system

Abstract

The system according to this embodiment aims to analyze the user's daily behavior data and cognitive test results to determine the risk of dementia at an early stage and provide an appropriate cognitive training program. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, a provision unit, and a notification unit. The collection unit collects the user's daily behavior data and cognitive test results. The analysis unit analyzes the data collected by the collection unit. The determination unit determines the dementia risk based on the data analyzed by the analysis unit. The provision unit provides a customized cognitive training program based on the risk determined by the determination unit. The notification unit notifies the user of the progress of the training and advice provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the daily behavior data of users and the results of cognitive tests are not fully utilized effectively to determine the risk of dementia at an early stage and provide appropriate countermeasures.

[0005] The system according to the embodiment aims to analyze the daily behavior data of users and the results of cognitive tests, determine the risk of dementia at an early stage, and provide an appropriate cognitive training program.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, a provision unit, and a notification unit. The data collection unit collects the user's daily behavior data and cognitive test results. The analysis unit analyzes the data collected by the data collection unit. The determination unit determines the risk of dementia based on the data analyzed by the analysis unit. The provision unit provides a customized cognitive training program based on the risk determined by the determination unit. The notification unit notifies the user of the progress of the training and advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's daily behavior data and cognitive test results to determine the risk of dementia at an early stage and provide an appropriate cognitive training program. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dementia risk warning system according to an embodiment of the present invention is a system in which AI analyzes the behavioral patterns and cognitive functions of individual users and provides early warnings of the risk of dementia. This system provides a customized cognitive training program to help users maintain their cognitive abilities. For example, the dementia risk warning system collects the user's daily behavioral data and cognitive test results. This includes the user's travel history, daily activities, conversation content, and cognitive test results. This data is analyzed by the AI, and the user's cognitive state is evaluated. Next, the dementia risk warning system uses the AI ​​to determine the user's risk of dementia based on the analysis results. For example, if abnormalities are observed in the user's behavioral patterns or if cognitive test results decline, the AI ​​determines that the user is at high risk of dementia. This determination is notified to the user and their family in real time. Furthermore, the dementia risk warning system uses AI to provide a customized cognitive training program tailored to the user's condition. For example, this may include games to enhance memory or tasks to improve attention. These training programs are adjusted according to the user's progress to provide optimal training. The dementia risk warning system also uses AI to provide the user and their family with real-time updates on progress and advice. For example, if a user's cognitive function improves, the system notifies them and recommends further training. Conversely, if cognitive function declines, it notifies them to seek medical attention early. This allows the dementia risk warning system to improve the rate of early detection of dementia risk in users and support the maintenance of cognitive function. It also reduces the burden of care on families and improves the user's quality of life. For example, the rate of early detection of dementia risk improves by 40%, and 75% of participants can delay cognitive decline. Furthermore, it is expected to reduce the average amount of time spent on care by families by 30%. In this way, the dementia risk warning system can warn users of their dementia risk early and support the maintenance of their cognitive abilities.

[0029] The dementia risk warning system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, a provision unit, and a notification unit. The collection unit collects the user's daily behavior data and cognitive test results. The collection unit collects data such as the user's movement history, daily activities, conversation content, and cognitive test results. The collection unit collects the user's movement history using GPS data, for example. The collection unit detects the user's daily activities with sensors and records the activity content, for example. The collection unit records the user's conversation content and transcribes it into text, for example. The collection unit collects cognitive test results in digital format, for example. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using AI, for example, and evaluates the user's cognitive state. The analysis unit analyzes the data using machine learning algorithms, for example. The analysis unit analyzes conversation content using natural language processing technology, for example. The analysis unit analyzes activity content using image recognition technology, for example. The determination unit determines the dementia risk based on the data analyzed by the analysis unit. The assessment unit, for example, uses AI to determine the risk of dementia based on the analysis results. The assessment unit, for example, calculates a risk score and evaluates the risk of dementia. The assessment unit, for example, makes a judgment based on risk evaluation criteria. The assessment unit, for example, determines the level of risk based on the analysis results. The provision unit provides a customized cognitive training program based on the risk determined by the assessment unit. The provision unit, for example, uses AI to provide a training program tailored to the user's condition. The provision unit, for example, provides a game to enhance memory. The provision unit, for example, provides tasks to improve attention. The provision unit, for example, adjusts the training program according to the user's progress. The notification unit notifies the user of the progress and advice of the training provided by the provision unit. The notification unit, for example, uses AI to notify the user and their family of the progress and advice in real time. The notification unit, for example, notifies the user if their cognitive function is improving. The notification unit, for example, recommends further training. The notification unit, for example, notifies the user to seek medical attention early if their cognitive function is declining.As a result, the dementia risk warning system according to this embodiment can warn users of their dementia risk early and support the maintenance of their cognitive abilities.

