system

Abstract

The system according to this embodiment aims to detect individuals at risk of dementia early on, even without the subject actively participating in the examination, and to provide advice on improving their lifestyle. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data stored on a smartphone. The analysis unit analyzes the data collected by the collection unit and determines whether the person is at risk of developing dementia. The provision unit provides advice on improving lifestyle habits based on the determination results obtained by the analysis 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that screening for pre-dementia requires the active participation of subjects in the examination, and many people miss the opportunity for the examination.

[0005] The system according to the embodiment aims to early detect pre-dementia and provide advice on improving lifestyle habits even if the subject does not actively participate in the examination.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data stored on a smartphone. The analysis unit analyzes the data collected by the data collection unit and determines whether the person is at risk of developing dementia. The data provision unit provides advice on improving lifestyle habits based on the determination results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect individuals at risk of dementia early on, even without the subject actively participating in the examination, and can provide advice on improving their lifestyle. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 2 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 prevention system according to an embodiment of the present invention is a system that prevents the onset of dementia by identifying individuals at risk of dementia early and improving their lifestyle habits. This system uses AI to analyze data automatically accumulated on a smartphone, screening subjects without any action from them and prompting feedback for dementia prevention. Specifically, the AI ​​analyzes touch, swipe, and walking data accumulated on the smartphone. This allows for screening of individuals at risk of dementia without the subject's awareness and provides advice for improving their lifestyle habits. For example, if walking data detects a decrease in walking distance, it provides feedback such as, "Your walking distance has decreased, so please be careful." This service was proposed by a practicing dementia physician and utilizes generative AI to analyze data automatically accumulated on a smartphone, screen for individuals at risk of dementia, and provide feedback to encourage appropriate lifestyle changes. This contributes to the early detection of individuals at risk of dementia and the prevention of dementia onset. The target is the national and local governments, and it solves the problems of strain on medical and nursing care costs due to dementia and the reluctance of individuals at risk of dementia to undergo early detection screening tests. This service contributes to the early detection and prevention of dementia by screening individuals at risk of developing dementia from data automatically accumulated on their smartphones, without the subject's awareness, and providing advice on preventing the onset of dementia. The market size is estimated at 14.5 trillion yen due to the social costs associated with dementia. With the development of generative AI, it is possible to estimate cognitive function from smartphone data such as walking data and generate feedback that encourages appropriate lifestyle changes. In a society with a declining birthrate and an aging population, dementia prevention has become an urgent issue for maintaining the social healthcare system, and awareness of disease prevention is increasing throughout society. This service recognizes the importance of detecting the prodromal stages of dementia, such as SCD (Subjective Cognitive Decline) and MCI (Mild Cognitive Impairment), and uses an algorithm centered on passive data that can be continuously measured without the subject needing to actively undergo testing. This allows for the early detection of cognitive decline and encourages appropriate lifestyle changes.This means that the dementia prevention system can contribute to the early detection of individuals at risk of developing dementia and to the prevention of the onset of dementia.

[0029] The dementia prevention system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data stored on a smartphone. For example, the data collection unit collects touch, swipe, and walking data stored on the smartphone. For example, the data collection unit can collect walking data using the smartphone's sensors. The data collection unit can also collect touch data using the smartphone's touchscreen. Furthermore, the data collection unit can collect swipe data using the smartphone's accelerometer. For example, the data collection unit collects walking data from the smartphone and analyzes walking speed and stride length. The data collection unit can also collect touch data from the smartphone and analyze the frequency and intensity of touches. The data collection unit can also collect swipe data from the smartphone and analyze the direction and speed of swipes. The analysis unit analyzes the data collected by the data collection unit and determines whether a person is at risk of developing dementia. For example, the analysis unit analyzes the collected walking data and detects changes in walking speed. The analysis unit can also analyze the collected touch data and detect changes in the frequency and intensity of touches. The analysis unit can also analyze the collected swipe data and detect changes in swipe direction and speed. For example, the analysis unit can detect changes in walking data and determine that walking speed is decreasing. The analysis unit can also detect changes in touch data and determine that touch frequency is decreasing. The analysis unit can also detect changes in swipe data and determine that swipe speed is decreasing. Based on the determination results obtained by the analysis unit, the provision unit provides advice for improving lifestyle habits. For example, based on changes in walking data, the provision unit can detect that the distance walked is decreasing and provide cautionary feedback. Based on changes in touch data, the provision unit can detect that the frequency of touches is decreasing and provide cautionary feedback. Based on changes in swipe data, the provision unit can detect that the swipe speed is decreasing and provide cautionary feedback. For example, based on changes in walking data, the provision unit can provide feedback such as, "Your walking distance is decreasing, so please be careful."The system can also provide feedback based on changes in touch data, such as "The frequency of touches has decreased, so please be careful." Furthermore, it can provide feedback based on changes in swipe data, such as "The speed of swiping has decreased, so please be careful." This allows the dementia prevention system according to this embodiment to contribute to the early detection of individuals at risk of dementia and the prevention of dementia onset.

