system

Abstract

We provide the system. [Solution] A means of analyzing cognitive function based on input from an information processing device, A means of evaluating risk based on the analysis results, A means for generating an individualized brain function training program based on assessed risk, A means of executing the generated program and tracking its progress, Methods for proposing cognitive function promotion activities in daily life, A system that includes means to detect abnormalities in cognitive function and prompt necessary interventions.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, it is important to detect early the decline of individual cognitive functions and the risk of dementia and take appropriate measures. However, current care methods have a problem that it is difficult to continuously implement daily and individually optimized evaluations and interventions, and they cannot sufficiently respond to prevention and early detection.

Means for Solving the Problems

[0005] This invention relates to a system that analyzes cognitive function based on data obtained from an information processing device and provides individually generated brain function training programs according to the results. This system dynamically updates risk assessments and proposes activities to improve and maintain cognitive function in daily life. Furthermore, it includes means for early detection of cognitive abnormalities and encourages necessary interventions, thereby supporting the prevention and early detection of dementia.

[0006] An "information processing device" is a device used for data collection, analysis, and control, and is capable of acquiring and processing information from users.

[0007] "Cognitive function" refers to the ability of humans to understand sensory information, think, and remember, and is involved in acquiring knowledge and solving problems.

[0008] "Analysis" is the process of breaking down data into smaller parts for evaluation and drawing conclusions; it is the process of transforming information into an understandable form.

[0009] "Risk assessment" is the act of analyzing the likelihood of problems or obstacles that may occur in the future, based on specific conditions and data.

[0010] A "brain function training program" is a set of activities and exercises designed to activate and improve cognitive function.

[0011] "Activity suggestions" refer to specific action plans that should be taken to support or improve the user's daily life.

[0012] Anomaly detection refers to the process of identifying data or behavior that deviates from normal patterns, thereby detecting potential problems early on.

[0013] "Intervention" refers to taking measures to actively engage with and resolve a specific situation or problem. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main 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. [[ID=第41]] [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

[0015] 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.

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

[0017] 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.

[0018] 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.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

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

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system according to the present invention aims to evaluate the user's cognitive function in daily life and to improve or maintain that function through appropriate intervention. This system mainly includes an information processing device, a data analysis algorithm, a training program module, and user interaction elements.

[0036] First, the device collects everyday data using various sensors (such as voice recognition and motion sensors) to record the user's actions and conversations. Smartphones or dedicated wearable devices are commonly used for this purpose. The collected data is securely transmitted to a server in a format that allows for real-time analysis.

[0037] The server uses advanced machine learning algorithms to analyze the received data. This analysis assesses key cognitive functions such as the user's language processing ability, memory, and attention. Furthermore, based on this assessment, an individual dementia risk is calculated. For example, if a user's vocabulary narrows or their motor skills decline, these may be treated as early signs of cognitive decline.

[0038] Next, the server automatically generates a brain function training program optimized for the user based on these evaluation results. Examples include reflex games to improve attention and storytelling exercises to enhance memory. These programs are incorporated into the user's schedule and notified via their device.

[0039] Users complete the presented training program and input their results and feedback into the terminal. This information is then sent back to the server and used to further improve the programs provided by the system.

[0040] Furthermore, the system also makes activity suggestions to users to promote social interaction in their daily lives. For example, it may recommend participating in activities held at a nearby community center. In addition, if the terminal detects any abnormalities on a daily basis, such as changes in speech fluency or gestures during conversation, the server will analyze the information and suggest contact to prompt medical intervention.

[0041] Thus, the system of the present invention comprehensively supports the user's cognitive function and provides a consistent service from early problem detection to the implementation of an optimal training program, thereby helping to prevent cognitive decline and maintain a healthy living environment.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects data from the user's daily life. This includes conversation monitoring using voice input devices and motion analysis using motion sensors.

[0045] Step 2:

[0046] The device temporarily stores the collected data and preprocesses it as needed. This preprocessing includes noise reduction and data formatting.

[0047] Step 3:

[0048] The terminal sends pre-processed data to the server. Data transmission is performed via a security protocol to ensure security.

[0049] Step 4:

[0050] The server applies machine learning algorithms to analyze the received data. Cognitive function-related metrics are calculated, and the user's current cognitive state is evaluated.

[0051] Step 5:

[0052] The server predicts the risk of dementia based on the analysis results. Risk assessment is performed based on past medical data and existing statistical models.

[0053] Step 6:

[0054] The server takes into account the dementia risk and cognitive function assessment results to generate a brain function training program optimized for the user.

[0055] Step 7:

[0056] The server sends the generated training program to the terminal. The program includes game-based exercises and memory-improving tasks.

[0057] Step 8:

[0058] The device notifies the user of the training program and encourages them to complete it. It may also set reminders if the user is busy.

[0059] Step 9:

[0060] Users follow notifications from their devices to complete the training program. Progress and results are fed back to the device.

[0061] Step 10:

[0062] The device uploads user feedback data to the server. Based on this feedback, the training plan for the entire system is optimized.

[0063] Step 11:

[0064] The device continues to collect data through daily observations and detects anomalies. When an anomaly is detected, an alert is sent to the server.

[0065] Step 12:

[0066] The server analyzes the anomaly detection data and, if necessary, notifies the user to consider medical intervention.

[0067] Step 13:

[0068] The server periodically analyzes data and provides users with activity suggestions via their devices to promote social interaction. This helps to improve users' quality of life.

[0069] (Example 1)

[0070] 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."

[0071] Many people are at risk of cognitive decline due to aging and lifestyle factors, but there is a challenge in providing individually optimized interventions. Furthermore, many current systems are insufficient in early detection of changes in cognitive function and in providing effective countermeasures.

[0072] 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.

[0073] In this invention, the server includes means for analyzing cognitive function based on data obtained from information processing means, means for dynamically evaluating risk based on the analysis results, and means for automatically generating an individualized training program corresponding to the evaluated risk. This enables improvement of cognitive function and early detection of anomalies that are tailored to individual needs.

[0074] "Information processing means" refers to all technological devices that support the process of collecting, transmitting, and analyzing data.

[0075] "Cognitive function" refers to all abilities related to human intellectual activity, such as memory, attention, and language processing.

[0076] "Analysis" refers to the process of extracting information from collected data using specific algorithms and methods, and deriving specific conclusions.

[0077] "Risk" refers to an indicator of the likelihood of developing cognitive decline or cognitive-related disorders.

[0078] A "training program" refers to a set of activities or exercises aimed at improving or maintaining specific cognitive functions.

[0079] A "user terminal" refers to a device used by a user to interact with the system.

[0080] "Feedback" refers to information or reactions from users or systems regarding the activities that have been carried out.

[0081] "Abnormal" refers to changes or behaviors in cognitive function that deviate from the normal range.

[0082] "Promoting activity" refers to suggestions and advice that encourage users to actively participate in their daily lives.

[0083] "Intervention" refers to specific measures or actions taken in response to cognitive abnormalities or declines.

[0084] This invention provides a system that comprehensively evaluates a user's cognitive functions in their daily life and proposes the optimal intervention. Specific embodiments of this system are described below.

[0085] The device uses various sensors, including voice recognition and motion sensors, to record the user's actions and conversations. Typically, smartphones or dedicated wearable devices are used. The data recorded by the device is transmitted to a server via a secure protocol. The data is encrypted during this transmission, ensuring its safety.

[0086] The server uses machine learning algorithms to analyze the incoming data. Specifically, it uses open-source libraries and custom models to evaluate important cognitive functions such as the user's language processing ability, memory, and attention. Based on the analysis results, an individual's dementia risk is calculated.

[0087] The server automatically generates a brain function training program optimized based on the user's assessment. This includes reflex games to enhance attention and storytelling exercises to improve memory. These training programs are implemented using Python and other languages ​​and are tailored to the user's cognitive characteristics and daily routine. The generated program is notified to the user via the terminal.

[0088] Users complete the presented training program and input the results and feedback into their device. This information is then sent back to the server and incorporated into the analysis, thereby improving the accuracy of the training program.

[0089] Furthermore, the system makes suggestions to promote social interaction within the user's daily activities. For example, it encourages participation in local community events to improve cognitive function. In addition, if an anomaly is detected, analysis is performed on the server to propose rapid intervention.

[0090] As a concrete example, an example of a prompt sentence to be input into a generative AI model is: "Describe a system for evaluating a user's cognitive function in their daily life and proposing a training program." With this prompt, the generative AI model can successively provide optimized programs tailored to the user.

[0091] As described above, the system according to this invention provides comprehensive support to enrich the user's daily life and prevent cognitive decline.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The device uses voice recognition and motion sensors to record user actions and conversations in real time. Input consists of user voice and motion data, which is digitized and stored. Output is a digital dataset representing user activity. This data contains information necessary for subsequent analysis.

[0095] Step 2:

[0096] The terminal encrypts the recorded digital data and sends it to the server using a secure protocol. The input is the digital dataset before encryption, which is securely transferred via the communication protocol. The output is the encrypted data stream received by the server.

[0097] Step 3:

[0098] The server decrypts the received encrypted data and performs preprocessing for analysis. The input is an encrypted data stream sent from the terminal, and the decrypted data is converted into an analyzable format. The output is the data ready for analysis.

[0099] Step 4:

[0100] The server applies machine learning algorithms to the decrypted data to evaluate the user's cognitive function. The input is the decrypted data, from which indicators such as language processing ability and memory are extracted. The output is an evaluation report that quantifies the user's cognitive function.

[0101] Step 5:

[0102] The server generates a personalized training program for each user based on the assessment report. The input is the user's cognitive function assessment report, which is used to select the optimal exercises. The output is the schedule and content of the generated training program.

[0103] Step 6:

[0104] The terminal notifies the user of the training program received from the server. The input is the program data sent from the server and displayed on the user's device. The output is a notification message and action reminder to the user.

[0105] Step 7:

[0106] Users perform exercises through the notified training program and input feedback into the device. The input includes the degree of completion and impressions of the completed program, which is digitized and stored on the device. The output is the feedback data stored on the device.

[0107] Step 8:

[0108] The server analyzes feedback received from the terminals to improve the training program. The input is user feedback data, which is used to improve the program in the next iteration. The output is the updated training program and improvement metrics.

[0109] (Application Example 1)

[0110] 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."

[0111] There is a challenge in detecting cognitive decline in users, including the elderly, at an early stage and providing personalized training to improve cognitive function and maintain a healthy lifestyle. Furthermore, promoting social interaction and preventing isolation is important, but it is not easy to propose this individually and efficiently.

[0112] 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.

[0113] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for evaluating risk based on the analysis results, means for generating an individualized brain function training program according to the evaluated risk, means for performing cognitive evaluation using a process including motion recognition, and means for utilizing computing resources to perform the analysis in a cloud environment. This enables appropriate evaluation of the user's cognitive function in daily life and individualized intervention.

[0114] An "information processing device" is a device that records user activities and conversations and processes them as data.

[0115] "Cognitive function" refers to functions related to brain activity, including the user's language processing ability, memory, and attention.

[0116] "Analysis" is the process of evaluating cognitive function using machine learning algorithms and other methods based on collected data.

[0117] "Risk assessment" is the process of estimating the likelihood of cognitive decline or progression to dementia based on the results of an analysis.

[0118] A "brain function training program" is a set of programs that include specific exercises and activities aimed at improving or maintaining cognitive function.

[0119] "Motion recognition" is the process of detecting a user's movements and actions through sensors and using that information as data.

[0120] A "cloud environment" is a collection of data storage and computing resources provided via the internet, enabling real-time data processing.

[0121] The system for implementing the present invention implements a program that combines an information processing device, a cloud environment, and machine learning algorithms to evaluate the user's cognitive function in real time and enable personalized interventions.

[0122] The devices will be offered in the form of smart glasses or smartphones and will be equipped with sensors for voice recognition and motion recognition. This will allow for the collection of data on the user's daily activities. Specific examples include analysis of the vocabulary the user uses in everyday conversation and detection of anomalies from daily behavioral patterns.

[0123] The collected data is securely transmitted to the server via Wi-Fi or mobile networks. The server is built in a cloud environment and analyzes the data using advanced machine learning algorithms. Technologies used include analyzing speech data with the Google® Speech-to-Text API and building machine learning models using TENSORFLOW®.

[0124] Based on the analysis, the server assesses the user's cognitive function risks and generates a personalized brain function training program as needed. This program is communicated to the user via a terminal and implemented. For example, if the user begins using a particular word frequently, a storytelling exercise to improve memory is suggested based on that.

[0125] Furthermore, this system can detect anomalies in real time and provide necessary interventions quickly. It also suggests activities that promote social interaction, thus contributing to the prevention of isolation. Examples of prompts include, "Extract information from changes in the vocabulary a 70-year-old man uses in his daily life," and "Analyze the trend of daily decline in physical fitness and suggest an optimal exercise program."

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The device continuously collects user voice and motion data using built-in sensors. In this step, speech recognition technology transcribes conversations into text, and motion sensors record movement patterns. The input is the user's raw data, and the output is a data file encoded in digital format.

[0129] Step 2:

[0130] The device transmits the collected data to the server via Wi-Fi or a mobile network. The input here is the data file generated in step 1, and the output is data encrypted using a secure communication protocol.