[0030] The data collection unit collects data on the user's daily activities and cognitive test results. Specifically, it collects data such as the user's travel history, daily activities, conversation content, and cognitive test results. For example, it collects the user's travel history using GPS data. This allows the system to understand where the user frequently visits and whether there are changes in their travel patterns. Furthermore, the data collection unit detects the user's daily activities using sensors and records the activity content. For example, it collects data such as steps, heart rate, and sleep patterns using wearable devices such as smartwatches and fitness trackers. This allows the system to monitor changes in the user's physical activity level and lifestyle rhythm. The data collection unit also records and transcribes the user's conversations. This involves converting conversations into text data using speech recognition technology and analyzing the content and emotions of the conversations using natural language processing technology. In addition, the data collection unit collects cognitive test results in digital format. For example, it automatically collects the results of cognitive tests conducted using smartphones or tablets and stores them in a database. This allows the system to continuously track changes in the user's cognitive function. The data collection unit centrally manages this diverse data, making it accessible to the analysis and judgment units. This allows the data collection unit to provide comprehensive data on user behavior and cognitive states, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it uses AI to analyze the collected data and evaluate the user's cognitive state. For example, it uses machine learning algorithms to analyze the data. This allows it to detect changes in the user's behavioral patterns and cognitive functions, enabling early detection of abnormal patterns. Furthermore, the analysis unit uses natural language processing technology to analyze conversation content. This allows it to evaluate the fluency, consistency, and emotional changes of the user's conversation, enabling it to detect signs of cognitive decline. The analysis unit also uses image recognition technology to analyze activity content. For example, it can analyze videos of activities the user performs daily to evaluate changes in the frequency and quality of those activities. This allows it to grasp changes in the user's physical activity level and lifestyle, enabling it to detect signs of cognitive decline. Based on these analysis results, the analysis unit comprehensively evaluates the user's cognitive state and provides data to determine the level of risk. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessment and trend analysis. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The assessment unit determines the risk of dementia based on the data analyzed by the analysis unit. Specifically, it uses AI to determine the risk of dementia based on the analysis results. For example, it calculates a risk score and evaluates the risk of dementia. The risk score is calculated using statistical models and machine learning algorithms based on the user's behavioral data and cognitive test results. This allows for a quantitative evaluation of the user's risk of dementia. Furthermore, the assessment unit makes a judgment based on risk evaluation criteria. For example, if the risk score exceeds a certain threshold, it determines that the risk of dementia is high and provides information for taking appropriate measures. The assessment unit also determines the level of risk based on the analysis results. This allows for early warning of the user's risk of dementia and the implementation of appropriate measures. The assessment unit transmits these judgment results to the provision unit and notification unit to provide appropriate information to the user and their family. In this way, the assessment unit can accurately assess the user's risk of dementia and support the maintenance of cognitive abilities by providing early warnings.

[0033] The service provider offers a customized cognitive training program based on the risk assessed by the assessment unit. Specifically, it uses AI to provide a training program tailored to the user's condition. For example, it provides games to enhance memory, including puzzles and quizzes to train the user's memory. Furthermore, the service provider provides tasks to improve attention, including tasks and exercises to train the user's attention. The service provider also adjusts the training program according to the user's progress. For example, if the user completes a certain task, it provides the next level of task, continuously providing training tailored to the user's abilities. This allows the service provider to provide a customized training program that effectively improves the user's cognitive function. In addition, the service provider collects user feedback and evaluates the effectiveness of the training program. This allows the service provider to continuously improve the accuracy and effectiveness of the training program and support the improvement of the user's cognitive function.

[0034] The notification unit notifies users and their families of the progress and advice regarding the training provided by the delivery unit. Specifically, it uses AI to notify users and their families of progress and advice in real time. For example, if a user's cognitive function is improving, it will notify them of this. This allows users and their families to feel the effects of the training and maintain their motivation. Furthermore, the notification unit recommends further training. For example, if a user completes a particular training, it will notify them of the next recommended training. Also, if cognitive function is declining, the notification unit will notify users to seek medical attention early. This allows users and their families to receive appropriate medical care early. The notification unit sends these notifications via smartphones, tablets, email, etc., to ensure that users and their families receive the information reliably. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. In this way, the notification unit can provide users and their families with timely and appropriate information and support the maintenance and improvement of cognitive function.

[0035] The data collection unit can collect data such as the user's movement history, daily activities, conversation content, and cognitive test results. For example, the data collection unit can collect the user's movement history using GPS data. For example, the data collection unit can detect the user's daily activities using sensors and record the activity content. For example, the data collection unit can record the user's conversation content and transcribe it into text. For example, the data collection unit can collect the results of cognitive tests in digital format. By collecting diverse data on the user, it is possible to provide information necessary for evaluating their cognitive state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect the user's movement history using GPS data and input that data into AI for analysis.

[0036] The analysis unit can analyze the collected data and evaluate the user's cognitive state. For example, the analysis unit can use AI to analyze the collected data and evaluate the user's cognitive state. For example, the analysis unit can use machine learning algorithms to analyze the data. For example, the analysis unit can use natural language processing technology to analyze the content of conversations. For example, the analysis unit can use image recognition technology to analyze the content of activities. In this way, by analyzing the collected data, the user's cognitive state can be accurately evaluated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data and evaluate the cognitive state.

[0037] The judgment unit can determine the user's dementia risk based on the analysis results. The judgment unit can determine the dementia risk based on the analysis results, for example, using AI. The judgment unit can calculate a risk score and evaluate the dementia risk, for example. The judgment unit can make a judgment based on risk evaluation criteria, for example. The judgment unit can determine the level of risk based on the analysis results, for example. This makes early warning possible by determining the dementia risk based on the analysis results. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the analysis results into AI, and the AI ​​can calculate a risk score and evaluate the dementia risk.

[0038] The service provider can provide a customized cognitive training program tailored to the user's condition. For example, the service provider can use AI to provide a training program tailored to the user's condition. For example, the service provider can provide a game to enhance memory. For example, the service provider can provide tasks to improve attention. For example, the service provider can adjust the training program according to the user's progress. In this way, by providing a training program tailored to the user's condition, it is possible to support the maintenance of cognitive abilities. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data about the user's condition into the AI, and the AI ​​can generate an optimal training program.

[0039] The notification unit can notify users and their families of their progress and advice in real time. For example, the notification unit uses AI to notify users and their families of their progress and advice in real time. For example, the notification unit will notify users if their cognitive function is improving. For example, the notification unit will recommend further training. For example, the notification unit will notify users if their cognitive function is declining, advising them to seek medical attention early. By notifying users of their progress and advice in real time, the notification unit can provide them with appropriate information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on progress and advice into the AI, which can then generate the most appropriate notification content.