[0030] The data collection unit collects data stored on the smartphone. Specifically, it utilizes various sensors on the smartphone to record the user's daily movements and behavioral patterns in detail. For example, it can collect the user's walking data using the smartphone's accelerometer and gyroscope. This provides detailed data such as walking speed, stride length, and walking rhythm. It is also possible to collect the user's touch data using the smartphone's touchscreen. Touch data includes touch frequency, intensity, and duration, and this data is an important indicator of the user's operating habits and reaction speed. Furthermore, swipe data can also be collected using the smartphone's accelerometer. Swipe data includes swipe direction, speed, and distance, and this data is used to evaluate the fluency and accuracy of the user's operations. The data collection unit collects this data in real time and transmits it to a central database. The data is encrypted and stored securely while protecting privacy. The data collection unit can adjust the frequency and accuracy of data collection, allowing it to flexibly respond to the user's behavioral patterns and environment. For example, if it detects that the user is walking, it can increase the frequency of walking data collection to obtain more detailed data. This allows the data collection unit to efficiently collect diverse data related to user behavior and operations, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit to determine whether a user is at risk of developing dementia. Specifically, it analyzes the collected walking data, touch data, and swipe data in detail to detect changes in the user's behavior patterns and operating habits. The analysis uses machine learning and AI technologies to detect anomalies and recognize patterns in the data. For example, in the analysis of walking data, it detects changes in walking speed and irregularities in stride length and evaluates whether these changes may indicate a decline in cognitive function. In the analysis of touch data, it detects changes in touch frequency and intensity and evaluates whether these changes indicate a decline in the user's reaction speed and operational accuracy. In the analysis of swipe data, it detects changes in swipe direction and speed and evaluates whether these changes indicate a decline in the user's operational fluency and accuracy. The analysis unit comprehensively analyzes this data and can detect changes in the user's cognitive function at an early stage. Furthermore, the analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, based on past data, it can evaluate changes in the user's behavior patterns over a specific period and predict future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to quickly and accurately detect changes in the user's cognitive function and support early intervention and countermeasures.

[0032] The service provider will provide advice on improving lifestyle habits based on the results obtained by the analysis unit. Specifically, it will provide feedback tailored to changes in the user's behavior patterns and operating habits to support the maintenance and improvement of cognitive function. For example, based on changes in walking data, it can provide specific advice to the user such as, "Your walking speed has decreased recently. Try to increase the distance you walk a little bit each day." Based on changes in touch data, it can suggest, "Your touch frequency has decreased. Try playing a simple game on your smartphone." Based on changes in swipe data, it can provide feedback such as, "Your swipe speed has slowed down. Try practicing using your smartphone on a daily basis." The service provider will provide this feedback to the user at an appropriate time and support the user in easily implementing it in their daily life. Furthermore, the service provider can collect user feedback and continuously improve the effectiveness and accuracy of the advice. For example, it can record how the user reacted to the advice provided and review the content and method of providing the advice based on that data. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email in combination. This allows the service provider to quickly and reliably provide users with action instructions, contributing to the prevention of dementia.

[0033] The data collection unit can collect touch, swipe, and walking data stored on a smartphone. For example, the data collection unit can collect touch data stored on a smartphone. The data collection unit can collect the frequency and intensity of touches using the smartphone's touchscreen. For example, the data collection unit can collect swipe data stored on a smartphone. The data collection unit can collect the direction and speed of swipes using the smartphone's accelerometer. For example, the data collection unit can collect walking data stored on a smartphone. The data collection unit can collect walking speed and stride length using the smartphone's sensors. By collecting diverse data stored on the smartphone, the accuracy of screening for individuals at risk of dementia can be improved. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input touch data stored on a smartphone into a generating AI and have the generating AI perform the analysis of the touch data.

[0034] The analysis unit can analyze the collected data and detect changes in walking data. For example, the analysis unit can analyze the collected walking data and detect changes in walking speed. The analysis unit can also analyze the collected walking data and detect changes in stride length. The analysis unit can also analyze the collected walking data and detect changes in walking pattern. For example, the analysis unit can detect changes in walking speed and determine that the walking speed is decreasing. The analysis unit can also detect changes in stride length and determine that the stride length is becoming shorter. The analysis unit can also detect changes in walking pattern and determine that the walking pattern is becoming irregular. By detecting changes in walking data, it becomes possible to detect individuals at risk of dementia at an early stage. 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 walking data into a generating AI and have the generating AI perform an analysis to detect changes in walking data.

[0035] The service provider can detect a decrease in walking distance based on changes in walking data and provide cautionary feedback. For example, based on changes in walking data, the service provider can detect a decrease in walking distance and provide feedback such as, "Your walking distance has decreased, so please be careful." The service provider can also detect a decrease in walking speed based on changes in walking data and provide feedback such as, "Your walking speed has decreased, so please be careful." The service provider can also detect a decrease in stride length based on changes in walking data and provide feedback such as, "Your stride length has decreased, so please be careful." This promotes improvement of lifestyle habits by providing appropriate feedback based on changes in walking data. 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 changes in walking data into a generating AI and have the generating AI generate the feedback.

[0036] The service provider can provide specific advice on improving lifestyle habits based on the assessment results for being at risk of dementia. For example, the service provider can recommend exercise based on the assessment results for being at risk of dementia. The service provider can also recommend dietary improvements based on the assessment results for being at risk of dementia. The service provider can also provide advice on improving sleep quality based on the assessment results for being at risk of dementia. For example, as an exercise recommendation, the service provider can provide advice such as, "Try to walk for 30 minutes every day." As an improvement to diet, the service provider can provide advice such as, "Try to eat a balanced diet." As advice on improving sleep quality, the service provider can provide advice such as, "Try to go to bed at the same time every night." By providing specific advice, it becomes easier for users to improve their lifestyle habits. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the assessment results for being at risk of dementia into a generating AI and have the generating AI execute advice on improving lifestyle habits.

