treated as foundational data for the system to generate personalized problems. In practice, the user enters the necessary information into a form via a GUI and clicks the "Submit" button.

[0717] Step 2:

[0718] The terminal sends information obtained from the user to the server. The input data, such as text information about hobbies and preferences, is securely transmitted to the server using the HTTPS protocol. This protocol ensures the confidentiality of the data.

[0719] Step 3:

[0720] The server stores the received information in a database. The received data is stored in temporary variables for processing and then added to the database using SQL queries. The database is designed to allow for quick retrieval and searching of information.

[0721] Step 4:

[0722] The server invokes a generation AI model based on information stored in the database. It generates a prompt from the stored data and sends instructions to the generation AI such as, "The user's hobbies are gardening and they like classical music. Based on this information, please generate a question to stimulate their memory." The AI ​​then processes the question generation based on this prompt.

[0723] Step 5:

[0724] The generative AI model analyzes the prompt text and generates personalized problems. This prompt-based problem generation process involves creating problems in various formats, such as text, images, and music, through natural language processing algorithms. The generated problem data is returned to the server.

[0725] Step 6:

[0726] The server sends the generated problem to the terminal. The generated result data is sent to the terminal as an HTTP response, making it accessible to the user.

[0727] Step 7:

[0728] The terminal presents the received problem to the user. The problem is displayed directly on the terminal's screen as text or an image, and the user inputs their answer. The user interface for solving the problem is crucial here.

[0729] Step 8:

[0730] The user answers the presented questions and sends their answers to the server via their device. Users can do so by clicking on the options or by entering their answers in the input form and clicking the "Submit Answer" button.

[0731] Step 9:

[0732] The server receives responses from users and evaluates whether they are correct. It processes the received response data using an evaluation algorithm and generates feedback data based on the results.

[0733] Step 10:

[0734] Feedback from the server is sent to the device, which then provides this feedback to the user. The feedback is displayed on the screen as text or sound, which the user can review, and is praised for correct answers and given the correct answer for incorrect answers.

[0735] (Application Example 1)

[0736] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0737] While numerous training tools exist today aimed at improving individual memory, few offer personalized experiences tailored to individual interests. Furthermore, there is a growing need to integrate memory training with customer experiences in physical spaces such as retail stores and commercial facilities to provide a richer and more valuable experience.

[0738] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0739] In this invention, the server includes means for acquiring information, means for providing products and experiences tailored to an individual's interests, and means for providing measurements related to the products and experiences and enhancing the experience. This makes it possible to provide personalized memory-enhancing experiences based on an individual's interests in a physical store.

[0740] "Means of acquiring information" refers to devices or methods for collecting data related to an individual's hobbies and interests.

[0741] "Means for generating measurements" refers to a process or apparatus for creating personalized memory improvement questions or quizzes based on acquired information.

[0742] "Means of presenting to an individual" refers to a method or device for displaying generated measurements to a user and soliciting their response.

[0743] "Means of evaluation" refers to methods or devices for collecting individual responses and analyzing their accuracy and quality.

[0744] "Means of providing feedback" refers to a process or device that provides additional information or comments to an individual based on the evaluation results.

[0745] "Means of providing goods or experiences" refers to methods or devices that provide relevant products or experiential value based on an individual's interests.

[0746] "Means of enhancing experience" refers to the process or device of designing an experience to contribute to improving an individual's memory through the products or experiences provided.

[0747] This invention is a system for enhancing individual memory in a physical store environment, collecting information and providing a personalized experience. The system consists of three elements: a server, a terminal, and a user.

[0748] Users input information about their hobbies and interests using devices such as smartphones or smart glasses. This input information is sent from the device to the server. The server uses a database and a generative AI model to generate personalized questions based on the received information. This generative AI model could be, for example, GPT, which uses natural language processing technology. An example of a prompt would be, "Your interest is art. Please create a quiz related to the paintings in the store. Example: Who painted this artwork?"

[0749] The generated questions are returned to the terminal and presented to the user. The user answers the presented quiz, and the results are sent back to the server. The server evaluates the received answers and determines correctness using data calculations such as CT. This evaluation result is provided to the user as feedback via the terminal, and the correct answers and additional information are displayed to enhance memory.

[0750] As a concrete example, an art-loving user could scan a specific painting in the store, and then be presented with a question related to that painting. By answering, the user would immediately receive relevant information and praise, enriching their in-store experience.