[0040] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the user's frequently performed behavioral patterns in the past. For example, the data collection unit can analyze the user's past behavioral data to identify tendencies for increased activity during specific time periods and collect data during those times. For example, the data collection unit can analyze the user's past behavioral data and collect data at the time when changes in behavior are observed. This allows for the selection of the optimal data collection method by analyzing past behavioral data, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter behavioral data based on the user's current health status and living environment. For example, if the user's health status is good, the data collection unit will collect detailed behavioral data. If the user's living environment changes, the data collection unit will filter the data to be collected accordingly. If the user's health status deteriorates, the data collection unit will limit the scope of data to be collected. This allows for appropriate data collection by filtering the data according to the user's health status and living environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's health status and living environment into a generating AI, which can then filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific location, the data collection unit will prioritize collecting behavioral data related to that location. For example, if the user is on the move, the data collection unit will prioritize collecting behavioral data related to the move. For example, if the user is at home, the data collection unit will prioritize collecting behavioral data at home. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, if a user is very active on social media, the data collection unit can collect data related to that activity. For example, if a user is inactive on social media, the data collection unit can collect data related to that activity. For example, the data collection unit can analyze a user's social media activity and collect data related to a specific topic. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis according to the importance.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. For example, the analysis unit applies a cognitive analysis algorithm to cognitive test results. For example, the analysis unit applies a natural language processing algorithm to conversation content. By applying the appropriate analysis algorithm according to the data category, accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, which can then apply an appropriate analysis algorithm according to the category.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis in stages according to the data collection period. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI, and the generating AI can determine the priority of analysis based on the collection period.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may adjust the order of analysis step by step according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis based on the relevance.

[0048] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, the judgment unit makes a judgment by considering the interrelationships between behavioral data and cognitive test results. For example, the judgment unit makes a judgment by considering the interrelationships between conversation content and behavioral data. For example, the judgment unit makes a judgment by considering the interrelationships between cognitive test results and conversation content. This improves the accuracy of the judgment by considering the interrelationships between data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the interrelationships between data to a generating AI, and the generating AI can make a judgment by considering the interrelationships.

[0049] The determination unit can make a determination while considering the user's attribute information. For example, the determination unit can make a determination while considering the user's age. For example, the determination unit can make a determination while considering the user's gender. For example, the determination unit can make a determination while considering the user's occupation. This makes it possible to make a more individualized determination by considering the user's attribute information. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the user's attribute information into a generating AI, and the generating AI can make a determination while considering the attribute information.

[0050] The determination unit can make a determination while considering the geographical distribution of the data. For example, the determination unit can make a determination based on the user's place of residence. For example, the determination unit can make a determination based on the user's travel history. For example, the determination unit can make a determination based on the user's activity range. By considering the geographical distribution of the data, it becomes possible to make determinations that are appropriate to regional characteristics. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the geographical distribution of the data into a generating AI, and the generating AI can make a determination while considering the geographical distribution.

[0051] The judgment unit can improve the accuracy of its judgment by referring to relevant literature during the judgment process. For example, the judgment unit may refer to the latest research papers when making a judgment. For example, the judgment unit may refer to past research results when making a judgment. For example, the judgment unit may refer to relevant literature comprehensively when making a judgment. As a result, the accuracy of the judgment is improved by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit may input relevant literature into a generating AI, and the generating AI may refer to the literature and make a judgment.

[0052] The service provider can select the optimal program by referring to the user's past training history at the time of delivery. For example, the service provider can analyze the effectiveness of training programs the user has previously performed and select the optimal program. For example, if a particular training program was highly effective based on the user's past training history, the service provider can prioritize providing that program. For example, the service provider can refer to the user's past training history and adjust the training program according to the progress. In this way, by referring to past training history, the service provider can provide the user with the optimal training program. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past training history into a generating AI, and the generating AI can select the optimal program.

[0053] The service provider can customize the training program based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can provide a short and effective training program. For example, if the user is relaxed, the service provider can provide a training program that can be performed in a relaxed state. For example, the service provider can adjust the content and frequency of the training program according to the user's lifestyle. By customizing the training program according to the user's lifestyle, more effective training can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data about the user's lifestyle into a generating AI, and the generating AI can customize the training program.

[0054] The service provider can select the optimal training program at the time of delivery, taking into account the user's geographical location information. For example, if the user is at home, the service provider will provide a training program that can be done at home. For example, if the user is out, the service provider will provide a training program that can be done while out. For example, the service provider will select the optimal training program based on the user's geographical location information. This enables effective training by providing the optimal training program based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can select the optimal training program.

[0055] The service provider can analyze the user's social media activity and propose a training program at the time of delivery. For example, if the user is very active on social media, the service provider will provide a training program related to that activity. For example, if the user is inactive on social media, the service provider will provide a training program related to that activity. For example, the service provider can analyze the user's social media activity and provide a training program related to a specific topic. In this way, by analyzing the user's social media activity, it is possible to provide a relevant training program. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can propose a relevant training program.

[0056] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has preferred to receive in the past. For example, the notification unit may send notifications at specific time periods based on the user's past notification history. For example, the notification unit may refer to the user's past notification history and select the optimal notification method. This allows the notification unit to provide the user with the most suitable notification method by referring to their past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past notification history into a generating AI, which can then select the optimal notification method.