[0037] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting data from apps that the user has frequently used in the past. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. The data collection unit can also select the types of data to collect based on the user's past data collection history. This allows the optimal data collection method to be selected by analyzing the past data collection history. Some or all of the above 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 data collection history into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter data based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit can prioritize collecting walking data. If the user is resting, the data collection unit can also prioritize collecting touch and swipe data. If the user is out and about, the data collection unit can also prioritize collecting location data. This enables data collection tailored to the user's activity status and 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 the user's current activity status and environment data into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. If the user is on the move, the data collection unit can also prioritize the collection of data related to movement. If the user is at home, the data collection unit can also prioritize the collection of data related to home. This improves the usefulness of the data by collecting highly relevant data based on the user's geographical location information. 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 and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is very active on social media, the data collection unit will prioritize collecting data related to that activity. If a user frequently posts about a particular topic, the data collection unit may also prioritize collecting data related to that topic. If a user uses social media during a specific time period, the data collection unit may also prioritize collecting data related to that time period. This improves the relevance of the data by collecting relevant data based on the 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 and have the generating AI collect the relevant data.

[0041] 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 can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient data 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 have the generating AI adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a walking analysis algorithm to walking data. The analysis unit can also apply a touch analysis algorithm to touch data. The analysis unit can also apply a swipe analysis algorithm to swipe data. By applying an analysis algorithm appropriate to the data category, the accuracy of the analysis is improved. 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 data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0043] 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. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also prioritize the analysis of data from a specific period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. 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 data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0044] 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. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0045] The feedback provider can adjust the level of detail of the feedback based on the importance of the judgment result when providing feedback. For example, the provider can provide detailed feedback for judgment results of high importance. The provider can also provide simplified feedback for judgment results of low importance. The provider can also provide feedback with an appropriate level of detail for judgment results of medium importance. This allows for efficient feedback provision by adjusting the level of detail of the feedback according to the importance of the judgment result. Some or all of the above processing in the provider may be performed using AI, for example, or without using AI. For example, the provider can input the importance of the judgment result into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0046] The service provider can apply different feedback algorithms depending on the category of the judgment result when providing feedback. For example, the service provider can provide advice on improving walking based on changes in walking data. The service provider can also provide advice on improving touch operations based on changes in touch data. The service provider can also provide advice on improving swipe operations based on changes in swipe data. By applying a feedback algorithm according to the category of the judgment result, the accuracy of the feedback is improved. 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 category of the judgment result into a generating AI and have the generating AI execute the application of an appropriate feedback algorithm.

[0047] The service provider can determine the priority of feedback based on the timing of the collection of judgment results when providing feedback. For example, the service provider can provide feedback based on the latest judgment results. The service provider can also provide the latest feedback while referring to past judgment results. The service provider can also provide feedback based on judgment results for a specific period. This allows for the priority of providing the latest information by determining the priority of feedback based on the timing of the collection of judgment results. 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 timing of the collection of judgment results into a generating AI and have the generating AI perform the determination of the feedback priority.

[0048] The feedback provider can adjust the order of feedback based on the relevance of the judgment results when providing feedback. For example, the provider can provide feedback based on highly relevant judgment results. The provider can also postpone less relevant judgment results. The provider can also dynamically adjust the order of feedback based on the relevance of the judgment results. This enables efficient feedback provision by adjusting the order of feedback based on the relevance of the judgment results. Some or all of the above processing in the provider may be performed using AI, for example, or without using AI. For example, the provider can input the relevance of the judgment results into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The dementia prevention system can also collect and analyze the user's sleep data. The data collection unit can collect the user's sleep patterns and sleep duration using the smartphone's sensors. For example, the data collection unit can detect the user's movements during sleep and evaluate the quality of sleep. The analysis unit can analyze the collected sleep data and detect changes in sleep quality and sleep duration. For example, the analysis unit can detect that the user's sleep duration has decreased and determine the possibility of sleep deprivation. The data provision unit can provide the user with advice on improving sleep based on the analysis results. For example, it can provide feedback such as, "Your sleep duration has been shorter recently. Try to go to bed at the same time every night." This can help improve the user's sleep habits and contribute to dementia prevention.

[0051] The dementia prevention system can also collect and analyze the user's dietary data. The collection unit can use the smartphone camera to collect photos of the meals the user eats. For example, the collection unit can record the contents of meals by having the user take pictures of them. The analysis unit can analyze the collected dietary data and evaluate nutritional balance and calorie intake. For example, the analysis unit can detect that the user's diet is high in calories and determine that dietary improvements are necessary. The provision unit can provide the user with dietary improvement advice based on the analysis results. For example, it can provide feedback such as, "Your recent meals have been high in calories. Try to eat a more balanced diet." This can help improve the user's eating habits and contribute to dementia prevention.

[0052] The dementia prevention system can further collect and analyze user social activity data. The collection unit can collect smartphone app usage data and evaluate the user's social activity. For example, the collection unit can record how often the user uses social media and the number of messages sent and received. The analysis unit can analyze the collected social activity data and detect changes in the user's social activity. For example, the analysis unit can detect a decrease in the user's frequency of social media use and determine the risk of social isolation. Based on the analysis results, the provision unit can provide advice to the user to encourage them to increase their social activity. For example, it can provide feedback such as, "Your frequency of social media use has decreased recently. Try to stay in touch with your friends." This can increase the user's social activity and contribute to dementia prevention.