[0751] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0752] Step 1:

[0753] Users use a device to input information about their hobbies and interests. Specifically, they use the input interface of a smartphone or smart glasses to enter information about their interests such as music, art, and fashion, and the device collects this as digital data. This data is treated as text information that embodies the user's interests.

[0754] Step 2:

[0755] The terminal sends the entered information to the cloud server. Here, the digital information (input text) is converted into prompt data and securely sent to the server via a communication protocol. This prompt data is then ready to be input into the generating AI model.

[0756] Step 3:

[0757] The server uses a generative AI model based on the received data to generate personalized questions. The generative AI model uses a question generation algorithm to perform natural language processing according to the received prompt data and generates highly relevant quizzes in text format. This generation process may produce specific questions such as, "Your interest is music. Who composed this song?"

[0758] Step 4:

[0759] The server sends the generated problems to the terminal. The output problem data is converted into a format for display on the terminal and sent to the user's terminal.

[0760] Step 5:

[0761] The device displays the received question on the screen and prompts the user to answer. The user enters their answer to the quiz and submits the result. This input data is compiled within the device as the user's answer.

[0762] Step 6:

[0763] The terminal sends the user's response to the server. The response data is then sent back to the server via a communication protocol.

[0764] Step 7:

[0765] The server evaluates the received response. It analyzes the input data using an algorithm that compares it against a database of correct answers and determines whether it is correct or incorrect. For example, if the answer is correct, a feedback message such as "Correct!" is generated; if it is incorrect, a message such as "Unfortunately, the correct answer is XX" is generated.

[0766] Step 8:

[0767] The server sends the generated feedback along with the evaluation results to the terminal. This feedback data includes suggestions for future actions, praise, and correction information based on the evaluation results.

[0768] Step 9:

[0769] The device presents feedback to the user. The results are displayed on the user's screen, and actions, including additional information and praise, are recommended. This feedback allows the user to enhance their memory while simultaneously acquiring product information and related experiences.

[0770] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0771] This invention aims to improve memory enhancement and user experience by combining a system that acquires user information, generates and provides memory-enhancing problems, and further incorporates an emotion engine that recognizes the user's emotions.

[0772] First, the user uses a terminal to input detailed information about the dementia patient. This includes hobbies, background, photos, favorite music and TV shows, etc. This information forms the basis for the system to personalize memory enhancement tasks.

[0773] The terminal sends information obtained from the user to the server. The server stores the transmitted information in a database and uses a generative AI to generate questions for memory enhancement. The generative AI analyzes the input information and determines the optimal quiz theme and format.

[0774] Furthermore, an emotion engine built into the device recognizes the user's emotions. The emotion engine determines the user's current emotional state using biometric information, voice analysis, facial expression analysis, and other methods. This emotional information is processed on the server and reflected in the generated problems and feedback.

[0775] The generated memory-enhancing questions are provided to the user via a device. The user answers the presented questions and sends their answers to the server via the device. The server evaluates the answers and generates appropriate feedback based on the user's emotional information.

[0776] For example, if the server determines that a user is experiencing stress, it can adjust the difficulty level of the problem based on emotional information and provide relaxing feedback. This allows for flexible responses tailored to the user's mood, enhancing memory enhancement and improving the user experience.

[0777] Thus, the system of the present invention provides a more personalized experience by offering problems and feedback aimed at enhancing memory based on the user's individual information and real-time emotional state.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] Users input information about dementia patients using a terminal. The terminal provides an interface for inputting data such as the patient's hobbies, history, favorite music and TV shows, and photos.

[0781] Step 2:

[0782] The terminal formats the information entered by the user and sends it to the server. The information is transmitted in an encrypted format using a secure communication protocol.

[0783] Step 3:

[0784] The server stores the received information in a database. The database is used to efficiently manage information for each patient and utilize it for subsequent processing.

[0785] Step 4:

[0786] The server uses information retrieved from the database to activate a generative AI and begin the process of generating memory enhancement problems. The generative AI creates problems tailored to the individual patient's background.

[0787] Step 5:

[0788] The emotion engine built into the device collects data to recognize the user's emotions. This is done by analyzing the user's voice and facial expressions using biosensors, cameras, and other devices.

[0789] Step 6:

[0790] The device sends emotional information collected by the emotion engine to the server. This emotional data is also encrypted before being sent to the server.

[0791] Step 7:

[0792] The server combines generated problems with user sentiment information to make adjustments. Specifically, it adjusts the difficulty level of the problems according to the user's mental state and selects an appropriate set of problems.

[0793] Step 8:

[0794] The device presents the user with tailored problems, allowing them to engage with interesting problems in a relaxed manner.