[0057] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit will provide the most suitable notification method for the smartphone. For example, if the user is using a tablet, the notification unit will provide the most suitable notification method for the tablet. For example, if the user is using a smartwatch, the notification unit will provide the most suitable notification method for the smartwatch. This enables effective notifications by providing the optimal notification method based on the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, which can then select the most suitable notification method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can adjust the frequency of data collection based on the user's health condition when collecting user behavioral data. For example, if the user is in good health, the collection frequency can be increased to collect more detailed data. Conversely, if the user's health is deteriorating, the collection frequency can be reduced to lessen the user's burden. This enables flexible data collection tailored to the user's health condition.

[0060] The analysis unit can improve the accuracy of user behavior data by considering the user's past behavior patterns. For example, by analyzing current behavior data based on the user's frequently performed past behavior patterns, more accurate analysis results can be obtained. This enables analysis that takes into account the user's past behavior patterns.

[0061] The assessment unit can improve the accuracy of its assessment of dementia risk by considering the user's living environment when determining the risk based on the user's behavioral data. For example, since the behavioral patterns of users living in urban areas differ from those of users living in rural areas, assessment criteria tailored to each living environment can be applied. This enables assessment that takes the user's living environment into account.

[0062] The service provider can customize the content of a user's cognitive training program by taking into account the user's hobbies and interests. For example, if a user is interested in music, a training program incorporating music can be provided. This makes it possible to provide training programs tailored to the user's hobbies and interests.

[0063] The notification unit can adjust the notification method to suit the user's communication style when notifying them of progress or advice. For example, if the user prefers email, notifications can be sent via email; if the user prefers phone calls, notifications can be sent via phone. This allows for notifications tailored to the user's communication style.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects data on the user's daily activities and cognitive test results. For example, the data collection unit collects data such as the user's movement history, daily activities, conversation content, and cognitive test results. Specifically, it collects movement history using GPS data, detects and records daily activities with sensors, records conversation content and transcribes it into text, and collects cognitive test results in digital format. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the data and evaluate the user's cognitive state. Specifically, it uses machine learning algorithms to analyze the data, natural language processing technology to analyze the conversation content, and image recognition technology to analyze the activity content. Step 3: The judgment unit determines the dementia risk based on the data analyzed by the analysis unit. For example, it uses AI to determine the dementia risk based on the analysis results, calculates a risk score, and evaluates it. Specifically, it makes a judgment based on risk evaluation criteria and determines the level of risk based on the analysis results. Step 4: The provisioning unit provides a customized cognitive training program based on the risk determined by the assessment unit. For example, it uses AI to provide a training program tailored to the user's condition, offering games to enhance memory and tasks to improve attention. Specifically, it adjusts the training program according to the user's progress. Step 5: The notification unit notifies users of the progress and advice provided by the delivery unit regarding the training. For example, it uses AI to notify users and their families of their progress and advice in real time. Specifically, it notifies users if their cognitive function is improving or recommends further training, and if their cognitive function is declining, it recommends seeking medical attention early.

[0066] (Example of form 2) The dementia risk warning system according to an embodiment of the present invention is a system in which AI analyzes the behavioral patterns and cognitive functions of individual users and provides early warnings of the risk of dementia. This system provides a customized cognitive training program to help users maintain their cognitive abilities. For example, the dementia risk warning system collects the user's daily behavioral data and cognitive test results. This includes the user's travel history, daily activities, conversation content, and cognitive test results. This data is analyzed by the AI, and the user's cognitive state is evaluated. Next, the dementia risk warning system uses the AI ​​to determine the user's risk of dementia based on the analysis results. For example, if abnormalities are observed in the user's behavioral patterns or if cognitive test results decline, the AI ​​determines that the user is at high risk of dementia. This determination is notified to the user and their family in real time. Furthermore, the dementia risk warning system uses AI to provide a customized cognitive training program tailored to the user's condition. For example, this may include games to enhance memory or tasks to improve attention. These training programs are adjusted according to the user's progress to provide optimal training. The dementia risk warning system also uses AI to provide the user and their family with real-time updates on progress and advice. For example, if a user's cognitive function improves, the system notifies them and recommends further training. Conversely, if cognitive function declines, it notifies them to seek medical attention early. This allows the dementia risk warning system to improve the rate of early detection of dementia risk in users and support the maintenance of cognitive function. It also reduces the burden of care on families and improves the user's quality of life. For example, the rate of early detection of dementia risk improves by 40%, and 75% of participants can delay cognitive decline. Furthermore, it is expected to reduce the average amount of time spent on care by families by 30%. In this way, the dementia risk warning system can warn users of their dementia risk early and support the maintenance of their cognitive abilities.

[0067] The dementia risk warning system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, a provision unit, and a notification unit. The collection unit collects the user's daily behavior data and cognitive test results. The collection unit collects data such as the user's movement history, daily activities, conversation content, and cognitive test results. The collection unit collects the user's movement history using GPS data, for example. The collection unit detects the user's daily activities with sensors and records the activity content, for example. The collection unit records the user's conversation content and transcribes it into text, for example. The collection unit collects cognitive test results in digital format, for example. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using AI, for example, and evaluates the user's cognitive state. The analysis unit analyzes the data using machine learning algorithms, for example. The analysis unit analyzes conversation content using natural language processing technology, for example. The analysis unit analyzes activity content using image recognition technology, for example. The determination unit determines the dementia risk based on the data analyzed by the analysis unit. The assessment unit, for example, uses AI to determine the risk of dementia based on the analysis results. The assessment unit, for example, calculates a risk score and evaluates the risk of dementia. The assessment unit, for example, makes a judgment based on risk evaluation criteria. The assessment unit, for example, determines the level of risk based on the analysis results. The provision unit provides a customized cognitive training program based on the risk determined by the assessment unit. The provision unit, for example, uses AI to provide a training program tailored to the user's condition. The provision unit, for example, provides a game to enhance memory. The provision unit, for example, provides tasks to improve attention. The provision unit, for example, adjusts the training program according to the user's progress. The notification unit notifies the user of the progress and advice of the training provided by the provision unit. The notification unit, for example, uses AI to notify the user and their family of the progress and advice in real time. The notification unit, for example, notifies the user if their cognitive function is improving. The notification unit, for example, recommends further training. The notification unit, for example, notifies the user to seek medical attention early if their cognitive function is declining.As a result, the dementia risk warning system according to this embodiment can warn users of their dementia risk early and support the maintenance of their cognitive abilities.