[0053] The dementia prevention system can also collect and analyze user exercise data. The collection unit can collect the user's exercise volume and exercise patterns using the smartphone's sensors. For example, the collection unit can record the user's movements during exercise and evaluate the quality of the exercise. The analysis unit can analyze the collected exercise data and detect changes in exercise volume and exercise patterns. For example, the analysis unit can detect a decrease in the user's exercise volume and determine the possibility of insufficient exercise. Based on the analysis results, the provision unit can provide advice to the user to encourage them to increase their exercise. For example, it can provide feedback such as, "Your exercise volume has decreased recently. Let's try walking for 30 minutes every day." This can improve the user's exercise habits and contribute to dementia prevention.

[0054] The dementia prevention system can further collect data based on the user's hobbies and interests. The data collection unit can collect data related to activities and hobbies that the user is interested in. For example, the data collection unit can record how often and what kind of music the user listens to. The analysis unit can analyze the collected data and detect changes in the user's hobbies and interests. For example, the analysis unit can detect that the user listens to music less frequently than before and determine that their interest has declined. Based on the analysis results, the provision unit can provide advice to the user to encourage them to rediscover their hobbies and interests. For example, it can provide feedback such as, "You've been listening to music less often lately. Let's refresh yourself by listening to your favorite songs." This helps maintain the user's hobbies and interests and contributes to dementia prevention.

[0055] The dementia prevention system can also provide region-specific health information based on the user's geographical location. The data collection unit can collect the user's location information and identify local health resources. For example, the data collection unit can collect information on exercise facilities and health events in the area where the user lives. The analysis unit can analyze the collected location information and identify local health resources that are beneficial to the user. For example, the analysis unit can identify exercise facilities and health events near the user and recommend them to the user. The delivery unit can provide region-specific health information to the user based on the analysis results. For example, it can provide feedback such as, "There is a walking event held every weekend at a nearby park. Why not participate?" This makes it easier for users to utilize local health resources and contributes to dementia prevention.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data stored on the smartphone. Specifically, it collects walking data using the smartphone's sensors, touch data using the touchscreen, and swipe data using the accelerometer. This allows for the collection of data such as walking speed, stride length, touch frequency and intensity, and swipe direction and speed. Step 2: The analysis unit analyzes the data collected by the collection unit to determine whether a person is at risk of developing dementia. Specifically, it analyzes walking data to detect changes in walking speed, analyzes touch data to detect changes in touch frequency and intensity, and analyzes swipe data to detect changes in swipe direction and speed. This allows it to determine changes such as a decrease in walking speed, a decrease in touch frequency, and a decrease in swipe speed. Step 3: The service provider provides advice on improving lifestyle habits based on the results obtained by the analysis unit. Specifically, it provides feedback such as "Your walking distance has decreased, so please be careful" based on changes in walking data, "Your touch frequency has decreased, so please be careful" based on changes in touch data, and "Your swipe speed has decreased, so please be careful" based on changes in swipe data.

[0058] (Example of form 2) The dementia prevention system according to an embodiment of the present invention is a system that prevents the onset of dementia by identifying individuals at risk of dementia early and improving their lifestyle habits. This system uses AI to analyze data automatically accumulated on a smartphone, screening subjects without any action from them and prompting feedback for dementia prevention. Specifically, the AI ​​analyzes touch, swipe, and walking data accumulated on the smartphone. This allows for screening of individuals at risk of dementia without the subject's awareness and provides advice for improving their lifestyle habits. For example, if walking data detects a decrease in walking distance, it provides feedback such as, "Your walking distance has decreased, so please be careful." This service was proposed by a practicing dementia physician and utilizes generative AI to analyze data automatically accumulated on a smartphone, screen for individuals at risk of dementia, and provide feedback to encourage appropriate lifestyle changes. This contributes to the early detection of individuals at risk of dementia and the prevention of dementia onset. The target is the national and local governments, and it solves the problems of strain on medical and nursing care costs due to dementia and the reluctance of individuals at risk of dementia to undergo early detection screening tests. This service contributes to the early detection and prevention of dementia by screening individuals at risk of developing dementia from data automatically accumulated on their smartphones, without the subject's awareness, and providing advice on preventing the onset of dementia. The market size is estimated at 14.5 trillion yen due to the social costs associated with dementia. With the development of generative AI, it is possible to estimate cognitive function from smartphone data such as walking data and generate feedback that encourages appropriate lifestyle changes. In a society with a declining birthrate and an aging population, dementia prevention has become an urgent issue for maintaining the social healthcare system, and awareness of disease prevention is increasing throughout society. This service recognizes the importance of detecting the prodromal stages of dementia, such as SCD (Subjective Cognitive Decline) and MCI (Mild Cognitive Impairment), and uses an algorithm centered on passive data that can be continuously measured without the subject needing to actively undergo testing. This allows for the early detection of cognitive decline and encourages appropriate lifestyle changes.This means that the dementia prevention system can contribute to the early detection of individuals at risk of developing dementia and to the prevention of the onset of dementia.