[0131] Step 3:

[0132] The server analyzes the received data in a cloud environment using machine learning algorithms. Specifically, it performs frequency analysis of language use from speech data and evaluation of activity patterns from behavioral data. The input is the encrypted data sent in step 2, and the output is the result of the cognitive function evaluation.

[0133] Step 4:

[0134] The server generates a personalized brain function training program based on the analysis results. For example, if the frequency of use of a particular linguistic expression changes, it will suggest specific exercises to improve memory. The input is the evaluation results from step 3, and the output is the user-specific training program.

[0135] Step 5:

[0136] The server utilizes idle time to notify the terminal of the created training program. The terminal displays the notification to the user and supports the user in starting the program. The input is the training program from step 4, and the output is the program notification to the user.

[0137] Step 6:

[0138] The user performs activities according to the instructions of the training program displayed on the device. Completion data for each activity is entered into the device and sent back to the server. The input is the user's completion data, and the output is the feedback data sent to the server.

[0139] Step 7:

[0140] The server analyzes the feedback data and considers suggestions for improving the training program. It utilizes generative AI models as needed to develop new programming strategies. The input is the feedback data from step 6, and the output is the improved future training program.

[0141] 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.

[0142] This invention relates to a cognitive function analysis system incorporating an emotion engine, which provides comprehensive cognitive function assessment and support based on the user's daily life data. The system includes an information processing device, an emotion recognition module, a training program generation module, and user feedback and anomaly detection modules.

[0143] First, the device acquires user emotional and behavioral data. This includes an emotion engine that uses cameras and voice input sensors to recognize emotions from facial expressions and tone of voice. For example, if a user displays a smile or an angry expression, the emotion engine recognizes the change in real time. This data, along with other lifestyle data necessary for analyzing cognitive function, is sent to the server.

[0144] The server integrates and analyzes received lifestyle and emotional data. Using machine learning algorithms, it comprehensively evaluates the user's cognitive and emotional state and estimates their daily risk of dementia. For example, if a decrease in attention is detected when the user is under excessive stress, this is considered a risk factor.

[0145] Next, the server dynamically adjusts a brain function training program optimized for the user based on the recognized emotional state. This training program is designed to positively change the user's emotions and includes, for example, games that promote relaxation and art sessions that are expected to relieve stress.

[0146] The terminal monitors the program's progress and provides the user with continuous notifications and feedback. The user enters feedback on the training they've completed into the terminal, and this data is sent to the server for re-evaluation. This feedback loop allows the system to quickly respond to user changes and continuously optimize the program.

[0147] Furthermore, the device constantly monitors emotional changes through its emotion engine and reports to the server if a significant negative change is detected. Based on this information, the server can suggest to the user that they consider early medical intervention.

[0148] Thus, the present invention contributes to dementia prevention and improvement of quality of life by comprehensively monitoring the user's cognitive function from an emotional perspective and providing personalized support.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The device uses a camera and voice input device to collect emotional data in real time from the user's facial expressions and tone of voice.

[0152] Step 2:

[0153] The device preprocesses the collected emotion data, performing noise reduction and data formatting. This preprocessing converts the data into a format suitable for analysis.

[0154] Step 3:

[0155] The device sends pre-processed emotional data along with other lifestyle data to the server. A secure protocol is used for data transmission.

[0156] Step 4:

[0157] The server analyzes the received data and assesses the user's cognitive and emotional state. Machine learning models are used to analyze the impact of stress and emotional fluctuations on cognitive function.

[0158] Step 5:

[0159] The server assesses the risk of dementia based on the analysis results. If a user frequently expresses negative emotions, they are judged to be at high risk.

[0160] Step 6:

[0161] The server generates brain function training programs adapted to the user's emotional state. Relaxation games and art sessions are selected to evoke positive emotions.

[0162] Step 7:

[0163] The server sends the generated training program to the terminal and notifies the user.

[0164] Step 8:

[0165] The device prompts the user to complete the training program and continuously monitors their progress.

[0166] Step 9:

[0167] Users follow notifications to complete the training program and input their progress and feedback into their device.

[0168] Step 10:

[0169] The device sends user feedback to the server, verifies whether the training program is appropriate for the user's condition, and makes adjustments as needed.

[0170] Step 11:

[0171] The device continuously monitors emotional changes through an emotion engine and sends an alert to the server if a significant negative change is detected.

[0172] Step 12:

[0173] The server analyzes the reported negative emotional changes and, if necessary, suggests medical intervention to the user to encourage early action.

[0174] (Example 2)

[0175] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0176] In modern society, cognitive decline and the risk of dementia are significant health issues. Especially with the aging population, there is a need for effective methods to monitor individual cognitive function and provide appropriate training and interventions. However, existing methods struggle to provide highly accurate support because they cannot comprehensively assess emotions and behaviors in real time.

[0177] 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.

[0178] In this invention, the server includes means for collecting emotional and behavioral data from information processing devices, means for analyzing cognitive functions based on the collected data, and means for evaluating risk based on the analysis results. This makes it possible to capture changes in the user's emotions and behavior in real time and provide personalized support accordingly.

[0179] An "information processing device" is a device that collects user emotional and behavioral data and transmits that data to a server via communication.

[0180] "Cognitive function" refers to the mental abilities that humans use to understand information, remember it, solve problems, and make judgments.

[0181] "Analysis" is the process of evaluating the user's cognitive and emotional state based on collected data, and extracting meaning using a specific algorithm.

[0182] "Risk" refers to an indicator that shows the possibility of cognitive decline or dementia in the user.

[0183] A "training program" is a set of activities and exercises designed to improve a user's cognitive function and regulate their emotional state.

[0184] "Progress monitoring" means tracking how the training program is being implemented by users and whether it is having any effect.

[0185] "Anomaly detection" refers to identifying deviations from a user's normal cognitive function and emotional state, and issuing alerts as needed.

[0186] "Intervention" refers to specific measures or activities taken to improve a user's health or cognitive state.

[0187] This invention comprises an information processing device, an emotion engine, a training program generation module, and feedback and anomaly detection modules. Specifically, the terminal uses a camera and voice input sensors to acquire user emotion and behavior data. This data includes an emotion engine that captures the user's facial expressions and voice tone in real time and identifies their emotional state.

[0188] Data collected by the device is transmitted to the server via a secure protocol. The server uses the received data to evaluate the user's cognitive function using a generative AI model. Machine learning algorithms integrate lifestyle data and emotional data to estimate the user's risk of dementia. Based on the analysis results, the server generates an optimal training program to improve cognitive function. This program may include activities that promote the conversion to positive emotions, such as relaxation-oriented music sessions and meditation exercises.

[0189] For example, if a user exhibits a higher-than-usual stress level, the server can generate a training program that includes an art session to reduce stress and notify the user via their device. Furthermore, based on user feedback, the server re-evaluates the effectiveness of the training and dynamically adjusts the program content.

[0190] An example of a prompt is, "Using the user's emotional data and daily life data, propose an optimal training program to assess and support cognitive function."

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The device collects user emotion and behavior data. This process uses a camera and voice input sensor to capture the user's facial expressions and voice tone in real time. An emotion engine then analyzes this data to identify emotional states such as smiles or anger. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0194] Step 2:

[0195] The device packages the collected emotional and lifestyle data and sends it to the server using a secure protocol. The input is processed emotional data and other sensor data, and the output is a secure data transfer to the server. Encryption techniques are used in this step to maintain data integrity and confidentiality.

[0196] Step 3:

[0197] The server integrates received lifestyle and emotional data and uses a generative AI model to evaluate the user's cognitive function. Here, machine learning algorithms explore data correlations and estimate dementia risk, among other things. The input is the submitted dataset, and the output is the risk assessment results and analysis report.

[0198] Step 4:

[0199] The server generates a cognitive training program optimized for the user based on the analysis results. This training program includes activities aimed at stress reduction and relaxation. The input is the risk assessment results, and the output is the individual training program.

[0200] Step 5:

[0201] The terminal displays the generated training program to the user and monitors the program's progress. It records the user's progress as they perform the program and provides notifications as needed. The input is the generated program, and the output is user feedback and progress data.

[0202] Step 6:

[0203] After completing the training program, the user enters feedback based on their experience into a terminal. The terminal then sends this feedback back to the server, which is used for future evaluations and adjustments to the training program. The input is the user's feedback on the completed training, and the output is the transmission of the feedback data to the server.

[0204] Step 7:

[0205] The device continuously monitors the user's emotions using an emotion engine. If a significant negative change occurs, the device sends a notification to the server, which then suggests interventions as needed. The input is emotion monitoring data, and the output is an anomaly notification to the server.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] There is a challenge in detecting cognitive decline and emotional fluctuations in the elderly at an early stage and prompting appropriate intervention. In particular, there is a need to dynamically assess users' emotions and cognitive function in their daily living environments and provide individually adapted support.

[0209] 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.

[0210] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for analyzing emotional state and providing optimized relaxation or relaxation programs, and means for detecting sudden changes in emotion and notifying information. This makes it possible to comprehensively monitor the emotional and cognitive state of elderly people in their daily lives and provide personalized support quickly, thereby preventing cognitive decline and improving their quality of life.

[0211] An "information processing device" refers to hardware and software used to receive and analyze data.

[0212] "Means for analyzing cognitive function" refers to algorithms that evaluate the state of cognitive function based on user behavior and emotional data.

[0213] "Means of risk assessment" refers to the process of determining cognitive decline or stress levels based on analysis results.

[0214] "Means for generating personalized brain function training programs" refers to a system that designs optimized training content according to the user's cognitive function state.

[0215] "Means of tracking progress" refers to a system that records the history of training and activities performed by users and monitors their progress.

[0216] "Means of suggesting cognitive function promotion activities" refers to functions that recommend activities in the user's daily life to maintain and improve cognitive function.

[0217] "Means for analyzing emotional states" refers to a function that recognizes and analyzes emotions from the user's facial expressions and tone of voice.

[0218] "Means of providing optimized relaxation or relaxation programs" refers to a function that suggests appropriate activities to reduce stress based on the user's emotional state.

[0219] "Means of detecting sudden emotional changes and notifying information" refers to a function that recognizes significant emotional fluctuations in real time and transmits that information to relevant parties.

[0220] The system for realizing this invention primarily comprises an information processing terminal, a server, and user interface functions. First, the terminal acquires emotional and behavioral data from the user in real time. This data acquisition uses sensor devices such as a camera and microphone, and is equipped with an emotion engine that includes facial recognition and voice analysis.

[0221] Data acquired by the device is sent to the server. The server uses this data to apply machine learning algorithms to analyze the user's cognitive and emotional state. TensorFlow or similar technology platforms are used for this analysis. Based on the analysis results, a personalized brain function training program is generated for the user. This program aims to reduce stress in daily life and improve cognitive function, and includes gaming activities and relaxation exercises.

[0222] For example, if the system detects that a user tends to experience high levels of stress on weekends, it can suggest relaxing activities such as listening to calming music or light exercise. Furthermore, if a sudden change in mood is detected, relevant notifications are sent to family members or caregivers to ensure prompt support.

[0223] When a user considers activities to relieve stress, a prompt such as "Suggest activities that the user can do to relieve stress" can be used with a generative AI model.

[0224] Through this system, it is expected that users' daily lives will be comprehensively supported, and their cognitive functions will be maintained and improved.

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The device uses a camera and microphone to acquire user emotion and behavior data. Camera images and audio data are used as input. The emotion engine analyzes facial expressions and tone using facial recognition algorithms and voice analysis technology, and outputs the user's emotional state. Specifically, if the user is smiling, the system recognizes this as "positive."

[0228] Step 2:

[0229] The acquired emotional and activity data is sent to the server in real time. The server receives this data as input and uses machine learning algorithms to analyze it, evaluate the user's cognitive function, and output daily risk information. Specifically, if decreased attention is observed while under high stress, this is evaluated as "at risk."

[0230] Step 3:

[0231] The server generates a brain function training program tailored to the user based on the analyzed results. This program receives the analysis results as input and suggests specific games or relaxation activities as output. The program is then sent to the user's device, and the program begins. For example, if relaxation is needed, a session listening to calming music might be suggested.

[0232] Step 4:

[0233] The user performs the suggested training or relaxation program on the device, and the device tracks their progress in real time. It receives user performance data as input and sends progress data to the server as feedback. Specifically, if the achievement rate exceeds a certain level, a "going well" message is displayed on the device.

[0234] Step 5:

[0235] The server evaluates the effectiveness of the training program based on user feedback data and readjusts the program as needed. In this case, the feedback data becomes the input, and the adjusted program is output. Specifically, if an activity is found to be ineffective, a new activity is suggested as an alternative.

[0236] Step 6:

[0237] The device constantly monitors the user's emotional changes via an emotion engine and reports to the server if a sudden negative change is detected. Monitoring data is used as input, and a notification message is output. Specifically, if negative emotions persist, an alert is sent to the caregiver.

[0238] Step 7:

[0239] When a user spontaneously uses a generative AI model to come up with activities, they can prompt the model with a message like, "Suggest activities that the user can do to relieve stress," and the AI ​​will output suggestions. Specific examples of suggestions from the AI ​​include "a short walk" and "recommendation for deep breathing."