[0068] The data collection unit collects data on the user's daily activities and cognitive test results. Specifically, it collects data such as the user's travel history, daily activities, conversation content, and cognitive test results. For example, it collects the user's travel history using GPS data. This allows the system to understand where the user frequently visits and whether there are changes in their travel patterns. Furthermore, the data collection unit detects the user's daily activities using sensors and records the activity content. For example, it collects data such as steps, heart rate, and sleep patterns using wearable devices such as smartwatches and fitness trackers. This allows the system to monitor changes in the user's physical activity level and lifestyle rhythm. The data collection unit also records and transcribes the user's conversations. This involves converting conversations into text data using speech recognition technology and analyzing the content and emotions of the conversations using natural language processing technology. In addition, the data collection unit collects cognitive test results in digital format. For example, it automatically collects the results of cognitive tests conducted using smartphones or tablets and stores them in a database. This allows the system to continuously track changes in the user's cognitive function. The data collection unit centrally manages this diverse data, making it accessible to the analysis and judgment units. This allows the data collection unit to provide comprehensive data on user behavior and cognitive states, improving the overall accuracy and reliability of the system.

[0069] The analysis unit analyzes the data collected by the data collection unit. Specifically, it uses AI to analyze the collected data and evaluate the user's cognitive state. For example, it uses machine learning algorithms to analyze the data. This allows it to detect changes in the user's behavioral patterns and cognitive functions, enabling early detection of abnormal patterns. Furthermore, the analysis unit uses natural language processing technology to analyze conversation content. This allows it to evaluate the fluency, consistency, and emotional changes of the user's conversation, enabling it to detect signs of cognitive decline. The analysis unit also uses image recognition technology to analyze activity content. For example, it can analyze videos of activities the user performs daily to evaluate changes in the frequency and quality of those activities. This allows it to grasp changes in the user's physical activity level and lifestyle, enabling it to detect signs of cognitive decline. Based on these analysis results, the analysis unit comprehensively evaluates the user's cognitive state and provides data to determine the level of risk. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessment and trend analysis. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0070] The assessment unit determines the risk of dementia based on the data analyzed by the analysis unit. Specifically, it uses AI to determine the risk of dementia based on the analysis results. For example, it calculates a risk score and evaluates the risk of dementia. The risk score is calculated using statistical models and machine learning algorithms based on the user's behavioral data and cognitive test results. This allows for a quantitative evaluation of the user's risk of dementia. Furthermore, the assessment unit makes a judgment based on risk evaluation criteria. For example, if the risk score exceeds a certain threshold, it determines that the risk of dementia is high and provides information for taking appropriate measures. The assessment unit also determines the level of risk based on the analysis results. This allows for early warning of the user's risk of dementia and the implementation of appropriate measures. The assessment unit transmits these judgment results to the provision unit and notification unit to provide appropriate information to the user and their family. In this way, the assessment unit can accurately assess the user's risk of dementia and support the maintenance of cognitive abilities by providing early warnings.

[0071] The service provider offers a customized cognitive training program based on the risk assessed by the assessment unit. Specifically, it uses AI to provide a training program tailored to the user's condition. For example, it provides games to enhance memory, including puzzles and quizzes to train the user's memory. Furthermore, the service provider provides tasks to improve attention, including tasks and exercises to train the user's attention. The service provider also adjusts the training program according to the user's progress. For example, if the user completes a certain task, it provides the next level of task, continuously providing training tailored to the user's abilities. This allows the service provider to provide a customized training program that effectively improves the user's cognitive function. In addition, the service provider collects user feedback and evaluates the effectiveness of the training program. This allows the service provider to continuously improve the accuracy and effectiveness of the training program and support the improvement of the user's cognitive function.

[0072] The notification unit notifies users and their families of the progress and advice regarding the training provided by the delivery unit. Specifically, it uses AI to notify users and their families of progress and advice in real time. For example, if a user's cognitive function is improving, it will notify them of this. This allows users and their families to feel the effects of the training and maintain their motivation. Furthermore, the notification unit recommends further training. For example, if a user completes a particular training, it will notify them of the next recommended training. Also, if cognitive function is declining, the notification unit will notify users to seek medical attention early. This allows users and their families to receive appropriate medical care early. The notification unit sends these notifications via smartphones, tablets, email, etc., to ensure that users and their families receive the information reliably. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. In this way, the notification unit can provide users and their families with timely and appropriate information and support the maintenance and improvement of cognitive function.

[0073] The data collection unit can collect data such as the user's movement history, daily activities, conversation content, and cognitive test results. For example, the data collection unit can collect the user's movement history using GPS data. For example, the data collection unit can detect the user's daily activities using sensors and record the activity content. For example, the data collection unit can record the user's conversation content and transcribe it into text. For example, the data collection unit can collect the results of cognitive tests in digital format. By collecting diverse data on the user, it is possible to provide information necessary for evaluating their cognitive state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect the user's movement history using GPS data and input that data into AI for analysis.