[0059] The dementia prevention system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data stored on a smartphone. For example, the data collection unit collects touch, swipe, and walking data stored on the smartphone. For example, the data collection unit can collect walking data using the smartphone's sensors. The data collection unit can also collect touch data using the smartphone's touchscreen. Furthermore, the data collection unit can collect swipe data using the smartphone's accelerometer. For example, the data collection unit collects walking data from the smartphone and analyzes walking speed and stride length. The data collection unit can also collect touch data from the smartphone and analyze the frequency and intensity of touches. The data collection unit can also collect swipe data from the smartphone and analyze the direction and speed of swipes. The analysis unit analyzes the data collected by the data collection unit and determines whether a person is at risk of developing dementia. For example, the analysis unit analyzes the collected walking data and detects changes in walking speed. The analysis unit can also analyze the collected touch data and detect changes in the frequency and intensity of touches. The analysis unit can also analyze the collected swipe data and detect changes in swipe direction and speed. For example, the analysis unit can detect changes in walking data and determine that walking speed is decreasing. The analysis unit can also detect changes in touch data and determine that touch frequency is decreasing. The analysis unit can also detect changes in swipe data and determine that swipe speed is decreasing. Based on the determination results obtained by the analysis unit, the provision unit provides advice for improving lifestyle habits. For example, based on changes in walking data, the provision unit can detect that the distance walked is decreasing and provide cautionary feedback. Based on changes in touch data, the provision unit can detect that the frequency of touches is decreasing and provide cautionary feedback. Based on changes in swipe data, the provision unit can detect that the swipe speed is decreasing and provide cautionary feedback. For example, based on changes in walking data, the provision unit can provide feedback such as, "Your walking distance is decreasing, so please be careful."The system can also provide feedback based on changes in touch data, such as "The frequency of touches has decreased, so please be careful." Furthermore, it can provide feedback based on changes in swipe data, such as "The speed of swiping has decreased, so please be careful." This allows the dementia prevention system according to this embodiment to contribute to the early detection of individuals at risk of dementia and the prevention of dementia onset.

[0060] The data collection unit collects data stored on the smartphone. Specifically, it utilizes various sensors on the smartphone to record the user's daily movements and behavioral patterns in detail. For example, it can collect the user's walking data using the smartphone's accelerometer and gyroscope. This provides detailed data such as walking speed, stride length, and walking rhythm. It is also possible to collect the user's touch data using the smartphone's touchscreen. Touch data includes touch frequency, intensity, and duration, and this data is an important indicator of the user's operating habits and reaction speed. Furthermore, swipe data can also be collected using the smartphone's accelerometer. Swipe data includes swipe direction, speed, and distance, and this data is used to evaluate the fluency and accuracy of the user's operations. The data collection unit collects this data in real time and transmits it to a central database. The data is encrypted and stored securely while protecting privacy. The data collection unit can adjust the frequency and accuracy of data collection, allowing it to flexibly respond to the user's behavioral patterns and environment. For example, if it detects that the user is walking, it can increase the frequency of walking data collection to obtain more detailed data. This allows the data collection unit to efficiently collect diverse data related to user behavior and operations, thereby improving the overall performance of the system.

[0061] The analysis unit analyzes the data collected by the collection unit to determine whether a user is at risk of developing dementia. Specifically, it analyzes the collected walking data, touch data, and swipe data in detail to detect changes in the user's behavior patterns and operating habits. The analysis uses machine learning and AI technologies to detect anomalies and recognize patterns in the data. For example, in the analysis of walking data, it detects changes in walking speed and irregularities in stride length and evaluates whether these changes may indicate a decline in cognitive function. In the analysis of touch data, it detects changes in touch frequency and intensity and evaluates whether these changes indicate a decline in the user's reaction speed and operational accuracy. In the analysis of swipe data, it detects changes in swipe direction and speed and evaluates whether these changes indicate a decline in the user's operational fluency and accuracy. The analysis unit comprehensively analyzes this data and can detect changes in the user's cognitive function at an early stage. Furthermore, the analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, based on past data, it can evaluate changes in the user's behavior patterns over a specific period and predict future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to quickly and accurately detect changes in the user's cognitive function and support early intervention and countermeasures.

[0062] The service provider will provide advice on improving lifestyle habits based on the results obtained by the analysis unit. Specifically, it will provide feedback tailored to changes in the user's behavior patterns and operating habits to support the maintenance and improvement of cognitive function. For example, based on changes in walking data, it can provide specific advice to the user such as, "Your walking speed has decreased recently. Try to increase the distance you walk a little bit each day." Based on changes in touch data, it can suggest, "Your touch frequency has decreased. Try playing a simple game on your smartphone." Based on changes in swipe data, it can provide feedback such as, "Your swipe speed has slowed down. Try practicing using your smartphone on a daily basis." The service provider will provide this feedback to the user at an appropriate time and support the user in easily implementing it in their daily life. Furthermore, the service provider can collect user feedback and continuously improve the effectiveness and accuracy of the advice. For example, it can record how the user reacted to the advice provided and review the content and method of providing the advice based on that data. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email in combination. This allows the service provider to quickly and reliably provide users with action instructions, contributing to the prevention of dementia.

[0063] The data collection unit can collect touch, swipe, and walking data stored on a smartphone. For example, the data collection unit can collect touch data stored on a smartphone. The data collection unit can collect the frequency and intensity of touches using the smartphone's touchscreen. For example, the data collection unit can collect swipe data stored on a smartphone. The data collection unit can collect the direction and speed of swipes using the smartphone's accelerometer. For example, the data collection unit can collect walking data stored on a smartphone. The data collection unit can collect walking speed and stride length using the smartphone's sensors. By collecting diverse data stored on the smartphone, the accuracy of screening for individuals at risk of dementia can be improved. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input touch data stored on a smartphone into a generating AI and have the generating AI perform the analysis of the touch data.