[0240] 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.

[0241] 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.

[0242] 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.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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".

[0256] The system according to the present invention aims to evaluate the user's cognitive function in daily life and to improve or maintain that function through appropriate intervention. This system mainly includes an information processing device, a data analysis algorithm, a training program module, and user interaction elements.

[0257] First, the device collects everyday data using various sensors (such as voice recognition and motion sensors) to record the user's actions and conversations. Smartphones or dedicated wearable devices are commonly used for this purpose. The collected data is securely transmitted to a server in a format that allows for real-time analysis.

[0258] The server uses advanced machine learning algorithms to analyze the received data. This analysis assesses key cognitive functions such as the user's language processing ability, memory, and attention. Furthermore, based on this assessment, an individual dementia risk is calculated. For example, if a user's vocabulary narrows or their motor skills decline, these may be treated as early signs of cognitive decline.

[0259] Next, the server automatically generates a brain function training program optimized for the user based on these evaluation results. Examples include reflex games to improve attention and storytelling exercises to enhance memory. These programs are incorporated into the user's schedule and notified via their device.

[0260] Users complete the presented training program and input their results and feedback into the terminal. This information is then sent back to the server and used to further improve the programs provided by the system.

[0261] Furthermore, the system also makes activity suggestions to users to promote social interaction in their daily lives. For example, it may recommend participating in activities held at a nearby community center. In addition, if the terminal detects any abnormalities on a daily basis, such as changes in speech fluency or gestures during conversation, the server will analyze the information and suggest contact to prompt medical intervention.

[0262] Thus, the system of the present invention comprehensively supports the user's cognitive function and provides a consistent service from early problem detection to the implementation of an optimal training program, thereby helping to prevent cognitive decline and maintain a healthy living environment.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The device collects data from the user's daily life. This includes conversation monitoring using voice input devices and motion analysis using motion sensors.

[0266] Step 2:

[0267] The device temporarily stores the collected data and preprocesses it as needed. This preprocessing includes noise reduction and data formatting.

[0268] Step 3:

[0269] The terminal sends pre-processed data to the server. Data transmission is performed via a security protocol to ensure security.

[0270] Step 4:

[0271] The server applies machine learning algorithms to analyze the received data. Cognitive function-related metrics are calculated, and the user's current cognitive state is evaluated.

[0272] Step 5:

[0273] The server predicts the risk of dementia based on the analysis results. Risk assessment is performed based on past medical data and existing statistical models.

[0274] Step 6:

[0275] The server generates a brain function training program optimized for the user, taking into account the assessment results of dementia risk and cognitive function.

[0276] Step 7:

[0277] The server sends the generated training program to the terminal. The program content includes game - style exercises and memory - improvement tasks.

[0278] Step 8:

[0279] The terminal notifies the user of the training program and prompts the user to implement it. A reminder may be set when the user is busy.

[0280] Step 9:

[0281] The user implements the training program according to the notification from the terminal and provides feedback on the progress and implementation results to the terminal.

[0282] Step 10:

[0283] The terminal uploads the feedback data from the user to the server. Based on this feedback, the training plan for the entire system is optimized.

[0284] Step 11:

[0285] The terminal continues to collect data through daily observations and detects abnormalities. When an abnormality is detected, an alert is sent to the server.

[0286] Step 12: [[ID=4,6]]

[0287] [[ID=,48]]The server analyzes the abnormality - detection data and, if necessary, notifies the user to consider medical intervention.

[0288] Step 13:

[0289] The server periodically analyzes data and provides users with activity suggestions via their devices to promote social interaction. This helps to improve users' quality of life.

[0290] (Example 1)

[0291] 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."

[0292] Many people are at risk of cognitive decline due to aging and lifestyle factors, but there is a challenge in providing individually optimized interventions. Furthermore, many current systems are insufficient in early detection of changes in cognitive function and in providing effective countermeasures.

[0293] 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.

[0294] In this invention, the server includes means for analyzing cognitive function based on data obtained from information processing means, means for dynamically evaluating risk based on the analysis results, and means for automatically generating an individualized training program corresponding to the evaluated risk. This enables improvement of cognitive function and early detection of anomalies that are tailored to individual needs.

[0295] "Information processing means" refers to all technological devices that support the process of collecting, transmitting, and analyzing data.

[0296] "Cognitive function" refers to all abilities related to human intellectual activity, such as memory, attention, and language processing.

[0297] "Analysis" refers to the process of extracting information from collected data using specific algorithms and methods, and deriving specific conclusions.

[0298] "Risk" refers to an indicator of the likelihood of developing cognitive decline or cognitive-related disorders.

[0299] The "training program" refers to a series of activities or exercises aimed at improving or maintaining specific cognitive functions.

[0300] The "user operation terminal" refers to a device used by the user to interact with the system.

[0301] "Feedback" refers to information or responses from the user or the system regarding the activities performed.

[0302] "Abnormality" refers to changes or behaviors of cognitive functions that deviate from the normal range.

[0303] "Promotion of activities" refers to suggestions or advice that encourage the user's active participation in daily life.

[0304] "Intervention" refers to specific measures or actions taken against abnormalities or declines in cognitive functions.

[0305] This invention provides a system that comprehensively evaluates the cognitive functions in the user's daily life and proposes optimal interventions. Hereinafter, specific embodiments of this system will be described.

[0306] The terminal uses various sensors including a voice recognition function and a motion sensor to record the user's actions and conversations. Generally, a smartphone or a dedicated wearable device is used. The data recorded by the terminal is transmitted to the server via a secure protocol. At this time, since the data is encrypted, it can be transferred safely.

[0307] The server analyzes the arriving data using a machine learning algorithm with the data as the axis. Specifically, open source libraries and custom models are used to evaluate important cognitive functions such as the user's language processing ability, memory, and attention. Based on the analysis results, an individual dementia risk is calculated.

[0308] The server automatically generates a brain function training program optimized based on the user's assessment. This includes reflex games to enhance attention and storytelling exercises to improve memory. These training programs are implemented using Python and other languages ​​and are tailored to the user's cognitive characteristics and daily routine. The generated program is notified to the user via the terminal.

[0309] Users complete the presented training program and input the results and feedback into their device. This information is then sent back to the server and incorporated into the analysis, thereby improving the accuracy of the training program.

[0310] Furthermore, the system makes suggestions to promote social interaction within the user's daily activities. For example, it encourages participation in local community events to improve cognitive function. In addition, if an anomaly is detected, analysis is performed on the server to propose rapid intervention.

[0311] As a concrete example, an example of a prompt sentence to be input into a generative AI model is: "Describe a system for evaluating a user's cognitive function in their daily life and proposing a training program." With this prompt, the generative AI model can successively provide optimized programs tailored to the user.

[0312] As described above, the system according to this invention provides comprehensive support to enrich the user's daily life and prevent cognitive decline.

[0313] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0314] Step 1:

[0315] The device uses voice recognition and motion sensors to record user actions and conversations in real time. Input consists of user voice and motion data, which is digitized and stored. Output is a digital dataset representing user activity. This data contains information necessary for subsequent analysis.

[0316] Step 2:

[0317] The terminal encrypts the recorded digital data and sends it to the server using a secure protocol. The input is the digital dataset before encryption, which is securely transferred via the communication protocol. The output is the encrypted data stream received by the server.

[0318] Step 3:

[0319] The server decrypts the received encrypted data and performs preprocessing for analysis. The input is an encrypted data stream sent from the terminal, and the decrypted data is converted into an analyzable format. The output is the data ready for analysis.

[0320] Step 4:

[0321] The server applies machine learning algorithms to the decrypted data to evaluate the user's cognitive function. The input is the decrypted data, from which indicators such as language processing ability and memory are extracted. The output is an evaluation report that quantifies the user's cognitive function.

[0322] Step 5:

[0323] The server generates a personalized training program for each user based on the assessment report. The input is the user's cognitive function assessment report, which is used to select the optimal exercises. The output is the schedule and content of the generated training program.

[0324] Step 6:

[0325] The terminal notifies the user of the training program received from the server. The input is the program data sent from the server and displayed on the user's device. The output is a notification message and action reminder to the user.

[0326] Step 7:

[0327] Users perform exercises through the notified training program and input feedback into the device. The input includes the degree of completion and impressions of the completed program, which is digitized and stored on the device. The output is the feedback data stored on the device.

[0328] Step 8:

[0329] The server analyzes feedback received from the terminals to improve the training program. The input is user feedback data, which is used to improve the program in the next iteration. The output is the updated training program and improvement metrics.

[0330] (Application Example 1)

[0331] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0332] There is a challenge in detecting cognitive decline in users, including the elderly, at an early stage and providing personalized training to improve cognitive function and maintain a healthy lifestyle. Furthermore, promoting social interaction and preventing isolation is important, but it is not easy to propose this individually and efficiently.

[0333] 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.

[0334] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for evaluating risk based on the analysis results, means for generating an individualized brain function training program according to the evaluated risk, means for performing cognitive evaluation using a process including motion recognition, and means for utilizing computing resources to perform the analysis in a cloud environment. This enables appropriate evaluation of the user's cognitive function in daily life and individualized intervention.

[0335] An "information processing device" is a device that records user activities and conversations and processes them as data.

[0336] "Cognitive function" refers to functions related to brain activity, including the user's language processing ability, memory, and attention.

[0337] "Analysis" is the process of evaluating cognitive function using machine learning algorithms and other methods based on collected data.

[0338] "Risk assessment" is the process of estimating the likelihood of cognitive decline or progression to dementia based on the results of an analysis.

[0339] A "brain function training program" is a set of programs that include specific exercises and activities aimed at improving or maintaining cognitive function.

[0340] "Motion recognition" is the process of detecting a user's movements and actions through sensors and using that information as data.

[0341] A "cloud environment" is a collection of data storage and computing resources provided via the internet, enabling real-time data processing.

[0342] The system for implementing the present invention implements a program that combines an information processing device, a cloud environment, and machine learning algorithms to evaluate the user's cognitive function in real time and enable personalized interventions.

[0343] The devices will be offered in the form of smart glasses or smartphones and will be equipped with sensors for voice recognition and motion recognition. This will allow for the collection of data on the user's daily activities. Specific examples include analysis of the vocabulary the user uses in everyday conversation and detection of anomalies from daily behavioral patterns.

[0344] The collected data is securely transmitted to the server via Wi-Fi or mobile network. The server is built in a cloud environment and analyzes the data using advanced machine learning algorithms. Technologies used include analyzing speech data with the Google Speech-to-Text API and building machine learning models using TensorFlow.

[0345] Based on the analysis, the server assesses the user's cognitive function risks and generates a personalized brain function training program as needed. This program is communicated to the user via a terminal and implemented. For example, if the user begins using a particular word frequently, a storytelling exercise to improve memory is suggested based on that.

[0346] Furthermore, this system can detect anomalies in real time and provide necessary interventions quickly. It also suggests activities that promote social interaction, thus contributing to the prevention of isolation. Examples of prompts include, "Extract information from changes in the vocabulary a 70-year-old man uses in his daily life," and "Analyze the trend of daily decline in physical fitness and suggest an optimal exercise program."

[0347] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0348] Step 1:

[0349] The device continuously collects user voice and motion data using built-in sensors. In this step, speech recognition technology transcribes conversations into text, and motion sensors record movement patterns. The input is the user's raw data, and the output is a data file encoded in digital format.

[0350] Step 2:

[0351] The device transmits the collected data to the server via Wi-Fi or a mobile network. The input here is the data file generated in step 1, and the output is data encrypted using a secure communication protocol.

[0352] Step 3:

[0353] The server analyzes the received data in a cloud environment using machine learning algorithms. Specifically, it performs frequency analysis of language use from speech data and evaluation of activity patterns from behavioral data. The input is the encrypted data sent in step 2, and the output is the result of the cognitive function evaluation.

[0354] Step 4:

[0355] The server generates a personalized brain function training program based on the analysis results. For example, if the frequency of use of a particular linguistic expression changes, it will suggest specific exercises to improve memory. The input is the evaluation results from step 3, and the output is the user-specific training program.

[0356] Step 5:

[0357] The server utilizes idle time to notify the terminal of the created training program. The terminal displays the notification to the user and supports the user in starting the program. The input is the training program from step 4, and the output is the program notification to the user.

[0358] Step 6:

[0359] The user performs activities according to the instructions of the training program displayed on the device. Completion data for each activity is entered into the device and sent back to the server. The input is the user's completion data, and the output is the feedback data sent to the server.

[0360] Step 7:

[0361] The server analyzes the feedback data and considers suggestions for improving the training program. It utilizes generative AI models as needed to develop new programming strategies. The input is the feedback data from step 6, and the output is the improved future training program.

[0362] 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.

[0363] This invention relates to a cognitive function analysis system incorporating an emotion engine, which provides comprehensive cognitive function assessment and support based on the user's daily life data. The system includes an information processing device, an emotion recognition module, a training program generation module, and user feedback and anomaly detection modules.

[0364] First, the device acquires user emotional and behavioral data. This includes an emotion engine that uses cameras and voice input sensors to recognize emotions from facial expressions and tone of voice. For example, if a user displays a smile or an angry expression, the emotion engine recognizes the change in real time. This data, along with other lifestyle data necessary for analyzing cognitive function, is sent to the server.