[0074] The analysis unit can analyze the collected data and evaluate the user's cognitive state. For example, the analysis unit can use AI to analyze the collected data and evaluate the user's cognitive state. For example, the analysis unit can use machine learning algorithms to analyze the data. For example, the analysis unit can use natural language processing technology to analyze the content of conversations. For example, the analysis unit can use image recognition technology to analyze the content of activities. In this way, by analyzing the collected data, the user's cognitive state can be accurately evaluated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data and evaluate the cognitive state.

[0075] The judgment unit can determine the user's dementia risk based on the analysis results. The judgment unit can determine the dementia risk based on the analysis results, for example, using AI. The judgment unit can calculate a risk score and evaluate the dementia risk, for example. The judgment unit can make a judgment based on risk evaluation criteria, for example. The judgment unit can determine the level of risk based on the analysis results, for example. This makes early warning possible by determining the dementia risk based on the analysis results. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the analysis results into AI, and the AI ​​can calculate a risk score and evaluate the dementia risk.

[0076] The service provider can provide a customized cognitive training program tailored to the user's condition. For example, the service provider can use AI to provide a training program tailored to the user's condition. For example, the service provider can provide a game to enhance memory. For example, the service provider can provide tasks to improve attention. For example, the service provider can adjust the training program according to the user's progress. In this way, by providing a training program tailored to the user's condition, it is possible to support the maintenance of cognitive abilities. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data about the user's condition into the AI, and the AI ​​can generate an optimal training program.

[0077] The notification unit can notify users and their families of their progress and advice in real time. For example, the notification unit uses AI to notify users and their families of their progress and advice in real time. For example, the notification unit will notify users if their cognitive function is improving. For example, the notification unit will recommend further training. For example, the notification unit will notify users if their cognitive function is declining, advising them to seek medical attention early. By notifying users of their progress and advice in real time, the notification unit can provide them with appropriate information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on progress and advice into the AI, which can then generate the most appropriate notification content.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can advance the collection timing to collect detailed behavioral data. For example, if the user is in a hurry, the data collection unit can adjust the collection timing to collect data when the user's behavior has calmed down. By adjusting the collection timing according to the user's emotions, more accurate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0079] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the user's frequently performed behavioral patterns in the past. For example, the data collection unit can analyze the user's past behavioral data to identify tendencies for increased activity during specific time periods and collect data during those times. For example, the data collection unit can analyze the user's past behavioral data and collect data at the time when changes in behavior are observed. This allows for the selection of the optimal data collection method by analyzing past behavioral data, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI, which can then select the optimal data collection method.

[0080] The data collection unit can filter behavioral data based on the user's current health status and living environment. For example, if the user's health status is good, the data collection unit will collect detailed behavioral data. If the user's living environment changes, the data collection unit will filter the data to be collected accordingly. If the user's health status deteriorates, the data collection unit will limit the scope of data to be collected. This allows for appropriate data collection by filtering the data according to the user's health status and living environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's health status and living environment into a generating AI, which can then filter the data.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related behavioral data. For example, if the user is relaxed, the data collection unit will prioritize collecting relaxation-related behavioral data. For example, if the user is in a hurry, the data collection unit will prioritize collecting behavioral data related to the hurried situation. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of data.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific location, the data collection unit will prioritize collecting behavioral data related to that location. For example, if the user is on the move, the data collection unit will prioritize collecting behavioral data related to the move. For example, if the user is at home, the data collection unit will prioritize collecting behavioral data at home. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, if a user is very active on social media, the data collection unit can collect data related to that activity. For example, if a user is inactive on social media, the data collection unit can collect data related to that activity. For example, the data collection unit can analyze a user's social media activity and collect data related to a specific topic. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis according to the importance.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. For example, the analysis unit applies a cognitive analysis algorithm to cognitive test results. For example, the analysis unit applies a natural language processing algorithm to conversation content. By applying the appropriate analysis algorithm according to the data category, accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, which can then apply an appropriate analysis algorithm according to the category.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is excited, the analysis unit will perform an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis in stages according to the data collection period. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI, and the generating AI can determine the priority of analysis based on the collection period.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may adjust the order of analysis step by step according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis based on the relevance.

[0090] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is tense, the judgment unit can relax the judgment criteria to reduce the user's burden. For example, if the user is relaxed, the judgment unit can make the judgment criteria stricter to perform a detailed evaluation. For example, if the user is in a hurry, the judgment unit can simplify the judgment criteria to perform a quick judgment. This allows for more appropriate judgments by adjusting the judgment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input user emotion data into a generative AI, which can estimate emotions and adjust the judgment criteria.

[0091] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, the judgment unit makes a judgment by considering the interrelationships between behavioral data and cognitive test results. For example, the judgment unit makes a judgment by considering the interrelationships between conversation content and behavioral data. For example, the judgment unit makes a judgment by considering the interrelationships between cognitive test results and conversation content. This improves the accuracy of the judgment by considering the interrelationships between data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the interrelationships between data to a generating AI, and the generating AI can make a judgment by considering the interrelationships.

[0092] The determination unit can make a determination while considering the user's attribute information. For example, the determination unit can make a determination while considering the user's age. For example, the determination unit can make a determination while considering the user's gender. For example, the determination unit can make a determination while considering the user's occupation. This makes it possible to make a more individualized determination by considering the user's attribute information. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the user's attribute information into a generating AI, and the generating AI can make a determination while considering the attribute information.

[0093] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is nervous, the judgment unit may display important results first to reduce the user's anxiety. For example, if the user is relaxed, the judgment unit may display detailed results sequentially. For example, if the user is in a hurry, the judgment unit may display concise results first. By adjusting the order in which results are displayed according to the user's emotions, it becomes possible to display results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the user's emotion data into the generative AI, which can estimate the emotions and adjust the order in which results are displayed.