[0064] The analysis unit can analyze the collected data and detect changes in walking data. For example, the analysis unit can analyze the collected walking data and detect changes in walking speed. The analysis unit can also analyze the collected walking data and detect changes in stride length. The analysis unit can also analyze the collected walking data and detect changes in walking pattern. For example, the analysis unit can detect changes in walking speed and determine that the walking speed is decreasing. The analysis unit can also detect changes in stride length and determine that the stride length is becoming shorter. The analysis unit can also detect changes in walking pattern and determine that the walking pattern is becoming irregular. By detecting changes in walking data, it becomes possible to detect individuals at risk of dementia at an early stage. 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 walking data into a generating AI and have the generating AI perform an analysis to detect changes in walking data.

[0065] The service provider can detect a decrease in walking distance based on changes in walking data and provide cautionary feedback. For example, based on changes in walking data, the service provider can detect a decrease in walking distance and provide feedback such as, "Your walking distance has decreased, so please be careful." The service provider can also detect a decrease in walking speed based on changes in walking data and provide feedback such as, "Your walking speed has decreased, so please be careful." The service provider can also detect a decrease in stride length based on changes in walking data and provide feedback such as, "Your stride length has decreased, so please be careful." This promotes improvement of lifestyle habits by providing appropriate feedback based on changes in walking data. 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 changes in walking data into a generating AI and have the generating AI generate the feedback.

[0066] The service provider can provide specific advice on improving lifestyle habits based on the assessment results for being at risk of dementia. For example, the service provider can recommend exercise based on the assessment results for being at risk of dementia. The service provider can also recommend dietary improvements based on the assessment results for being at risk of dementia. The service provider can also provide advice on improving sleep quality based on the assessment results for being at risk of dementia. For example, as an exercise recommendation, the service provider can provide advice such as, "Try to walk for 30 minutes every day." As an improvement to diet, the service provider can provide advice such as, "Try to eat a balanced diet." As advice on improving sleep quality, the service provider can provide advice such as, "Try to go to bed at the same time every night." By providing specific advice, it becomes easier for users to improve their lifestyle habits. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the assessment results for being at risk of dementia into a generating AI and have the generating AI execute advice on improving lifestyle habits.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can temporarily stop data collection and resume it later. This reduces the user's burden by adjusting the timing of data collection 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 or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0068] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting data from apps that the user has frequently used in the past. The data collection unit can also suggest the optimal collection timing based on the user's past data collection history. The data collection unit can also select the types of data to collect based on the user's past data collection history. This allows the optimal data collection method to be selected by analyzing the past data collection history. Some or all of the above 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 data collection history into a generating AI and have the generating AI select the optimal collection method.

[0069] The data collection unit can filter data based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit can prioritize collecting walking data. If the user is resting, the data collection unit can also prioritize collecting touch and swipe data. If the user is out and about, the data collection unit can also prioritize collecting location data. This enables data collection tailored to the user's activity status and 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 the user's current activity status and environment data into a generating AI and have the generating AI perform the filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. If the user is relaxed, the data collection unit may also prioritize collecting relaxation-related data. If the user is in a hurry, the data collection unit may also prioritize collecting urgency-related data. This allows for more appropriate data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to be collected.

[0071] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. If the user is on the move, the data collection unit can also prioritize the collection of data related to movement. If the user is at home, the data collection unit can also prioritize the collection of data related to home. This improves the usefulness of the data by collecting highly relevant data based on the user's geographical location information. 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 and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is very active on social media, the data collection unit will prioritize collecting data related to that activity. If a user frequently posts about a particular topic, the data collection unit may also prioritize collecting data related to that topic. If a user uses social media during a specific time period, the data collection unit may also prioritize collecting data related to that time period. This improves the relevance of the data by collecting relevant data based on the 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 and have the generating AI collect the relevant data.

[0073] 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 stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0074] 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 can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient data 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 have the generating AI adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a walking analysis algorithm to walking data. The analysis unit can also apply a touch analysis algorithm to touch data. The analysis unit can also apply a swipe analysis algorithm to swipe data. By applying an analysis algorithm appropriate to the data category, the accuracy of the analysis is improved. 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 data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0076] 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 stressed, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a brief analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0077] 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. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also prioritize the analysis of data from a specific period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. 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 data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0078] 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. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0079] The service provider can estimate the user's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple, visually easy-to-understand feedback. If the user is relaxed, the service provider can also provide detailed feedback. If the user is in a hurry, the service provider can also provide concise, to-the-point feedback. By adjusting the way feedback is presented according to the user's emotions, the service provider can provide feedback that is 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way feedback is presented.

[0080] The feedback provider can adjust the level of detail of the feedback based on the importance of the judgment result when providing feedback. For example, the provider can provide detailed feedback for judgment results of high importance. The provider can also provide simplified feedback for judgment results of low importance. The provider can also provide feedback with an appropriate level of detail for judgment results of medium importance. This allows for efficient feedback provision by adjusting the level of detail of the feedback according to the importance of the judgment result. Some or all of the above processing in the provider may be performed using AI, for example, or without using AI. For example, the provider can input the importance of the judgment result into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0081] The service provider can apply different feedback algorithms depending on the category of the judgment result when providing feedback. For example, the service provider can provide advice on improving walking based on changes in walking data. The service provider can also provide advice on improving touch operations based on changes in touch data. The service provider can also provide advice on improving swipe operations based on changes in swipe data. By applying a feedback algorithm according to the category of the judgment result, the accuracy of the feedback is improved. 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 category of the judgment result into a generating AI and have the generating AI execute the application of an appropriate feedback algorithm.