[0365] The server integrates and analyzes received lifestyle and emotional data. Using machine learning algorithms, it comprehensively evaluates the user's cognitive and emotional state and estimates their daily risk of dementia. For example, if a decrease in attention is detected when the user is under excessive stress, this is considered a risk factor.

[0366] Next, the server dynamically adjusts a brain function training program optimized for the user based on the recognized emotional state. This training program is designed to positively change the user's emotions and includes, for example, games that promote relaxation and art sessions that are expected to relieve stress.

[0367] The terminal monitors the program's progress and provides the user with continuous notifications and feedback. The user enters feedback on the training they've completed into the terminal, and this data is sent to the server for re-evaluation. This feedback loop allows the system to quickly respond to user changes and continuously optimize the program.

[0368] Furthermore, the device constantly monitors emotional changes through its emotion engine and reports to the server if a significant negative change is detected. Based on this information, the server can suggest to the user that they consider early medical intervention.

[0369] Thus, the present invention contributes to dementia prevention and improvement of quality of life by comprehensively monitoring the user's cognitive function from an emotional perspective and providing personalized support.

[0370] The following describes the processing flow.

[0371] Step 1:

[0372] The device uses a camera and voice input device to collect emotional data in real time from the user's facial expressions and tone of voice.

[0373] Step 2:

[0374] The device preprocesses the collected emotion data, performing noise reduction and data formatting. This preprocessing converts the data into a format suitable for analysis.

[0375] Step 3:

[0376] The device sends pre-processed emotional data along with other lifestyle data to the server. A secure protocol is used for data transmission.

[0377] Step 4:

[0378] The server analyzes the received data and assesses the user's cognitive and emotional state. Machine learning models are used to analyze the impact of stress and emotional fluctuations on cognitive function.

[0379] Step 5:

[0380] The server assesses the risk of dementia based on the analysis results. If a user frequently expresses negative emotions, they are judged to be at high risk.

[0381] Step 6:

[0382] The server generates brain function training programs adapted to the user's emotional state. Relaxation games and art sessions are selected to evoke positive emotions.

[0383] Step 7:

[0384] The server sends the generated training program to the terminal and notifies the user.

[0385] Step 8:

[0386] The device prompts the user to complete the training program and continuously monitors their progress.

[0387] Step 9:

[0388] Users follow notifications to complete the training program and input their progress and feedback into their device.

[0389] Step 10:

[0390] The device sends user feedback to the server, verifies whether the training program is appropriate for the user's condition, and makes adjustments as needed.

[0391] Step 11:

[0392] The device continuously monitors emotional changes through an emotion engine and sends an alert to the server if a significant negative change is detected.

[0393] Step 12:

[0394] The server analyzes the reported negative emotional changes and, if necessary, suggests medical intervention to the user to encourage early action.

[0395] (Example 2)

[0396] 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".

[0397] In modern society, cognitive decline and the risk of dementia are significant health issues. Especially with the aging population, there is a need for effective methods to monitor individual cognitive function and provide appropriate training and interventions. However, existing methods struggle to provide highly accurate support because they cannot comprehensively assess emotions and behaviors in real time.

[0398] 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.

[0399] In this invention, the server includes means for collecting emotional and behavioral data from information processing devices, means for analyzing cognitive functions based on the collected data, and means for evaluating risk based on the analysis results. This makes it possible to capture changes in the user's emotions and behavior in real time and provide personalized support accordingly.

[0400] An "information processing device" is a device that collects user emotional and behavioral data and transmits that data to a server via communication.

[0401] "Cognitive function" refers to the mental abilities that humans use to understand information, remember it, solve problems, and make judgments.

[0402] "Analysis" is the process of evaluating the user's cognitive and emotional state based on collected data, and extracting meaning using a specific algorithm.

[0403] "Risk" refers to an indicator that shows the possibility of cognitive decline or dementia in the user.

[0404] A "training program" is a set of activities and exercises designed to improve a user's cognitive function and regulate their emotional state.

[0405] "Progress monitoring" means tracking how the training program is being implemented by users and whether it is having any effect.

[0406] "Anomaly detection" refers to identifying deviations from a user's normal cognitive function and emotional state, and issuing alerts as needed.

[0407] "Intervention" refers to specific measures or activities taken to improve a user's health or cognitive state.

[0408] This invention comprises an information processing device, an emotion engine, a training program generation module, and feedback and anomaly detection modules. Specifically, the terminal uses a camera and voice input sensors to acquire user emotion and behavior data. This data includes an emotion engine that captures the user's facial expressions and voice tone in real time and identifies their emotional state.

[0409] Data collected by the device is transmitted to the server via a secure protocol. The server uses the received data to evaluate the user's cognitive function using a generative AI model. Machine learning algorithms integrate lifestyle data and emotional data to estimate the user's risk of dementia. Based on the analysis results, the server generates an optimal training program to improve cognitive function. This program may include activities that promote the conversion to positive emotions, such as relaxation-oriented music sessions and meditation exercises.

[0410] For example, if a user exhibits a higher-than-usual stress level, the server can generate a training program that includes an art session to reduce stress and notify the user via their device. Furthermore, based on user feedback, the server re-evaluates the effectiveness of the training and dynamically adjusts the program content.

[0411] An example of a prompt is, "Using the user's emotional data and daily life data, propose an optimal training program to assess and support cognitive function."

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The device collects user emotion and behavior data. This process uses a camera and voice input sensor to capture the user's facial expressions and voice tone in real time. An emotion engine then analyzes this data to identify emotional states such as smiles or anger. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0415] Step 2:

[0416] The device packages the collected emotional and lifestyle data and sends it to the server using a secure protocol. The input is processed emotional data and other sensor data, and the output is a secure data transfer to the server. Encryption techniques are used in this step to maintain data integrity and confidentiality.

[0417] Step 3:

[0418] The server integrates received lifestyle and emotional data and uses a generative AI model to evaluate the user's cognitive function. Here, machine learning algorithms explore data correlations and estimate dementia risk, among other things. The input is the submitted dataset, and the output is the risk assessment results and analysis report.

[0419] Step 4:

[0420] The server generates a cognitive training program optimized for the user based on the analysis results. This training program includes activities aimed at stress reduction and relaxation. The input is the risk assessment results, and the output is the individual training program.

[0421] Step 5:

[0422] The terminal displays the generated training program to the user and monitors the program's progress. It records the user's progress as they perform the program and provides notifications as needed. The input is the generated program, and the output is user feedback and progress data.

[0423] Step 6:

[0424] After completing the training program, the user enters feedback based on their experience into a terminal. The terminal then sends this feedback back to the server, which is used for future evaluations and adjustments to the training program. The input is the user's feedback on the completed training, and the output is the transmission of the feedback data to the server.

[0425] Step 7:

[0426] The device continuously monitors the user's emotions using an emotion engine. If a significant negative change occurs, the device sends a notification to the server, which then suggests interventions as needed. The input is emotion monitoring data, and the output is an anomaly notification to the server.

[0427] (Application Example 2)

[0428] 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."

[0429] There is a challenge in detecting cognitive decline and emotional fluctuations in the elderly at an early stage and prompting appropriate intervention. In particular, there is a need to dynamically assess users' emotions and cognitive function in their daily living environments and provide individually adapted support.

[0430] 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.

[0431] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for analyzing emotional state and providing optimized relaxation or relaxation programs, and means for detecting sudden changes in emotion and notifying information. This makes it possible to comprehensively monitor the emotional and cognitive state of elderly people in their daily lives and provide personalized support quickly, thereby preventing cognitive decline and improving their quality of life.

[0432] An "information processing device" refers to hardware and software used to receive and analyze data.

[0433] "Means for analyzing cognitive function" refers to algorithms that evaluate the state of cognitive function based on user behavior and emotional data.

[0434] "Means of risk assessment" refers to the process of determining cognitive decline or stress levels based on analysis results.

[0435] "Means for generating personalized brain function training programs" refers to a system that designs optimized training content according to the user's cognitive function state.

[0436] "Means of tracking progress" refers to a system that records the history of training and activities performed by users and monitors their progress.

[0437] "Means of suggesting cognitive function promotion activities" refers to functions that recommend activities in the user's daily life to maintain and improve cognitive function.

[0438] "Means for analyzing emotional states" refers to a function that recognizes and analyzes emotions from the user's facial expressions and tone of voice.

[0439] "Means of providing optimized relaxation or relaxation programs" refers to a function that suggests appropriate activities to reduce stress based on the user's emotional state.

[0440] "Means of detecting sudden emotional changes and notifying information" refers to a function that recognizes significant emotional fluctuations in real time and transmits that information to relevant parties.

[0441] The system for realizing this invention primarily comprises an information processing terminal, a server, and user interface functions. First, the terminal acquires emotional and behavioral data from the user in real time. This data acquisition uses sensor devices such as a camera and microphone, and is equipped with an emotion engine that includes facial recognition and voice analysis.

[0442] Data acquired by the device is sent to the server. The server uses this data to apply machine learning algorithms to analyze the user's cognitive and emotional state. TensorFlow or similar technology platforms are used for this analysis. Based on the analysis results, a personalized brain function training program is generated for the user. This program aims to reduce stress in daily life and improve cognitive function, and includes gaming activities and relaxation exercises.

[0443] For example, if the system detects that a user tends to experience high levels of stress on weekends, it can suggest relaxing activities such as listening to calming music or light exercise. Furthermore, if a sudden change in mood is detected, relevant notifications are sent to family members or caregivers to ensure prompt support.

[0444] When a user considers activities to relieve stress, a prompt such as "Suggest activities that the user can do to relieve stress" can be used with a generative AI model.

[0445] Through this system, it is expected that users' daily lives will be comprehensively supported, and their cognitive functions will be maintained and improved.

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The device uses a camera and microphone to acquire user emotion and behavior data. Camera images and audio data are used as input. The emotion engine analyzes facial expressions and tone using facial recognition algorithms and voice analysis technology, and outputs the user's emotional state. Specifically, if the user is smiling, the system recognizes this as "positive."

[0449] Step 2:

[0450] The acquired emotional and activity data is sent to the server in real time. The server receives this data as input and uses machine learning algorithms to analyze it, evaluate the user's cognitive function, and output daily risk information. Specifically, if decreased attention is observed while under high stress, this is evaluated as "at risk."

[0451] Step 3:

[0452] The server generates a brain function training program tailored to the user based on the analyzed results. This program receives the analysis results as input and suggests specific games or relaxation activities as output. The program is then sent to the user's device, and the program begins. For example, if relaxation is needed, a session listening to calming music might be suggested.

[0453] Step 4:

[0454] The user performs the suggested training or relaxation program on the device, and the device tracks their progress in real time. It receives user performance data as input and sends progress data to the server as feedback. Specifically, if the achievement rate exceeds a certain level, a "going well" message is displayed on the device.

[0455] Step 5:

[0456] The server evaluates the effectiveness of the training program based on user feedback data and readjusts the program as needed. In this case, the feedback data becomes the input, and the adjusted program is output. Specifically, if an activity is found to be ineffective, a new activity is suggested as an alternative.

[0457] Step 6:

[0458] The device constantly monitors the user's emotional changes via an emotion engine and reports to the server if a sudden negative change is detected. Monitoring data is used as input, and a notification message is output. Specifically, if negative emotions persist, an alert is sent to the caregiver.

[0459] Step 7:

[0460] When a user spontaneously uses a generative AI model to come up with activities, they can prompt the model with a message like, "Suggest activities that the user can do to relieve stress," and the AI ​​will output suggestions. Specific examples of suggestions from the AI ​​include "a short walk" and "recommendation for deep breathing."

[0461] 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.

[0462] 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.

[0463] 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.

[0464] [Third Embodiment]

[0465] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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).

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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".

[0477] The system according to the present invention aims to evaluate the user's cognitive function in daily life and to improve or maintain that function through appropriate intervention. This system mainly includes an information processing device, a data analysis algorithm, a training program module, and user interaction elements.

[0478] First, the device collects everyday data using various sensors (such as voice recognition and motion sensors) to record the user's actions and conversations. Smartphones or dedicated wearable devices are commonly used for this purpose. The collected data is securely transmitted to a server in a format that allows for real-time analysis.

[0479] The server uses advanced machine learning algorithms to analyze the received data. This analysis assesses key cognitive functions such as the user's language processing ability, memory, and attention. Furthermore, based on this assessment, an individual dementia risk is calculated. For example, if a user's vocabulary narrows or their motor skills decline, these may be treated as early signs of cognitive decline.

[0480] Next, the server automatically generates a brain function training program optimized for the user based on these evaluation results. Examples include reflex games to improve attention and storytelling exercises to enhance memory. These programs are incorporated into the user's schedule and notified via their device.

[0481] Users complete the presented training program and input their results and feedback into the terminal. This information is then sent back to the server and used to further improve the programs provided by the system.

[0482] Furthermore, the system also makes activity suggestions to users to promote social interaction in their daily lives. For example, it may recommend participating in activities held at a nearby community center. In addition, if the terminal detects any abnormalities on a daily basis, such as changes in speech fluency or gestures during conversation, the server will analyze the information and suggest contact to prompt medical intervention.