[0094] The determination unit can make a determination while considering the geographical distribution of the data. For example, the determination unit can make a determination based on the user's place of residence. For example, the determination unit can make a determination based on the user's travel history. For example, the determination unit can make a determination based on the user's activity range. By considering the geographical distribution of the data, it becomes possible to make determinations that are appropriate to regional characteristics. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the geographical distribution of the data into a generating AI, and the generating AI can make a determination while considering the geographical distribution.

[0095] The judgment unit can improve the accuracy of its judgment by referring to relevant literature during the judgment process. For example, the judgment unit may refer to the latest research papers when making a judgment. For example, the judgment unit may refer to past research results when making a judgment. For example, the judgment unit may refer to relevant literature comprehensively when making a judgment. As a result, the accuracy of the judgment is improved by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit may input relevant literature into a generating AI, and the generating AI may refer to the literature and make a judgment.

[0096] The service provider can estimate the user's emotions and adjust the content of the training program based on the estimated emotions. For example, if the user is relaxed, the service provider will provide a training program that can be performed in a relaxed state. For example, if the user is stressed, the service provider will provide a training program that helps reduce stress. For example, if the user is excited, the service provider will provide a training program to calm the excitement. By adjusting the content of the training program according to the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can estimate the emotions and adjust the content of the training program.

[0097] The service provider can select the optimal program by referring to the user's past training history at the time of delivery. For example, the service provider can analyze the effectiveness of training programs the user has previously performed and select the optimal program. For example, if a particular training program was highly effective based on the user's past training history, the service provider can prioritize providing that program. For example, the service provider can refer to the user's past training history and adjust the training program according to the progress. In this way, by referring to past training history, the service provider can provide the user with the optimal training program. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past training history into a generating AI, and the generating AI can select the optimal program.

[0098] The service provider can customize the training program based on the user's current lifestyle at the time of delivery. For example, if the user is busy, the service provider can provide a short and effective training program. For example, if the user is relaxed, the service provider can provide a training program that can be performed in a relaxed state. For example, the service provider can adjust the content and frequency of the training program according to the user's lifestyle. By customizing the training program according to the user's lifestyle, more effective training can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data about the user's lifestyle into a generating AI, and the generating AI can customize the training program.

[0099] The service provider can estimate the user's emotions and determine the priority of training programs based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing training programs that help reduce stress. For example, if the user is relaxed, the service provider will prioritize providing training programs that can be performed in a relaxed state. For example, if the user is excited, the service provider will prioritize providing training programs that help calm excitement. By prioritizing training programs according to the user's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of training programs.

[0100] The service provider can select the optimal training program at the time of delivery, taking into account the user's geographical location information. For example, if the user is at home, the service provider will provide a training program that can be done at home. For example, if the user is out, the service provider will provide a training program that can be done while out. For example, the service provider will select the optimal training program based on the user's geographical location information. This enables effective training by providing the optimal training program based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can select the optimal training program.

[0101] The service provider can analyze the user's social media activity and propose a training program at the time of delivery. For example, if the user is very active on social media, the service provider will provide a training program related to that activity. For example, if the user is inactive on social media, the service provider will provide a training program related to that activity. For example, the service provider can analyze the user's social media activity and provide a training program related to a specific topic. In this way, by analyzing the user's social media activity, it is possible to provide a relevant training program. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can propose a relevant training program.

[0102] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is tense, the notification unit will provide a calm notification. If the user is relaxed, the notification unit will provide a detailed notification. If the user is in a hurry, the notification unit will provide a concise notification. By adjusting the content of the notification according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the content of the notification.

[0103] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has preferred to receive in the past. For example, the notification unit may send notifications at specific time periods based on the user's past notification history. For example, the notification unit may refer to the user's past notification history and select the optimal notification method. This allows the notification unit to provide the user with the most suitable notification method by referring to their past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past notification history into a generating AI, which can then select the optimal notification method.

[0104] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize notifications that help reduce stress. If the user is relaxed, the notification unit will prioritize notifications that can be received in a relaxed state. If the user is excited, the notification unit will prioritize notifications that help calm the excitement. By determining notification priorities according to the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotions and determine notification priorities.

[0105] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit will provide the most suitable notification method for the smartphone. For example, if the user is using a tablet, the notification unit will provide the most suitable notification method for the tablet. For example, if the user is using a smartwatch, the notification unit will provide the most suitable notification method for the smartwatch. This enables effective notifications by providing the optimal notification method based on the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, which can then select the most suitable notification method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can adjust the frequency of data collection based on the user's health condition when collecting user behavioral data. For example, if the user is in good health, the collection frequency can be increased to collect more detailed data. Conversely, if the user's health is deteriorating, the collection frequency can be reduced to lessen the user's burden. This enables flexible data collection tailored to the user's health condition.

[0108] The analysis unit can improve the accuracy of user behavior data by considering the user's past behavior patterns. For example, by analyzing current behavior data based on the user's frequently performed past behavior patterns, more accurate analysis results can be obtained. This enables analysis that takes into account the user's past behavior patterns.

[0109] The assessment unit can improve the accuracy of its assessment of dementia risk by considering the user's living environment when determining the risk based on the user's behavioral data. For example, since the behavioral patterns of users living in urban areas differ from those of users living in rural areas, assessment criteria tailored to each living environment can be applied. This enables assessment that takes the user's living environment into account.

[0110] The service provider can customize the content of a user's cognitive training program by taking into account the user's hobbies and interests. For example, if a user is interested in music, a training program incorporating music can be provided. This makes it possible to provide training programs tailored to the user's hobbies and interests.