[0082] The service provider can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise feedback. If the user is relaxed, the service provider can also provide detailed feedback. If the user is in a hurry, the service provider can also provide brief feedback. This allows the service provider to provide appropriate feedback to the user by adjusting the length of the feedback 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0083] The service provider can determine the priority of feedback based on the timing of the collection of judgment results when providing feedback. For example, the service provider can provide feedback based on the latest judgment results. The service provider can also provide the latest feedback while referring to past judgment results. The service provider can also provide feedback based on judgment results for a specific period. This allows for the priority of providing the latest information by determining the priority of feedback based on the timing of the collection of judgment results. 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 timing of the collection of judgment results into a generating AI and have the generating AI perform the determination of the feedback priority.

[0084] The feedback provider can adjust the order of feedback based on the relevance of the judgment results when providing feedback. For example, the provider can provide feedback based on highly relevant judgment results. The provider can also postpone less relevant judgment results. The provider can also dynamically adjust the order of feedback based on the relevance of the judgment results. This enables efficient feedback provision by adjusting the order of feedback based on the relevance of the judgment results. Some or all of the above processing in the provider may be performed using AI, for example, or without using AI. For example, the provider can input the relevance of the judgment results into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The dementia prevention system can also collect and analyze the user's sleep data. The data collection unit can collect the user's sleep patterns and sleep duration using the smartphone's sensors. For example, the data collection unit can detect the user's movements during sleep and evaluate the quality of sleep. The analysis unit can analyze the collected sleep data and detect changes in sleep quality and sleep duration. For example, the analysis unit can detect that the user's sleep duration has decreased and determine the possibility of sleep deprivation. The data provision unit can provide the user with advice on improving sleep based on the analysis results. For example, it can provide feedback such as, "Your sleep duration has been shorter recently. Try to go to bed at the same time every night." This can help improve the user's sleep habits and contribute to dementia prevention.

[0087] The dementia prevention system can also collect and analyze the user's dietary data. The collection unit can use the smartphone camera to collect photos of the meals the user eats. For example, the collection unit can record the contents of meals by having the user take pictures of them. The analysis unit can analyze the collected dietary data and evaluate nutritional balance and calorie intake. For example, the analysis unit can detect that the user's diet is high in calories and determine that dietary improvements are necessary. The provision unit can provide the user with dietary improvement advice based on the analysis results. For example, it can provide feedback such as, "Your recent meals have been high in calories. Try to eat a more balanced diet." This can help improve the user's eating habits and contribute to dementia prevention.

[0088] The dementia prevention system can further collect and analyze user social activity data. The collection unit can collect smartphone app usage data and evaluate the user's social activity. For example, the collection unit can record how often the user uses social media and the number of messages sent and received. The analysis unit can analyze the collected social activity data and detect changes in the user's social activity. For example, the analysis unit can detect a decrease in the user's frequency of social media use and determine the risk of social isolation. Based on the analysis results, the provision unit can provide advice to the user to encourage them to increase their social activity. For example, it can provide feedback such as, "Your frequency of social media use has decreased recently. Try to stay in touch with your friends." This can increase the user's social activity and contribute to dementia prevention.

[0089] The dementia prevention system can further estimate and analyze the user's stress level. The data collection unit can collect the user's heart rate and skin electrical activity using the smartphone's sensors. For example, the data collection unit can record fluctuations in the user's heart rate and evaluate their stress level. The analysis unit can analyze the collected stress data and detect changes in the user's stress level. For example, the analysis unit can detect an increase in the user's heart rate and determine that their stress level is increasing. Based on the analysis results, the service provider can offer the user advice on stress reduction. For example, it can provide feedback such as, "Your heart rate has been elevated recently. Take some time to relax." This helps manage the user's stress level and contributes to dementia prevention.

[0090] The dementia prevention system can further estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. The data collection unit can collect the user's facial expressions and tone of voice using the smartphone's camera and microphone. For example, the data collection unit can estimate emotions from the user's facial expressions and record changes in emotions. The analysis unit can analyze the collected emotion data and detect changes in the user's emotions. For example, the analysis unit can detect that the user's facial expression is sad and determine that their emotions have decreased. The data provision unit can provide the user with emotion-appropriate feedback based on the analysis results. For example, it can provide feedback such as, "You seem to have a sad expression lately. Let's go outside to cheer you up." This allows the system to provide appropriate feedback according to the user's emotions and contribute to dementia prevention.

[0091] The dementia prevention system can also collect and analyze user exercise data. The collection unit can collect the user's exercise volume and exercise patterns using the smartphone's sensors. For example, the collection unit can record the user's movements during exercise and evaluate the quality of the exercise. The analysis unit can analyze the collected exercise data and detect changes in exercise volume and exercise patterns. For example, the analysis unit can detect a decrease in the user's exercise volume and determine the possibility of insufficient exercise. Based on the analysis results, the provision unit can provide advice to the user to encourage them to increase their exercise. For example, it can provide feedback such as, "Your exercise volume has decreased recently. Let's try walking for 30 minutes every day." This can improve the user's exercise habits and contribute to dementia prevention.

[0092] The dementia prevention system can further estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The data collection unit can increase the frequency of data collection and collect more detailed data when the user's emotions are heightened. For example, if the user is excited, the data collection unit can frequently collect data on heart rate and skin electrical activity. The analysis unit can analyze the collected data and detect changes in emotions. For example, the analysis unit can detect a sudden increase in the user's heart rate and determine that their emotions are heightened. Based on the analysis results, the provision unit can provide the user with emotion-appropriate feedback. For example, it can provide feedback such as, "Your heart rate has been rising recently. Take a deep breath and relax." This allows for appropriate data collection and feedback tailored to the user's emotions, contributing to dementia prevention.