[0483] Thus, the system of the present invention comprehensively supports the user's cognitive function and provides a consistent service from early problem detection to the implementation of an optimal training program, thereby helping to prevent cognitive decline and maintain a healthy living environment.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The device collects data from the user's daily life. This includes conversation monitoring using voice input devices and motion analysis using motion sensors.

[0487] Step 2:

[0488] The device temporarily stores the collected data and preprocesses it as needed. This preprocessing includes noise reduction and data formatting.

[0489] Step 3:

[0490] The terminal sends pre-processed data to the server. Data transmission is performed via a security protocol to ensure security.

[0491] Step 4:

[0492] The server applies machine learning algorithms to analyze the received data. Cognitive function-related metrics are calculated, and the user's current cognitive state is evaluated.

[0493] Step 5:

[0494] The server predicts the risk of dementia based on the analysis results. Risk assessment is performed based on past medical data and existing statistical models.

[0495] Step 6:

[0496] The server takes into account the dementia risk and cognitive function assessment results to generate a brain function training program optimized for the user.

[0497] Step 7:

[0498] The server sends the generated training program to the terminal. The program includes game-based exercises and memory-improving tasks.

[0499] Step 8:

[0500] The device notifies the user of the training program and encourages them to complete it. It may also set reminders if the user is busy.

[0501] Step 9:

[0502] Users follow notifications from their devices to complete the training program. Progress and results are fed back to the device.

[0503] Step 10:

[0504] The device uploads user feedback data to the server. Based on this feedback, the training plan for the entire system is optimized.

[0505] Step 11:

[0506] The device continues to collect data through daily observations and detects anomalies. When an anomaly is detected, an alert is sent to the server.

[0507] Step 12:

[0508] The server analyzes the anomaly detection data and, if necessary, notifies the user to consider medical intervention.

[0509] Step 13:

[0510] The server periodically analyzes data and provides users with activity suggestions via their devices to promote social interaction. This helps to improve users' quality of life.

[0511] (Example 1)

[0512] 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."

[0513] Many people are at risk of cognitive decline due to aging and lifestyle factors, but there is a challenge in providing individually optimized interventions. Furthermore, many current systems are insufficient in early detection of changes in cognitive function and in providing effective countermeasures.

[0514] 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.

[0515] In this invention, the server includes means for analyzing cognitive function based on data obtained from information processing means, means for dynamically evaluating risk based on the analysis results, and means for automatically generating an individualized training program corresponding to the evaluated risk. This enables improvement of cognitive function and early detection of anomalies that are tailored to individual needs.

[0516] "Information processing means" refers to all technological devices that support the process of collecting, transmitting, and analyzing data.

[0517] "Cognitive function" refers to all abilities related to human intellectual activity, such as memory, attention, and language processing.

[0518] "Analysis" refers to the process of extracting information from collected data using specific algorithms and methods, and deriving specific conclusions.

[0519] "Risk" refers to an indicator of the likelihood of developing cognitive decline or cognitive-related disorders.

[0520] A "training program" refers to a set of activities or exercises aimed at improving or maintaining specific cognitive functions.

[0521] A "user terminal" refers to a device used by a user to interact with the system.

[0522] "Feedback" refers to information or reactions from users or systems regarding the activities that have been carried out.

[0523] "Abnormal" refers to changes or behaviors in cognitive function that deviate from the normal range.

[0524] "Promoting activity" refers to suggestions and advice that encourage users to actively participate in their daily lives.

[0525] "Intervention" refers to specific measures or actions taken in response to cognitive abnormalities or declines.

[0526] This invention provides a system that comprehensively evaluates a user's cognitive functions in their daily life and proposes the optimal intervention. Specific embodiments of this system are described below.

[0527] The device uses various sensors, including voice recognition and motion sensors, to record the user's actions and conversations. Typically, smartphones or dedicated wearable devices are used. The data recorded by the device is transmitted to a server via a secure protocol. The data is encrypted during this transmission, ensuring its safety.

[0528] The server uses machine learning algorithms to analyze the incoming data. Specifically, it uses open-source libraries and custom models to evaluate important cognitive functions such as the user's language processing ability, memory, and attention. Based on the analysis results, an individual's dementia risk is calculated.

[0529] The server automatically generates a brain function training program optimized based on the user's assessment. This includes reflex games to enhance attention and storytelling exercises to improve memory. These training programs are implemented using Python and other languages ​​and are tailored to the user's cognitive characteristics and daily routine. The generated program is notified to the user via the terminal.

[0530] Users complete the presented training program and input the results and feedback into their device. This information is then sent back to the server and incorporated into the analysis, thereby improving the accuracy of the training program.

[0531] Furthermore, the system makes suggestions to promote social interaction within the user's daily activities. For example, it encourages participation in local community events to improve cognitive function. In addition, if an anomaly is detected, analysis is performed on the server to propose rapid intervention.

[0532] As a concrete example, an example of a prompt sentence to be input into a generative AI model is: "Describe a system for evaluating a user's cognitive function in their daily life and proposing a training program." With this prompt, the generative AI model can successively provide optimized programs tailored to the user.

[0533] As described above, the system according to this invention provides comprehensive support to enrich the user's daily life and prevent cognitive decline.

[0534] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0535] Step 1:

[0536] The device uses voice recognition and motion sensors to record user actions and conversations in real time. Input consists of user voice and motion data, which is digitized and stored. Output is a digital dataset representing user activity. This data contains information necessary for subsequent analysis.

[0537] Step 2:

[0538] The terminal encrypts the recorded digital data and sends it to the server using a secure protocol. The input is the digital dataset before encryption, which is securely transferred via the communication protocol. The output is the encrypted data stream received by the server.

[0539] Step 3:

[0540] The server decrypts the received encrypted data and performs preprocessing for analysis. The input is an encrypted data stream sent from the terminal, and the decrypted data is converted into an analyzable format. The output is the data ready for analysis.

[0541] Step 4:

[0542] The server applies machine learning algorithms to the decrypted data to evaluate the user's cognitive function. The input is the decrypted data, from which indicators such as language processing ability and memory are extracted. The output is an evaluation report that quantifies the user's cognitive function.

[0543] Step 5:

[0544] The server generates a personalized training program for each user based on the assessment report. The input is the user's cognitive function assessment report, which is used to select the optimal exercises. The output is the schedule and content of the generated training program.

[0545] Step 6:

[0546] The terminal notifies the user of the training program received from the server. The input is the program data sent from the server and displayed on the user's device. The output is a notification message and action reminder to the user.

[0547] Step 7:

[0548] Users perform exercises through the notified training program and input feedback into the device. The input includes the degree of completion and impressions of the completed program, which is digitized and stored on the device. The output is the feedback data stored on the device.

[0549] Step 8:

[0550] The server analyzes feedback received from the terminals to improve the training program. The input is user feedback data, which is used to improve the program in the next iteration. The output is the updated training program and improvement metrics.

[0551] (Application Example 1)

[0552] 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."

[0553] There is a challenge in detecting cognitive decline in users, including the elderly, at an early stage and providing personalized training to improve cognitive function and maintain a healthy lifestyle. Furthermore, promoting social interaction and preventing isolation is important, but it is not easy to propose this individually and efficiently.

[0554] 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.

[0555] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for evaluating risk based on the analysis results, means for generating an individualized brain function training program according to the evaluated risk, means for performing cognitive evaluation using a process including motion recognition, and means for utilizing computing resources to perform the analysis in a cloud environment. This enables appropriate evaluation of the user's cognitive function in daily life and individualized intervention.

[0556] An "information processing device" is a device that records user activities and conversations and processes them as data.

[0557] "Cognitive function" refers to functions related to brain activity, including the user's language processing ability, memory, and attention.

[0558] "Analysis" is the process of evaluating cognitive function using machine learning algorithms and other methods based on collected data.

[0559] "Risk assessment" is the process of estimating the likelihood of cognitive decline or progression to dementia based on the results of an analysis.

[0560] A "brain function training program" is a set of programs that include specific exercises and activities aimed at improving or maintaining cognitive function.

[0561] "Motion recognition" is the process of detecting a user's movements and actions through sensors and using that information as data.

[0562] A "cloud environment" is a collection of data storage and computing resources provided via the internet, enabling real-time data processing.

[0563] The system for implementing the present invention implements a program that combines an information processing device, a cloud environment, and machine learning algorithms to evaluate the user's cognitive function in real time and enable personalized interventions.

[0564] The devices will be offered in the form of smart glasses or smartphones and will be equipped with sensors for voice recognition and motion recognition. This will allow for the collection of data on the user's daily activities. Specific examples include analysis of the vocabulary the user uses in everyday conversation and detection of anomalies from daily behavioral patterns.

[0565] The collected data is securely transmitted to the server via Wi-Fi or mobile network. The server is built in a cloud environment and analyzes the data using advanced machine learning algorithms. Technologies used include analyzing speech data with the Google Speech-to-Text API and building machine learning models using TensorFlow.

[0566] Based on the analysis, the server assesses the user's cognitive function risks and generates a personalized brain function training program as needed. This program is communicated to the user via a terminal and implemented. For example, if the user begins using a particular word frequently, a storytelling exercise to improve memory is suggested based on that.

[0567] Furthermore, this system can detect anomalies in real time and provide necessary interventions quickly. It also suggests activities that promote social interaction, thus contributing to the prevention of isolation. Examples of prompts include, "Extract information from changes in the vocabulary a 70-year-old man uses in his daily life," and "Analyze the trend of daily decline in physical fitness and suggest an optimal exercise program."

[0568] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0569] Step 1:

[0570] The device continuously collects user voice and motion data using built-in sensors. In this step, speech recognition technology transcribes conversations into text, and motion sensors record movement patterns. The input is the user's raw data, and the output is a data file encoded in digital format.

[0571] Step 2:

[0572] The device transmits the collected data to the server via Wi-Fi or a mobile network. The input here is the data file generated in step 1, and the output is data encrypted using a secure communication protocol.

[0573] Step 3:

[0574] The server analyzes the received data in a cloud environment using machine learning algorithms. Specifically, it performs frequency analysis of language use from speech data and evaluation of activity patterns from behavioral data. The input is the encrypted data sent in step 2, and the output is the result of the cognitive function evaluation.

[0575] Step 4:

[0576] The server generates a personalized brain function training program based on the analysis results. For example, if the frequency of use of a particular linguistic expression changes, it will suggest specific exercises to improve memory. The input is the evaluation results from step 3, and the output is the user-specific training program.

[0577] Step 5:

[0578] The server utilizes idle time to notify the terminal of the created training program. The terminal displays the notification to the user and supports the user in starting the program. The input is the training program from step 4, and the output is the program notification to the user.

[0579] Step 6:

[0580] The user performs activities according to the instructions of the training program displayed on the device. Completion data for each activity is entered into the device and sent back to the server. The input is the user's completion data, and the output is the feedback data sent to the server.

[0581] Step 7:

[0582] The server analyzes the feedback data and considers suggestions for improving the training program. It utilizes generative AI models as needed to develop new programming strategies. The input is the feedback data from step 6, and the output is the improved future training program.

[0583] 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.

[0584] This invention relates to a cognitive function analysis system incorporating an emotion engine, which provides comprehensive cognitive function assessment and support based on the user's daily life data. The system includes an information processing device, an emotion recognition module, a training program generation module, and user feedback and anomaly detection modules.

[0585] First, the device acquires user emotional and behavioral data. This includes an emotion engine that uses cameras and voice input sensors to recognize emotions from facial expressions and tone of voice. For example, if a user displays a smile or an angry expression, the emotion engine recognizes the change in real time. This data, along with other lifestyle data necessary for analyzing cognitive function, is sent to the server.

[0586] The server integrates and analyzes received lifestyle and emotional data. Using machine learning algorithms, it comprehensively evaluates the user's cognitive and emotional state and estimates their daily risk of dementia. For example, if a decrease in attention is detected when the user is under excessive stress, this is considered a risk factor.

[0587] Next, the server dynamically adjusts a brain function training program optimized for the user based on the recognized emotional state. This training program is designed to positively change the user's emotions and includes, for example, games that promote relaxation and art sessions that are expected to relieve stress.

[0588] The terminal monitors the program's progress and provides the user with continuous notifications and feedback. The user enters feedback on the training they've completed into the terminal, and this data is sent to the server for re-evaluation. This feedback loop allows the system to quickly respond to user changes and continuously optimize the program.

[0589] Furthermore, the device constantly monitors emotional changes through its emotion engine and reports to the server if a significant negative change is detected. Based on this information, the server can suggest to the user that they consider early medical intervention.

[0590] Thus, the present invention contributes to dementia prevention and improvement of quality of life by comprehensively monitoring the user's cognitive function from an emotional perspective and providing personalized support.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The device uses a camera and voice input device to collect emotional data in real time from the user's facial expressions and tone of voice.

[0594] Step 2:

[0595] The device preprocesses the collected emotion data, performing noise reduction and data formatting. This preprocessing converts the data into a format suitable for analysis.

[0596] Step 3:

[0597] The device sends pre-processed emotional data along with other lifestyle data to the server. A secure protocol is used for data transmission.

[0598] Step 4:

[0599] The server analyzes the received data and assesses the user's cognitive and emotional state. Machine learning models are used to analyze the impact of stress and emotional fluctuations on cognitive function.