[0795] Step 9:

[0796] The user enters their answers to questions displayed on the device. Answers can be entered in multiple-choice or free-text format.

[0797] Step 10:

[0798] The terminal sends the user's response to the server. The response data is recorded on the server and used for evaluation.

[0799] Step 11:

[0800] The server evaluates the responses and generates appropriate feedback based on the user's emotional information obtained from the emotion engine. This feedback includes encouraging messages and guidance for the next steps.

[0801] Step 12:

[0802] The terminal displays feedback from the server to the user. Based on this feedback, the user can check their progress and move on to the next problem.

[0803] (Example 2)

[0804] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0805] There is a need to maximize the effectiveness of memory enhancement and improve the user experience by providing memory enhancement tasks based on individual user information and responding flexibly according to the user's emotional state. Conventional technologies have difficulty providing tasks and feedback that adequately consider individual user information and emotional states, resulting in a lack of highly effective memory enhancement methods.

[0806] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0807] In this invention, the server includes means for acquiring information, means for generating tasks for memory enhancement, means for recognizing the user's emotions, means for adjusting the difficulty of the tasks using emotional information, means for receiving and evaluating the user's responses, and means for providing the evaluation results as feedback. This enables effective and personalized memory enhancement based on individual user information and real-time emotional states.

[0808] "Means of acquiring information" refers to functions for collecting data provided by users and inputting it into the system.

[0809] "Means for generating tasks to enhance memory" refers to a function that creates questions and quizzes tailored to the user based on the information provided by the user.

[0810] "Means of recognizing user emotions" refers to technologies that analyze a user's voice, facial expressions, and biometric information to determine their current emotional state.

[0811] "Means of adjusting problem difficulty using emotional information" refers to a function that takes the user's emotional state into consideration and changes the difficulty level of a problem to an appropriate level.

[0812] "Means for receiving and evaluating user responses" refers to a function that allows the system to receive responses submitted by users to assigned tasks and to determine their accuracy and relevance.

[0813] "Means of providing evaluation results as feedback" refers to a function that allows the system to return appropriate information and advice to the user based on the evaluation of the user's responses.

[0814] This system aims to provide memory enhancement tasks tailored to each user's individual information, and to offer feedback that takes emotions into account. By utilizing advanced generative AI models and emotion recognition technology, it provides a learning experience that matches the user's characteristics.

[0815] Specifically, users input personal information required by cognitive functions via their devices. This includes hobbies, past experiences, visual materials, and favorite music and videos. This information serves as foundational data for highly personalized user experiences.

[0816] The terminal organizes the input information and sends it to the server using a secure communication method. The server stores the information in a database and dynamically generates tasks for memory enhancement using a generative AI model. In this generation process, for example, natural language processing technology is used to determine the task content using prompt sentences that are aligned with the user's interests.

[0817] Furthermore, the device's built-in emotion recognition technology analyzes the user's voice and visual information to identify their emotional state in real time. This emotional information is sent to a server and used to generate tasks and feedback.

[0818] The generative AI model has the ability to detect the user's emotional state and adjust the difficulty and content of the task accordingly. For example, if the user is feeling stressed, the server will reduce the complexity of the problem and generate feedback that helps the user relax.

[0819] As a concrete example, a prompt might be an instruction such as, "Generate a memory problem on a topic of interest to the user, with relaxing content." This prompt is processed by a generative AI model, resulting in the creation of personalized problems.

[0820] In this way, the system creates a personalized learning and feedback experience based on the information entered by the user and real-time sentiment data.

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1:

[0823] Users enter personal information using their devices. This information includes their hobbies, background, visual data, and favorite music and videos. This data serves as the basis for personalizing memory enhancement tasks.

[0824] Step 2:

[0825] The terminal transmits information obtained from the user to the server using a secure protocol. This process converts the input information into an appropriate format to ensure smooth data processing on the server side. The output is user information converted into a format that the server can accept.

[0826] Step 3:

[0827] The server stores the received user information in a database. This stored information becomes the data source used by the generating AI model. This data is used to reflect the individual needs of the user in order to generate memory enhancement tasks.

[0828] Step 4:

[0829] The server generates memory enhancement tasks using a generative AI model based on stored information. Specifically, the AI ​​model analyzes prompts based on the user's interests and determines the task content. The input is user information from the database, and the output is a task tailored to the user.