[0093] The dementia prevention system can further collect data based on the user's hobbies and interests. The data collection unit can collect data related to activities and hobbies that the user is interested in. For example, the data collection unit can record how often and what kind of music the user listens to. The analysis unit can analyze the collected data and detect changes in the user's hobbies and interests. For example, the analysis unit can detect that the user listens to music less frequently than before and determine that their interest has declined. Based on the analysis results, the provision unit can provide advice to the user to encourage them to rediscover their hobbies and interests. For example, it can provide feedback such as, "You've been listening to music less often lately. Let's refresh yourself by listening to your favorite songs." This helps maintain the user's hobbies and interests and contributes to dementia prevention.

[0094] The dementia prevention system can further estimate the user's emotions and adjust the way feedback is presented based on those estimated emotions. The service provider can provide detailed feedback when the user is relaxed. For example, when the user is relaxed, the service provider can provide specific advice on improving lifestyle habits. The analysis unit can analyze the collected data and detect changes in the user's emotions. For example, the analysis unit can detect that the user's facial expression is calm and determine that they are relaxed. Based on the analysis results, the service provider can provide the user with emotion-appropriate feedback. For example, it can provide feedback such as, "You seem relaxed lately. Let's continue to strive for a balanced lifestyle." This allows the system to provide appropriate feedback tailored to the user's emotions and contribute to dementia prevention.

[0095] The dementia prevention system can also provide region-specific health information based on the user's geographical location. The data collection unit can collect the user's location information and identify local health resources. For example, the data collection unit can collect information on exercise facilities and health events in the area where the user lives. The analysis unit can analyze the collected location information and identify local health resources that are beneficial to the user. For example, the analysis unit can identify exercise facilities and health events near the user and recommend them to the user. The delivery unit can provide region-specific health information to the user based on the analysis results. For example, it can provide feedback such as, "There is a walking event held every weekend at a nearby park. Why not participate?" This makes it easier for users to utilize local health resources and contributes to dementia prevention.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects data stored on the smartphone. Specifically, it collects walking data using the smartphone's sensors, touch data using the touchscreen, and swipe data using the accelerometer. This allows for the collection of data such as walking speed, stride length, touch frequency and intensity, and swipe direction and speed. Step 2: The analysis unit analyzes the data collected by the collection unit to determine whether a person is at risk of developing dementia. Specifically, it analyzes walking data to detect changes in walking speed, analyzes touch data to detect changes in touch frequency and intensity, and analyzes swipe data to detect changes in swipe direction and speed. This allows it to determine changes such as a decrease in walking speed, a decrease in touch frequency, and a decrease in swipe speed. Step 3: The service provider provides advice on improving lifestyle habits based on the results obtained by the analysis unit. Specifically, it provides feedback such as "Your walking distance has decreased, so please be careful" based on changes in walking data, "Your touch frequency has decreased, so please be careful" based on changes in touch data, and "Your swipe speed has decreased, so please be careful" based on changes in swipe data.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects walking data using the sensors of the smart device 14, collects touch data using the touchscreen, and collects swipe data using the accelerometer. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and determines whether a person is at risk of developing dementia. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12, which provides advice on improving lifestyle habits based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be implemented in the control unit 46A of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects walking data using the sensors of the smart glasses 214, collects touch data using the touchscreen, and collects swipe data using the accelerometer. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and determines whether a person is at risk of developing dementia. The data provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which provides advice on improving lifestyle habits based on the analysis results. Some or all of the data collection unit, analysis unit, and data provision unit may be implemented, for example, in the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects walking data using the sensors of the headset terminal 314, collects touch data using the touchscreen, and collects swipe data using the accelerometer. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to determine whether the person is at risk of dementia. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides advice on improving lifestyle habits based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be implemented in the control unit 46A of the headset terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects walking data using the robot 414's sensors, collects touch data using the touchscreen, and collects swipe data using the acceleration sensor. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and determines whether the person is at risk of dementia. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which provides advice on improving lifestyle habits based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be implemented, for example, in the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects data stored on smartphones, An analysis unit analyzes the data collected by the aforementioned collection unit and determines whether a person is at risk of developing dementia, The system includes a provisioning unit that provides advice on improving lifestyle habits based on the determination results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects touch, swipe, and walking data stored on smartphones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to detect changes in walking data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on changes in walking data, it detects when the distance walked decreases and provides alerting feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on the assessment results for being at risk of dementia, we provide specific advice on improving lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing feedback, adjust the level of detail in the feedback based on the importance of the assessment result. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing feedback, different feedback algorithms are applied depending on the category of the judgment result. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing feedback, we prioritize the feedback based on when the assessment results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, adjust the order of feedback based on the relevance of the judgment results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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 stored on smartphones, An analysis unit analyzes the data collected by the aforementioned collection unit and determines whether a person is at risk of developing dementia, The system includes a provisioning unit that provides advice on improving lifestyle habits based on the determination results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects touch, swipe, and walking data stored on smartphones. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to detect changes in walking data. The system according to feature 1.

4. The aforementioned supply unit is, Based on changes in walking data, it detects when the distance walked decreases and provides alerting feedback. The system according to feature 1.

5. The aforementioned supply unit is, Based on the assessment results for being at risk of dementia, we provide specific advice on improving lifestyle habits. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. The system according to feature 1.

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