[0600] Step 5:

[0601] The server assesses the risk of dementia based on the analysis results. If a user frequently expresses negative emotions, they are judged to be at high risk.

[0602] Step 6:

[0603] The server generates brain function training programs adapted to the user's emotional state. Relaxation games and art sessions are selected to evoke positive emotions.

[0604] Step 7:

[0605] The server sends the generated training program to the terminal and notifies the user.

[0606] Step 8:

[0607] The device prompts the user to complete the training program and continuously monitors their progress.

[0608] Step 9:

[0609] Users follow notifications to complete the training program and input their progress and feedback into their device.

[0610] Step 10:

[0611] The device sends user feedback to the server, verifies whether the training program is appropriate for the user's condition, and makes adjustments as needed.

[0612] Step 11:

[0613] The device continuously monitors emotional changes through an emotion engine and sends an alert to the server if a significant negative change is detected.

[0614] Step 12:

[0615] The server analyzes the reported negative emotional changes and, if necessary, suggests medical intervention to the user to encourage early action.

[0616] (Example 2)

[0617] 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."

[0618] In modern society, cognitive decline and the risk of dementia are significant health issues. Especially with the aging population, there is a need for effective methods to monitor individual cognitive function and provide appropriate training and interventions. However, existing methods struggle to provide highly accurate support because they cannot comprehensively assess emotions and behaviors in real time.

[0619] 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.

[0620] In this invention, the server includes means for collecting emotional and behavioral data from information processing devices, means for analyzing cognitive functions based on the collected data, and means for evaluating risk based on the analysis results. This makes it possible to capture changes in the user's emotions and behavior in real time and provide personalized support accordingly.

[0621] An "information processing device" is a device that collects user emotional and behavioral data and transmits that data to a server via communication.

[0622] "Cognitive function" refers to the mental abilities that humans use to understand information, remember it, solve problems, and make judgments.

[0623] "Analysis" is the process of evaluating the user's cognitive and emotional state based on collected data, and extracting meaning using a specific algorithm.

[0624] "Risk" refers to an indicator that shows the possibility of cognitive decline or dementia in the user.

[0625] A "training program" is a set of activities and exercises designed to improve a user's cognitive function and regulate their emotional state.

[0626] "Progress monitoring" means tracking how the training program is being implemented by users and whether it is having any effect.

[0627] "Anomaly detection" refers to identifying deviations from a user's normal cognitive function and emotional state, and issuing alerts as needed.

[0628] "Intervention" refers to specific measures or activities taken to improve a user's health or cognitive state.

[0629] This invention comprises an information processing device, an emotion engine, a training program generation module, and feedback and anomaly detection modules. Specifically, the terminal uses a camera and voice input sensors to acquire user emotion and behavior data. This data includes an emotion engine that captures the user's facial expressions and voice tone in real time and identifies their emotional state.

[0630] Data collected by the device is transmitted to the server via a secure protocol. The server uses the received data to evaluate the user's cognitive function using a generative AI model. Machine learning algorithms integrate lifestyle data and emotional data to estimate the user's risk of dementia. Based on the analysis results, the server generates an optimal training program to improve cognitive function. This program may include activities that promote the conversion to positive emotions, such as relaxation-oriented music sessions and meditation exercises.

[0631] For example, if a user exhibits a higher-than-usual stress level, the server can generate a training program that includes an art session to reduce stress and notify the user via their device. Furthermore, based on user feedback, the server re-evaluates the effectiveness of the training and dynamically adjusts the program content.

[0632] An example of a prompt is, "Using the user's emotional data and daily life data, propose an optimal training program to assess and support cognitive function."

[0633] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0634] Step 1:

[0635] The device collects user emotion and behavior data. This process uses a camera and voice input sensor to capture the user's facial expressions and voice tone in real time. An emotion engine then analyzes this data to identify emotional states such as smiles or anger. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0636] Step 2:

[0637] The device packages the collected emotional and lifestyle data and sends it to the server using a secure protocol. The input is processed emotional data and other sensor data, and the output is a secure data transfer to the server. Encryption techniques are used in this step to maintain data integrity and confidentiality.

[0638] Step 3:

[0639] The server integrates received lifestyle and emotional data and uses a generative AI model to evaluate the user's cognitive function. Here, machine learning algorithms explore data correlations and estimate dementia risk, among other things. The input is the submitted dataset, and the output is the risk assessment results and analysis report.

[0640] Step 4:

[0641] The server generates a cognitive training program optimized for the user based on the analysis results. This training program includes activities aimed at stress reduction and relaxation. The input is the risk assessment results, and the output is the individual training program.

[0642] Step 5:

[0643] The terminal displays the generated training program to the user and monitors the program's progress. It records the user's progress as they perform the program and provides notifications as needed. The input is the generated program, and the output is user feedback and progress data.

[0644] Step 6:

[0645] After completing the training program, the user enters feedback based on their experience into a terminal. The terminal then sends this feedback back to the server, which is used for future evaluations and adjustments to the training program. The input is the user's feedback on the completed training, and the output is the transmission of the feedback data to the server.

[0646] Step 7:

[0647] The device continuously monitors the user's emotions using an emotion engine. If a significant negative change occurs, the device sends a notification to the server, which then suggests interventions as needed. The input is emotion monitoring data, and the output is an anomaly notification to the server.

[0648] (Application Example 2)

[0649] 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."

[0650] There is a challenge in detecting cognitive decline and emotional fluctuations in the elderly at an early stage and prompting appropriate intervention. In particular, there is a need to dynamically assess users' emotions and cognitive function in their daily living environments and provide individually adapted support.

[0651] 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.

[0652] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for analyzing emotional state and providing optimized relaxation or relaxation programs, and means for detecting sudden changes in emotion and notifying information. This makes it possible to comprehensively monitor the emotional and cognitive state of elderly people in their daily lives and provide personalized support quickly, thereby preventing cognitive decline and improving their quality of life.

[0653] An "information processing device" refers to hardware and software used to receive and analyze data.

[0654] "Means for analyzing cognitive function" refers to algorithms that evaluate the state of cognitive function based on user behavior and emotional data.

[0655] "Means of risk assessment" refers to the process of determining cognitive decline or stress levels based on analysis results.

[0656] "Means for generating personalized brain function training programs" refers to a system that designs optimized training content according to the user's cognitive function state.

[0657] "Means of tracking progress" refers to a system that records the history of training and activities performed by users and monitors their progress.

[0658] "Means of suggesting cognitive function promotion activities" refers to functions that recommend activities in the user's daily life to maintain and improve cognitive function.

[0659] "Means for analyzing emotional states" refers to a function that recognizes and analyzes emotions from the user's facial expressions and tone of voice.

[0660] "Means of providing optimized relaxation or relaxation programs" refers to a function that suggests appropriate activities to reduce stress based on the user's emotional state.

[0661] "Means of detecting sudden emotional changes and notifying information" refers to a function that recognizes significant emotional fluctuations in real time and transmits that information to relevant parties.

[0662] The system for realizing this invention primarily comprises an information processing terminal, a server, and user interface functions. First, the terminal acquires emotional and behavioral data from the user in real time. This data acquisition uses sensor devices such as a camera and microphone, and is equipped with an emotion engine that includes facial recognition and voice analysis.

[0663] Data acquired by the device is sent to the server. The server uses this data to apply machine learning algorithms to analyze the user's cognitive and emotional state. TensorFlow or similar technology platforms are used for this analysis. Based on the analysis results, a personalized brain function training program is generated for the user. This program aims to reduce stress in daily life and improve cognitive function, and includes gaming activities and relaxation exercises.

[0664] For example, if the system detects that a user tends to experience high levels of stress on weekends, it can suggest relaxing activities such as listening to calming music or light exercise. Furthermore, if a sudden change in mood is detected, relevant notifications are sent to family members or caregivers to ensure prompt support.

[0665] When a user considers activities to relieve stress, a prompt such as "Suggest activities that the user can do to relieve stress" can be used with a generative AI model.

[0666] Through this system, it is expected that users' daily lives will be comprehensively supported, and their cognitive functions will be maintained and improved.

[0667] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0668] Step 1:

[0669] The device uses a camera and microphone to acquire user emotion and behavior data. Camera images and audio data are used as input. The emotion engine analyzes facial expressions and tone using facial recognition algorithms and voice analysis technology, and outputs the user's emotional state. Specifically, if the user is smiling, the system recognizes this as "positive."

[0670] Step 2:

[0671] The acquired emotional and activity data is sent to the server in real time. The server receives this data as input and uses machine learning algorithms to analyze it, evaluate the user's cognitive function, and output daily risk information. Specifically, if decreased attention is observed while under high stress, this is evaluated as "at risk."

[0672] Step 3:

[0673] The server generates a brain function training program tailored to the user based on the analyzed results. This program receives the analysis results as input and suggests specific games or relaxation activities as output. The program is then sent to the user's device, and the program begins. For example, if relaxation is needed, a session listening to calming music might be suggested.

[0674] Step 4:

[0675] The user performs the suggested training or relaxation program on the device, and the device tracks their progress in real time. It receives user performance data as input and sends progress data to the server as feedback. Specifically, if the achievement rate exceeds a certain level, a "going well" message is displayed on the device.

[0676] Step 5:

[0677] The server evaluates the effectiveness of the training program based on user feedback data and readjusts the program as needed. In this case, the feedback data becomes the input, and the adjusted program is output. Specifically, if an activity is found to be ineffective, a new activity is suggested as an alternative.

[0678] Step 6:

[0679] The device constantly monitors the user's emotional changes via an emotion engine and reports to the server if a sudden negative change is detected. Monitoring data is used as input, and a notification message is output. Specifically, if negative emotions persist, an alert is sent to the caregiver.

[0680] Step 7:

[0681] When a user spontaneously uses a generative AI model to come up with activities, they can prompt the model with a message like, "Suggest activities that the user can do to relieve stress," and the AI ​​will output suggestions. Specific examples of suggestions from the AI ​​include "a short walk" and "recommendation for deep breathing."

[0682] 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.

[0683] 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.

[0684] 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.

[0685] [Fourth Embodiment]

[0686] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0687] 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.

[0688] 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).

[0689] 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.

[0690] 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.

[0691] 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).

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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".

[0699] The system according to the present invention aims to evaluate the user's cognitive function in daily life and to improve or maintain that function through appropriate intervention. This system mainly includes an information processing device, a data analysis algorithm, a training program module, and user interaction elements.

[0700] First, the device collects everyday data using various sensors (such as voice recognition and motion sensors) to record the user's actions and conversations. Smartphones or dedicated wearable devices are commonly used for this purpose. The collected data is securely transmitted to a server in a format that allows for real-time analysis.

[0701] The server uses advanced machine learning algorithms to analyze the received data. This analysis assesses key cognitive functions such as the user's language processing ability, memory, and attention. Furthermore, based on this assessment, an individual dementia risk is calculated. For example, if a user's vocabulary narrows or their motor skills decline, these may be treated as early signs of cognitive decline.

[0702] Next, the server automatically generates a brain function training program optimized for the user based on these evaluation results. Examples include reflex games to improve attention and storytelling exercises to enhance memory. These programs are incorporated into the user's schedule and notified via their device.

[0703] Users complete the presented training program and input their results and feedback into the terminal. This information is then sent back to the server and used to further improve the programs provided by the system.

[0704] Furthermore, the system also makes activity suggestions to users to promote social interaction in their daily lives. For example, it may recommend participating in activities held at a nearby community center. In addition, if the terminal detects any abnormalities on a daily basis, such as changes in speech fluency or gestures during conversation, the server will analyze the information and suggest contact to prompt medical intervention.

[0705] Thus, the system of the present invention comprehensively supports the user's cognitive function and provides a consistent service from early problem detection to the implementation of an optimal training program, thereby helping to prevent cognitive decline and maintain a healthy living environment.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The device collects data from the user's daily life. This includes conversation monitoring using voice input devices and motion analysis using motion sensors.

[0709] Step 2:

[0710] The device temporarily stores the collected data and preprocesses it as needed. This preprocessing includes noise reduction and data formatting.

[0711] Step 3:

[0712] The terminal sends pre-processed data to the server. Data transmission is performed via a security protocol to ensure security.

[0713] Step 4:

[0714] The server applies machine learning algorithms to analyze the received data. Cognitive function-related metrics are calculated, and the user's current cognitive state is evaluated.

[0715] Step 5:

[0716] The server predicts the risk of dementia based on the analysis results. Risk assessment is performed based on past medical data and existing statistical models.

[0717] Step 6:

[0718] The server takes into account the dementia risk and cognitive function assessment results to generate a brain function training program optimized for the user.

[0719] Step 7:

[0720] The server sends the generated training program to the terminal. The program includes game-based exercises and memory-improving tasks.

[0721] Step 8:

[0722] The device notifies the user of the training program and encourages them to complete it. It may also set reminders if the user is busy.

[0723] Step 9:

[0724] Users follow notifications from their devices to complete the training program. Progress and results are fed back to the device.

[0725] Step 10:

[0726] The device uploads user feedback data to the server. Based on this feedback, the training plan for the entire system is optimized.