[0830] Step 5:

[0831] The device's built-in emotion engine recognizes the user's emotions. The device acquires the user's voice and facial expression data and analyzes their emotional state. This analysis result is sent to the server as the user's most recent emotional state. The results of the emotion analysis influence the generated tasks and feedback. The output is data indicating the emotional state.

[0832] Step 6:

[0833] The server sends the generated task to the terminal. The terminal displays this task to the user and accepts the user's response. The output is a presentation of the task with an interface for the user to input their answer.

[0834] Step 7:

[0835] The user's response is sent from the terminal to the server, which then evaluates the response. The server further considers the user's sentiment information to generate optimal feedback. The input is the user's response and sentiment information, and the output is the feedback content.

[0836] Step 8:

[0837] The terminal displays feedback provided by the server to the user. By receiving this feedback, the user can understand areas for further learning and improvement. The output is a display of feedback to the user.

[0838] (Application Example 2)

[0839] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0840] In modern work environments, worker stress and fatigue are factors that lead to decreased productivity and safety risks. To prevent this and improve the work environment, a system is needed that can recognize workers' emotional states in real time and respond appropriately to individual situations. However, current technology does not adequately provide real-time responses based on emotional states, and a more effective solution is needed.

[0841] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0842] In this invention, the server includes a device for acquiring information, a device for recognizing emotional states in real time, and a device for adjusting the difficulty level of tasks based on the emotional states. This enables dynamic adjustment of tasks and appropriate feedback in accordance with the emotional states of individual workers in the work environment.

[0843] A "device for acquiring information" is a device for accumulating individual user information and has the function of collecting diverse information, including hobbies, background, and biometric data.

[0844] A "device for generating tasks to enhance memory" is a device that, based on acquired information, presents customized tasks aimed at improving the user's memory, utilizing AI to design optimal tasks.

[0845] A "device for presenting tasks to users" is a device that presents generated tasks to users through means such as sight and hearing, enabling them to effectively tackle the tasks.

[0846] A "device that receives and analyzes user responses" is a device that receives user answers to presented tasks and analyzes their accuracy, speed, and content.

[0847] A "feedback device" is a device that provides appropriate comments and advice tailored to the user based on the analysis results, contributing to the improvement of the user's understanding and memory.

[0848] A "device that recognizes emotional states in real time" is a device that detects the user's facial expressions, voice tone, and biosignals, analyzes that data, and determines the user's emotional state in real time.

[0849] A "task difficulty adjustment device" is a device that appropriately changes the difficulty and content of a task while taking into account the user's emotional state and stress level, so that the user can work on the task without difficulty.

[0850] The system for realizing this invention mainly consists of a server, a terminal, and a user.

[0851] The server uses generative AI models to collect information and generate customized tasks for each user. The server utilizes storage media to accumulate each user's hobbies, background, and past performance data. It also uses an intelligent engine to process user response data for analysis and generate appropriate feedback. This process employs database management systems and specialized generative AI software. Specifically, machine learning libraries are used for data analysis.

[0852] The device is responsible for recognizing the user's emotional state in real time. For emotion recognition, facial recognition software is used for facial expression analysis, and a speech recognition platform is used for voice analysis. The device has built-in sensors and a camera, and it sends the data acquired through these to a server, where the analysis results are provided to the user via the display.

[0853] Users work on tasks presented through their devices and provide answers. The device monitors the user's reactions, performance, and emotional state. For example, if the server detects high stress levels in the user, it uses a generative AI model to generate feedback such as, "We recommend you reduce the workload or take a break."

[0854] A concrete example of a prompt message is, "Based on the worker's facial expression data and biometric information, fatigue has been detected. Suggest an appropriate method for refreshing the worker." This prompt message is input into a generative AI model and used to automatically generate appropriate countermeasures.

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The device collects personal information from the user. This information includes hobbies, background, and past performance data. The collected information is obtained through sensors and input interfaces and transmitted to a server. This information processing prepares the foundational data for generating tasks tailored to the user's characteristics.

[0858] Step 2:

[0859] The device recognizes the user's emotions in real time. It utilizes a voice recognition platform and facial recognition software to acquire facial expressions, voice tone, and biometric information via sensors. The acquired emotional data is processed on the device and sent to the server as the user's current emotional state. This prepares the device for adjusting tasks according to the user's emotional state.

[0860] Step 3:

[0861] The server stores the received information and generates tasks optimized for each user. A generation AI model is used to determine the task content and difficulty level based on the input information. Data analysis is then used to evaluate the user's performance trends and design appropriate tasks. The generated tasks are sent to the terminal.