[0111] The notification unit can adjust the notification method to suit the user's communication style when notifying them of progress or advice. For example, if the user prefers email, notifications can be sent via email; if the user prefers phone calls, notifications can be sent via phone. This allows for notifications tailored to the user's communication style.

[0112] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is feeling stressed, a data collection method that helps reduce stress can be adopted. This makes it possible to collect data in a way that is tailored to the user's emotions.

[0113] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is relaxed, detailed analysis results can be displayed; if the user is in a hurry, concise analysis results focusing on the key points can be displayed. This makes it possible to display analysis results that are tailored to the user's emotions.

[0114] The judgment unit can estimate the user's emotions and adjust the notification method of the judgment result based on the estimated user emotions. For example, if the user is tense, a calm notification can be sent, and if the user is relaxed, a more detailed notification can be sent. This makes it possible to send notifications that are tailored to the user's emotions.

[0115] The service provider can estimate the user's emotions and adjust the content of the training program based on those emotions. For example, if the user is feeling stressed, it can provide a training program that helps reduce stress, and if the user is relaxed, it can provide a training program that can be performed in a relaxed state. This makes it possible to provide training programs that are tailored to the user's emotions.

[0116] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is busy, the notification can be delayed until the user has calmed down. This allows for notifications to be sent at an appropriate time according to the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects data on the user's daily activities and cognitive test results. For example, the data collection unit collects data such as the user's movement history, daily activities, conversation content, and cognitive test results. Specifically, it collects movement history using GPS data, detects and records daily activities with sensors, records conversation content and transcribes it into text, and collects cognitive test results in digital format. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the data and evaluate the user's cognitive state. Specifically, it uses machine learning algorithms to analyze the data, natural language processing technology to analyze the conversation content, and image recognition technology to analyze the activity content. Step 3: The judgment unit determines the dementia risk based on the data analyzed by the analysis unit. For example, it uses AI to determine the dementia risk based on the analysis results, calculates a risk score, and evaluates it. Specifically, it makes a judgment based on risk evaluation criteria and determines the level of risk based on the analysis results. Step 4: The provisioning unit provides a customized cognitive training program based on the risk determined by the assessment unit. For example, it uses AI to provide a training program tailored to the user's condition, offering games to enhance memory and tasks to improve attention. Specifically, it adjusts the training program according to the user's progress. Step 5: The notification unit notifies users of the progress and advice provided by the delivery unit regarding the training. For example, it uses AI to notify users and their families of their progress and advice in real time. Specifically, it notifies users if their cognitive function is improving or recommends further training, and if their cognitive function is declining, it recommends seeking medical attention early.

[0119] 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.

[0120] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, provision unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on the user's daily activities using the sensors and GPS data of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12 and determines the risk of dementia based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides a customized cognitive training program. The notification unit is implemented in the control unit 46A of the smart device 14 and notifies the user and their family of their progress and advice in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0126] 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.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0128] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] 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.

[0130] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, provision unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on the user's daily activities using the sensors and GPS data of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12 and determines the risk of dementia based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides a customized cognitive training program. The notification unit is implemented in the control unit 46A of the smart glasses 214 and notifies the user and their family of their progress and advice in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0142] 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.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0144] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] 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.

[0146] 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.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, provision unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on the user's daily activities using sensors and GPS data from the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12 and determines the risk of dementia based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides a customized cognitive training program. The notification unit is implemented in the control unit 46A of the headset terminal 314 and notifies the user and their family of their progress and advice in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0158] 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.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0160] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] 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.

[0162] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] 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.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, provision unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on the user's daily activities using the robot 414's sensors and GPS data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The determination unit is implemented in the identification processing unit 290 of the data processing unit 12 and determines the risk of dementia based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides a customized cognitive training program. The notification unit is implemented in the control unit 46A of the robot 414 and notifies the user and their family of their progress and advice in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] 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.

[0173] Figure 9 shows the 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.

[0174] 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.

[0175] 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.

[0176] 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, and motorcycles, 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 based, for example, 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.

[0177] 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."

[0178] 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.

[0179] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] 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 other things 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.

[0189] 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.

[0190] (Note 1) A data collection unit that collects users' daily behavioral data and cognitive test results, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines the risk of dementia based on the data analyzed by the aforementioned analysis unit, A providing unit that provides a customized cognitive training program based on the risk determined by the aforementioned determination unit, The system includes a notification unit that notifies the user of the progress and advice regarding the training provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as the user's movement history, daily activities, conversation content, and cognitive test results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to evaluate the user's cognitive state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, The user's risk of dementia is determined based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide a customized cognitive training program tailored to the user's condition. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, The system provides real-time updates and advice to the user and their family. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current health status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When making a judgment, the accuracy of the judgment is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a decision, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When making a decision, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, When making a judgment, we refer to relevant literature to improve the accuracy of the judgment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the content of the training program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the most suitable program by referring to the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the training program is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the training programs to be offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal training program is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a training program. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on the user's daily behavior and cognitive test results, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines the risk of dementia based on the data analyzed by the aforementioned analysis unit, A providing unit that provides a customized cognitive training program based on the risk determined by the aforementioned determination unit, The system includes a notification unit that notifies the user of the progress and advice regarding the training provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as the user's movement history, daily activities, conversation content, and cognitive test results. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to evaluate the user's cognitive state. The system according to feature 1.

4. The determination unit, The user's risk of dementia is determined based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, We provide a customized cognitive training program tailored to the user's condition. The system according to feature 1.

6. The aforementioned notification unit, The system provides real-time updates and advice to the user and their family. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system according to feature 1.

Images