[0727] Step 11:

[0728] The device continues to collect data through daily observations and detects anomalies. When an anomaly is detected, an alert is sent to the server.

[0729] Step 12:

[0730] The server analyzes the anomaly detection data and, if necessary, notifies the user to consider medical intervention.

[0731] Step 13:

[0732] The server periodically analyzes data and provides users with activity suggestions via their devices to promote social interaction. This helps to improve users' quality of life.

[0733] (Example 1)

[0734] 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".

[0735] Many people are at risk of cognitive decline due to aging and lifestyle factors, but there is a challenge in providing individually optimized interventions. Furthermore, many current systems are insufficient in early detection of changes in cognitive function and in providing effective countermeasures.

[0736] 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.

[0737] In this invention, the server includes means for analyzing cognitive function based on data obtained from information processing means, means for dynamically evaluating risk based on the analysis results, and means for automatically generating an individualized training program corresponding to the evaluated risk. This enables improvement of cognitive function and early detection of anomalies that are tailored to individual needs.

[0738] "Information processing means" refers to all technological devices that support the process of collecting, transmitting, and analyzing data.

[0739] "Cognitive function" refers to all abilities related to human intellectual activity, such as memory, attention, and language processing.

[0740] "Analysis" refers to the process of extracting information from collected data using specific algorithms and methods, and deriving specific conclusions.

[0741] "Risk" refers to an indicator of the likelihood of developing cognitive decline or cognitive-related disorders.

[0742] A "training program" refers to a set of activities or exercises aimed at improving or maintaining specific cognitive functions.

[0743] A "user terminal" refers to a device used by a user to interact with the system.

[0744] "Feedback" refers to information or reactions from users or systems regarding the activities that have been carried out.

[0745] "Abnormal" refers to changes or behaviors in cognitive function that deviate from the normal range.

[0746] "Promoting activity" refers to suggestions and advice that encourage users to actively participate in their daily lives.

[0747] "Intervention" refers to specific measures or actions taken in response to cognitive abnormalities or declines.

[0748] This invention provides a system that comprehensively evaluates a user's cognitive functions in their daily life and proposes the optimal intervention. Specific embodiments of this system are described below.

[0749] The device uses various sensors, including voice recognition and motion sensors, to record the user's actions and conversations. Typically, smartphones or dedicated wearable devices are used. The data recorded by the device is transmitted to a server via a secure protocol. The data is encrypted during this transmission, ensuring its safety.

[0750] The server uses machine learning algorithms to analyze the incoming data. Specifically, it uses open-source libraries and custom models to evaluate important cognitive functions such as the user's language processing ability, memory, and attention. Based on the analysis results, an individual's dementia risk is calculated.

[0751] The server automatically generates a brain function training program optimized based on the user's assessment. This includes reflex games to enhance attention and storytelling exercises to improve memory. These training programs are implemented using Python and other languages ​​and are tailored to the user's cognitive characteristics and daily routine. The generated program is notified to the user via the terminal.

[0752] Users complete the presented training program and input the results and feedback into their device. This information is then sent back to the server and incorporated into the analysis, thereby improving the accuracy of the training program.

[0753] Furthermore, the system makes suggestions to promote social interaction within the user's daily activities. For example, it encourages participation in local community events to improve cognitive function. In addition, if an anomaly is detected, analysis is performed on the server to propose rapid intervention.

[0754] As a concrete example, an example of a prompt sentence to be input into a generative AI model is: "Describe a system for evaluating a user's cognitive function in their daily life and proposing a training program." With this prompt, the generative AI model can successively provide optimized programs tailored to the user.

[0755] As described above, the system according to this invention provides comprehensive support to enrich the user's daily life and prevent cognitive decline.

[0756] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0757] Step 1:

[0758] The device uses voice recognition and motion sensors to record user actions and conversations in real time. Input consists of user voice and motion data, which is digitized and stored. Output is a digital dataset representing user activity. This data contains information necessary for subsequent analysis.

[0759] Step 2:

[0760] The terminal encrypts the recorded digital data and sends it to the server using a secure protocol. The input is the digital dataset before encryption, which is securely transferred via the communication protocol. The output is the encrypted data stream received by the server.

[0761] Step 3:

[0762] The server decrypts the received encrypted data and performs preprocessing for analysis. The input is an encrypted data stream sent from the terminal, and the decrypted data is converted into an analyzable format. The output is the data ready for analysis.

[0763] Step 4:

[0764] The server applies machine learning algorithms to the decrypted data to evaluate the user's cognitive function. The input is the decrypted data, from which indicators such as language processing ability and memory are extracted. The output is an evaluation report that quantifies the user's cognitive function.

[0765] Step 5:

[0766] The server generates a personalized training program for each user based on the assessment report. The input is the user's cognitive function assessment report, which is used to select the optimal exercises. The output is the schedule and content of the generated training program.

[0767] Step 6:

[0768] The terminal notifies the user of the training program received from the server. The input is the program data sent from the server and displayed on the user's device. The output is a notification message and action reminder to the user.

[0769] Step 7:

[0770] Users perform exercises through the notified training program and input feedback into the device. The input includes the degree of completion and impressions of the completed program, which is digitized and stored on the device. The output is the feedback data stored on the device.

[0771] Step 8:

[0772] The server analyzes feedback received from the terminals to improve the training program. The input is user feedback data, which is used to improve the program in the next iteration. The output is the updated training program and improvement metrics.

[0773] (Application Example 1)

[0774] 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".

[0775] There is a challenge in detecting cognitive decline in users, including the elderly, at an early stage and providing personalized training to improve cognitive function and maintain a healthy lifestyle. Furthermore, promoting social interaction and preventing isolation is important, but it is not easy to propose this individually and efficiently.

[0776] 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.

[0777] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for evaluating risk based on the analysis results, means for generating an individualized brain function training program according to the evaluated risk, means for performing cognitive evaluation using a process including motion recognition, and means for utilizing computing resources to perform the analysis in a cloud environment. This enables appropriate evaluation of the user's cognitive function in daily life and individualized intervention.

[0778] An "information processing device" is a device that records user activities and conversations and processes them as data.

[0779] "Cognitive function" refers to functions related to brain activity, including the user's language processing ability, memory, and attention.

[0780] "Analysis" is the process of evaluating cognitive function using machine learning algorithms and other methods based on collected data.

[0781] "Risk assessment" is the process of estimating the likelihood of cognitive decline or progression to dementia based on the results of an analysis.

[0782] A "brain function training program" is a set of programs that include specific exercises and activities aimed at improving or maintaining cognitive function.

[0783] "Motion recognition" is the process of detecting a user's movements and actions through sensors and using that information as data.

[0784] A "cloud environment" is a collection of data storage and computing resources provided via the internet, enabling real-time data processing.

[0785] The system for implementing the present invention implements a program that combines an information processing device, a cloud environment, and machine learning algorithms to evaluate the user's cognitive function in real time and enable personalized interventions.

[0786] The devices will be offered in the form of smart glasses or smartphones and will be equipped with sensors for voice recognition and motion recognition. This will allow for the collection of data on the user's daily activities. Specific examples include analysis of the vocabulary the user uses in everyday conversation and detection of anomalies from daily behavioral patterns.

[0787] The collected data is securely transmitted to the server via Wi-Fi or mobile network. The server is built in a cloud environment and analyzes the data using advanced machine learning algorithms. Technologies used include analyzing speech data with the Google Speech-to-Text API and building machine learning models using TensorFlow.

[0788] Based on the analysis, the server assesses the user's cognitive function risks and generates a personalized brain function training program as needed. This program is communicated to the user via a terminal and implemented. For example, if the user begins using a particular word frequently, a storytelling exercise to improve memory is suggested based on that.

[0789] Furthermore, this system can detect anomalies in real time and provide necessary interventions quickly. It also suggests activities that promote social interaction, thus contributing to the prevention of isolation. Examples of prompts include, "Extract information from changes in the vocabulary a 70-year-old man uses in his daily life," and "Analyze the trend of daily decline in physical fitness and suggest an optimal exercise program."

[0790] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0791] Step 1:

[0792] The device continuously collects user voice and motion data using built-in sensors. In this step, speech recognition technology transcribes conversations into text, and motion sensors record movement patterns. The input is the user's raw data, and the output is a data file encoded in digital format.

[0793] Step 2:

[0794] The device transmits the collected data to the server via Wi-Fi or a mobile network. The input here is the data file generated in step 1, and the output is data encrypted using a secure communication protocol.

[0795] Step 3:

[0796] The server analyzes the received data in a cloud environment using machine learning algorithms. Specifically, it performs frequency analysis of language use from speech data and evaluation of activity patterns from behavioral data. The input is the encrypted data sent in step 2, and the output is the result of the cognitive function evaluation.

[0797] Step 4:

[0798] The server generates a personalized brain function training program based on the analysis results. For example, if the frequency of use of a particular linguistic expression changes, it will suggest specific exercises to improve memory. The input is the evaluation results from step 3, and the output is the user-specific training program.

[0799] Step 5:

[0800] The server utilizes idle time to notify the terminal of the created training program. The terminal displays the notification to the user and supports the user in starting the program. The input is the training program from step 4, and the output is the program notification to the user.

[0801] Step 6:

[0802] The user performs activities according to the instructions of the training program displayed on the device. Completion data for each activity is entered into the device and sent back to the server. The input is the user's completion data, and the output is the feedback data sent to the server.

[0803] Step 7:

[0804] The server analyzes the feedback data and considers suggestions for improving the training program. It utilizes generative AI models as needed to develop new programming strategies. The input is the feedback data from step 6, and the output is the improved future training program.

[0805] 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.

[0806] This invention relates to a cognitive function analysis system incorporating an emotion engine, which provides comprehensive cognitive function assessment and support based on the user's daily life data. The system includes an information processing device, an emotion recognition module, a training program generation module, and user feedback and anomaly detection modules.

[0807] First, the device acquires user emotional and behavioral data. This includes an emotion engine that uses cameras and voice input sensors to recognize emotions from facial expressions and tone of voice. For example, if a user displays a smile or an angry expression, the emotion engine recognizes the change in real time. This data, along with other lifestyle data necessary for analyzing cognitive function, is sent to the server.

[0808] The server integrates and analyzes received lifestyle and emotional data. Using machine learning algorithms, it comprehensively evaluates the user's cognitive and emotional state and estimates their daily risk of dementia. For example, if a decrease in attention is detected when the user is under excessive stress, this is considered a risk factor.

[0809] Next, the server dynamically adjusts a brain function training program optimized for the user based on the recognized emotional state. This training program is designed to positively change the user's emotions and includes, for example, games that promote relaxation and art sessions that are expected to relieve stress.

[0810] The terminal monitors the program's progress and provides the user with continuous notifications and feedback. The user enters feedback on the training they've completed into the terminal, and this data is sent to the server for re-evaluation. This feedback loop allows the system to quickly respond to user changes and continuously optimize the program.

[0811] Furthermore, the device constantly monitors emotional changes through its emotion engine and reports to the server if a significant negative change is detected. Based on this information, the server can suggest to the user that they consider early medical intervention.

[0812] Thus, the present invention contributes to dementia prevention and improvement of quality of life by comprehensively monitoring the user's cognitive function from an emotional perspective and providing personalized support.

[0813] The following describes the processing flow.

[0814] Step 1:

[0815] The device uses a camera and voice input device to collect emotional data in real time from the user's facial expressions and tone of voice.

[0816] Step 2:

[0817] The device preprocesses the collected emotion data, performing noise reduction and data formatting. This preprocessing converts the data into a format suitable for analysis.

[0818] Step 3:

[0819] The device sends pre-processed emotional data along with other lifestyle data to the server. A secure protocol is used for data transmission.

[0820] Step 4:

[0821] The server analyzes the received data and assesses the user's cognitive and emotional state. Machine learning models are used to analyze the impact of stress and emotional fluctuations on cognitive function.

[0822] Step 5:

[0823] The server assesses the risk of dementia based on the analysis results. If a user frequently expresses negative emotions, they are judged to be at high risk.

[0824] Step 6:

[0825] The server generates brain function training programs adapted to the user's emotional state. Relaxation games and art sessions are selected to evoke positive emotions.

[0826] Step 7:

[0827] The server sends the generated training program to the terminal and notifies the user.

[0828] Step 8:

[0829] The device prompts the user to complete the training program and continuously monitors their progress.

[0830] Step 9:

[0831] Users follow notifications to complete the training program and input their progress and feedback into their device.

[0832] Step 10:

[0833] The device sends user feedback to the server, verifies whether the training program is appropriate for the user's condition, and makes adjustments as needed.

[0834] Step 11:

[0835] The device continuously monitors emotional changes through an emotion engine and sends an alert to the server if a significant negative change is detected.

[0836] Step 12:

[0837] The server analyzes the reported negative emotional changes and, if necessary, suggests medical intervention to the user to encourage early action.

[0838] (Example 2)

[0839] 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".

[0840] In modern society, cognitive decline and the risk of dementia are significant health issues. Especially with the aging population, there is a need for effective methods to monitor individual cognitive function and provide appropriate training and interventions. However, existing methods struggle to provide highly accurate support because they cannot comprehensively assess emotions and behaviors in real time.

[0841] 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.