[0862] Step 4:

[0863] The device presents the user with a generated task. The content of the task is clearly communicated to the user through the display and audio output. The user begins working on the presented task, and the device monitors the user's response at this point.

[0864] Step 5:

[0865] The user works on a task and enters their answer into the device. Data about the user's activity (accuracy of the answer, time taken, etc.) is also acquired at the same time. The device sends this data to the server.

[0866] Step 6:

[0867] The server analyzes the user's responses and evaluates the results. An intelligent engine is used to calculate the accuracy and time taken for the responses, and compare them to expected performance. The evaluation results are then sent back to the terminal.

[0868] Step 7:

[0869] The device generates appropriate feedback based on the evaluation results and provides it to the user. This includes suggestions for refreshing the user based on their emotional state and advice for the next steps. Using prompts generated by an AI model, the system advises the user on the best course of action.

[0870] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0871] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0872] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0873] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0874] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0875] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0876] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0877] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0878] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0879] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0880] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0881] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0882] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0883] 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.

[0884] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0885] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0886] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0887] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0888] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0889] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0890] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] Means of obtaining information,

[0894] A means for generating questions for memory enhancement based on the aforementioned information,

[0895] A means for presenting the aforementioned problem to the user,

[0896] A means for receiving and evaluating the user's response,

[0897] A means of providing the aforementioned evaluation results as feedback,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, having a database for storing information.

[0901] (Claim 3)

[0902] The system according to claim 1, which includes artificial intelligence that performs generation processing when generating the aforementioned problem.

[0903] "Example 1"

[0904] (Claim 1)

[0905] Means for acquiring information and transmitting said information to a central processing unit,

[0906] A means for calling a generation engine that has a storage medium for storing acquired information and generates problems for memory enhancement based on said information,

[0907] A means of presenting the generated problem to the user via an output device,

[0908] A means for receiving the user's response and evaluating the response,

[0909] A means of providing evaluation results to users as feedback,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, wherein the generated problem includes text, images, and music.

[0913] (Claim 3)

[0914] The system according to claim 1, which generates a prompt sentence based on input information and instructs artificial intelligence to generate a problem.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] Means of obtaining information,

[0918] A means for generating a measurement for memory enhancement based on the aforementioned information,

[0919] A means of presenting the aforementioned measurement to an individual,

[0920] A means for receiving and evaluating the aforementioned individual's response,

[0921] A means of providing the aforementioned evaluation results as feedback,

[0922] A means of providing products and experiences tailored to individual interests,

[0923] A means of providing measurements related to the aforementioned products and experiences, and enhancing the experience,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, comprising a storage device for storing information.

[0927] (Claim 3)

[0928] The system according to claim 1, which includes computational intelligence that performs generation processing when generating the aforementioned measurement.

[0929] "Example 2 of combining an emotion engine"

[0930] (Claim 1)

[0931] Means of obtaining information,

[0932] Means for generating tasks for memory enhancement based on the aforementioned information,

[0933] Means of recognizing user emotions,

[0934] A method for adjusting the difficulty of a problem using emotional information,

[0935] A means for presenting the aforementioned problem to the user,

[0936] A means of receiving and evaluating user responses,

[0937] A means of providing the aforementioned evaluation results as feedback,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, having a data structure for storing information.

[0941] (Claim 3)

[0942] The system according to claim 1, comprising an intelligent processing device that performs generation processing when generating the aforementioned problem.

[0943] "Application example 2 when combining with an emotional engine"

[0944] (Claim 1)

[0945] A device for acquiring information,

[0946] A device that generates tasks for memory enhancement based on the aforementioned information,

[0947] A device for presenting the aforementioned problem to the user,

[0948] A device that receives and analyzes the user's response,

[0949] A device that provides the aforementioned analysis results as feedback,

[0950] A device that recognizes emotional states in real time,

[0951] A device that adjusts the difficulty level of the task based on the aforementioned emotional state,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, having a storage medium for storing information.

[0955] (Claim 3)

[0956] The system according to claim 1, which includes an intelligent engine that performs computational processing when generating the aforementioned problem. [Explanation of symbols]

[0957] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining information, A means for generating questions for memory enhancement based on the aforementioned information, A means for presenting the aforementioned problem to the user, A means for receiving and evaluating the user's response, A means of providing the aforementioned evaluation results as feedback, A system that includes this.

2. The system according to claim 1, having a database for storing information.

3. The system according to claim 1, which includes artificial intelligence that performs generation processing when generating the aforementioned problem.

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