[0842] In this invention, the server includes means for collecting emotional and behavioral data from information processing devices, means for analyzing cognitive functions based on the collected data, and means for evaluating risk based on the analysis results. This makes it possible to capture changes in the user's emotions and behavior in real time and provide personalized support accordingly.

[0843] An "information processing device" is a device that collects user emotional and behavioral data and transmits that data to a server via communication.

[0844] "Cognitive function" refers to the mental abilities that humans use to understand information, remember it, solve problems, and make judgments.

[0845] "Analysis" is the process of evaluating the user's cognitive and emotional state based on collected data, and extracting meaning using a specific algorithm.

[0846] "Risk" refers to an indicator that shows the possibility of cognitive decline or dementia in the user.

[0847] A "training program" is a set of activities and exercises designed to improve a user's cognitive function and regulate their emotional state.

[0848] "Progress monitoring" means tracking how the training program is being implemented by users and whether it is having any effect.

[0849] "Anomaly detection" refers to identifying deviations from a user's normal cognitive function and emotional state, and issuing alerts as needed.

[0850] "Intervention" refers to specific measures or activities taken to improve a user's health or cognitive state.

[0851] This invention comprises an information processing device, an emotion engine, a training program generation module, and feedback and anomaly detection modules. Specifically, the terminal uses a camera and voice input sensors to acquire user emotion and behavior data. This data includes an emotion engine that captures the user's facial expressions and voice tone in real time and identifies their emotional state.

[0852] Data collected by the device is transmitted to the server via a secure protocol. The server uses the received data to evaluate the user's cognitive function using a generative AI model. Machine learning algorithms integrate lifestyle data and emotional data to estimate the user's risk of dementia. Based on the analysis results, the server generates an optimal training program to improve cognitive function. This program may include activities that promote the conversion to positive emotions, such as relaxation-oriented music sessions and meditation exercises.

[0853] For example, if a user exhibits a higher-than-usual stress level, the server can generate a training program that includes an art session to reduce stress and notify the user via their device. Furthermore, based on user feedback, the server re-evaluates the effectiveness of the training and dynamically adjusts the program content.

[0854] An example of a prompt is, "Using the user's emotional data and daily life data, propose an optimal training program to assess and support cognitive function."

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] The device collects user emotion and behavior data. This process uses a camera and voice input sensor to capture the user's facial expressions and voice tone in real time. An emotion engine then analyzes this data to identify emotional states such as smiles or anger. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state.

[0858] Step 2:

[0859] The device packages the collected emotional and lifestyle data and sends it to the server using a secure protocol. The input is processed emotional data and other sensor data, and the output is a secure data transfer to the server. Encryption techniques are used in this step to maintain data integrity and confidentiality.

[0860] Step 3:

[0861] The server integrates received lifestyle and emotional data and uses a generative AI model to evaluate the user's cognitive function. Here, machine learning algorithms explore data correlations and estimate dementia risk, among other things. The input is the submitted dataset, and the output is the risk assessment results and analysis report.

[0862] Step 4:

[0863] The server generates a cognitive training program optimized for the user based on the analysis results. This training program includes activities aimed at stress reduction and relaxation. The input is the risk assessment results, and the output is the individual training program.

[0864] Step 5:

[0865] The terminal displays the generated training program to the user and monitors the program's progress. It records the user's progress as they perform the program and provides notifications as needed. The input is the generated program, and the output is user feedback and progress data.

[0866] Step 6:

[0867] After completing the training program, the user enters feedback based on their experience into a terminal. The terminal then sends this feedback back to the server, which is used for future evaluations and adjustments to the training program. The input is the user's feedback on the completed training, and the output is the transmission of the feedback data to the server.

[0868] Step 7:

[0869] The device continuously monitors the user's emotions using an emotion engine. If a significant negative change occurs, the device sends a notification to the server, which then suggests interventions as needed. The input is emotion monitoring data, and the output is an anomaly notification to the server.

[0870] (Application Example 2)

[0871] 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".

[0872] There is a challenge in detecting cognitive decline and emotional fluctuations in the elderly at an early stage and prompting appropriate intervention. In particular, there is a need to dynamically assess users' emotions and cognitive function in their daily living environments and provide individually adapted support.

[0873] 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.

[0874] In this invention, the server includes means for analyzing cognitive function based on input from an information processing device, means for analyzing emotional state and providing optimized relaxation or relaxation programs, and means for detecting sudden changes in emotion and notifying information. This makes it possible to comprehensively monitor the emotional and cognitive state of elderly people in their daily lives and provide personalized support quickly, thereby preventing cognitive decline and improving their quality of life.

[0875] An "information processing device" refers to hardware and software used to receive and analyze data.

[0876] "Means for analyzing cognitive function" refers to algorithms that evaluate the state of cognitive function based on user behavior and emotional data.

[0877] "Means of risk assessment" refers to the process of determining cognitive decline or stress levels based on analysis results.

[0878] "Means for generating personalized brain function training programs" refers to a system that designs optimized training content according to the user's cognitive function state.

[0879] "Means of tracking progress" refers to a system that records the history of training and activities performed by users and monitors their progress.

[0880] "Means of suggesting cognitive function promotion activities" refers to functions that recommend activities in the user's daily life to maintain and improve cognitive function.

[0881] "Means for analyzing emotional states" refers to a function that recognizes and analyzes emotions from the user's facial expressions and tone of voice.

[0882] "Means of providing optimized relaxation or relaxation programs" refers to a function that suggests appropriate activities to reduce stress based on the user's emotional state.

[0883] "Means of detecting sudden emotional changes and notifying information" refers to a function that recognizes significant emotional fluctuations in real time and transmits that information to relevant parties.

[0884] The system for realizing this invention primarily comprises an information processing terminal, a server, and user interface functions. First, the terminal acquires emotional and behavioral data from the user in real time. This data acquisition uses sensor devices such as a camera and microphone, and is equipped with an emotion engine that includes facial recognition and voice analysis.

[0885] Data acquired by the device is sent to the server. The server uses this data to apply machine learning algorithms to analyze the user's cognitive and emotional state. TensorFlow or similar technology platforms are used for this analysis. Based on the analysis results, a personalized brain function training program is generated for the user. This program aims to reduce stress in daily life and improve cognitive function, and includes gaming activities and relaxation exercises.

[0886] For example, if the system detects that a user tends to experience high levels of stress on weekends, it can suggest relaxing activities such as listening to calming music or light exercise. Furthermore, if a sudden change in mood is detected, relevant notifications are sent to family members or caregivers to ensure prompt support.

[0887] When a user considers activities to relieve stress, a prompt such as "Suggest activities that the user can do to relieve stress" can be used with a generative AI model.

[0888] Through this system, it is expected that users' daily lives will be comprehensively supported, and their cognitive functions will be maintained and improved.

[0889] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0890] Step 1:

[0891] The device uses a camera and microphone to acquire user emotion and behavior data. Camera images and audio data are used as input. The emotion engine analyzes facial expressions and tone using facial recognition algorithms and voice analysis technology, and outputs the user's emotional state. Specifically, if the user is smiling, the system recognizes this as "positive."

[0892] Step 2:

[0893] The acquired emotional and activity data is sent to the server in real time. The server receives this data as input and uses machine learning algorithms to analyze it, evaluate the user's cognitive function, and output daily risk information. Specifically, if decreased attention is observed while under high stress, this is evaluated as "at risk."

[0894] Step 3:

[0895] The server generates a brain function training program tailored to the user based on the analyzed results. This program receives the analysis results as input and suggests specific games or relaxation activities as output. The program is then sent to the user's device, and the program begins. For example, if relaxation is needed, a session listening to calming music might be suggested.

[0896] Step 4:

[0897] The user performs the suggested training or relaxation program on the device, and the device tracks their progress in real time. It receives user performance data as input and sends progress data to the server as feedback. Specifically, if the achievement rate exceeds a certain level, a "going well" message is displayed on the device.

[0898] Step 5:

[0899] The server evaluates the effectiveness of the training program based on user feedback data and readjusts the program as needed. In this case, the feedback data becomes the input, and the adjusted program is output. Specifically, if an activity is found to be ineffective, a new activity is suggested as an alternative.

[0900] Step 6:

[0901] The device constantly monitors the user's emotional changes via an emotion engine and reports to the server if a sudden negative change is detected. Monitoring data is used as input, and a notification message is output. Specifically, if negative emotions persist, an alert is sent to the caregiver.

[0902] Step 7:

[0903] When a user spontaneously uses a generative AI model to come up with activities, they can prompt the model with a message like, "Suggest activities that the user can do to relieve stress," and the AI ​​will output suggestions. Specific examples of suggestions from the AI ​​include "a short walk" and "recommendation for deep breathing."

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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."

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] The following is further disclosed regarding the embodiments described above.

[0926] (Claim 1)

[0927] A means of analyzing cognitive function based on input from an information processing device,

[0928] A means of evaluating risk based on the analysis results,

[0929] A means for generating an individualized brain function training program based on assessed risk,

[0930] A means of executing the generated program and tracking its progress,

[0931] Methods for proposing cognitive function promotion activities in daily life,

[0932] A system that includes means to detect abnormalities in cognitive function and prompt necessary interventions.

[0933] (Claim 2)

[0934] The system according to claim 1, further comprising means for dynamically updating individual risk assessments using data obtained on a daily basis.

[0935] (Claim 3)

[0936] The system according to claim 1, further comprising means for proposing activities that promote social interaction.

[0937] "Example 1"

[0938] (Claim 1)

[0939] A means for analyzing cognitive function based on data obtained from information processing means,

[0940] A means of dynamically evaluating risk based on analysis results,

[0941] A means for automatically generating individualized training programs based on assessed risks,

[0942] A means of notifying the user of the generated program via their terminal and prompting them to execute it,

[0943] A means of collecting the results of the executed program as feedback and incorporating them into the analysis,

[0944] Means of proposing ways to promote activities in daily life,

[0945] A system that includes means to detect abnormalities in cognitive function and prompt rapid intervention.

[0946] (Claim 2)

[0947] The system according to claim 1, further comprising means for dynamically updating individual risk assessments using data obtained from daily activities.

[0948] (Claim 3)

[0949] The system according to claim 1, further comprising means for proposing activities to promote social interaction.

[0950] "Application Example 1"

[0951] (Claim 1)

[0952] A means of analyzing cognitive function based on input from an information processing device,

[0953] A means of evaluating risk based on the analysis results,

[0954] A means for generating an individualized brain function training program based on assessed risk,

[0955] A means of executing the generated program and tracking its progress,

[0956] Methods for proposing cognitive function promotion activities in daily life,

[0957] A means to detect cognitive abnormalities and prompt necessary interventions,

[0958] A means of performing cognitive assessment using a process that includes motion recognition,

[0959] A system that includes means of utilizing computing resources to perform analysis in a cloud environment.

[0960] (Claim 2)

[0961] The system according to claim 1, further comprising means for dynamically updating individual risk assessments using data obtained on a daily basis.

[0962] (Claim 3)

[0963] The system according to claim 1, further comprising means for proposing activities that promote social interaction.

[0964] "Example 2 of combining an emotion engine"

[0965] (Claim 1)

[0966] Means for collecting emotional and behavioral data from information processing devices,

[0967] A means of analyzing cognitive function based on collected data,

[0968] A means of evaluating risk based on the analysis results,

[0969] A means for generating an individualized cognitive training program based on assessed risk,

[0970] A means to execute the generated program and monitor its progress,

[0971] Methods for proposing activities to promote cognitive function in daily life,

[0972] A system that includes means to detect abnormalities in cognitive function and prompt necessary interventions.

[0973] (Claim 2)

[0974] The system according to claim 1, further comprising means for dynamically updating individual risk assessments using data obtained on a daily basis.

[0975] (Claim 3)

[0976] The system according to claim 1, further comprising means for proposing activities that promote social interaction.

[0977] "Application example 2 when combining with an emotional engine"

[0978] (Claim 1)

[0979] A means of analyzing cognitive function based on input from an information processing device,

[0980] A means of evaluating risk based on the analysis results,

[0981] A means for generating an individualized brain function training program based on assessed risk,

[0982] A means of executing the generated program and tracking its progress,

[0983] Methods for proposing cognitive function promotion activities in daily life,

[0984] A means to detect cognitive abnormalities and prompt necessary interventions,

[0985] A means of analyzing emotional states and providing optimized relaxation and relaxation programs,

[0986] A system that includes means for detecting sudden changes in emotions and notifying information about them.

[0987] (Claim 2)

[0988] The system according to claim 1, further comprising means for dynamically updating individual risk assessments using data obtained on a daily basis.

[0989] (Claim 3)

[0990] The system according to claim 1, further comprising means for proposing activities that promote social interaction. [Explanation of Symbols]

[0991] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing cognitive function based on input from an information processing device, A means of evaluating risk based on the analysis results, A means for generating an individualized brain function training program based on assessed risk, A means of executing the generated program and tracking its progress, Methods for proposing cognitive function promotion activities in daily life, A system that includes means to detect abnormalities in cognitive function and prompt necessary interventions.

2. The system according to claim 1, further comprising means for dynamically updating individual risk assessments using data obtained on a daily basis.

3. The system according to claim 1, further comprising means for proposing activities that promote social interaction.

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