System

Abstract

An object of a system according to an embodiment is to predict a dementia risk on the basis of personal data of an individual and propose an appropriate preventive measure.SOLUTION: A system includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection part collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The predictor predicts the dementia risk based on the data analyzed by the analyzer. The proposal unit proposes a preventive measure based on the dementia risk predicted by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of not being able to adequately predict dementia risk based on an individual's personal data and propose appropriate preventive measures.

[0005] The system according to the embodiment aims to predict the risk of dementia based on the personal data of each individual and propose appropriate preventive measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects personal data. The analysis unit analyzes the personal data collected by the data collection unit. The prediction unit predicts a dementia risk based on the data analyzed by the analysis unit. The proposal unit proposes preventive measures based on the dementia risk predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the risk of dementia based on the personal data of each individual and propose appropriate preventive measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dementia prediction system according to an embodiment of the present invention is a system that uses personal data of an individual to predict the onset time and symptoms of dementia and, based on the prediction, proposes optimal preventive measures for each individual. This allows the dementia prediction system to assess the user's dementia risk and propose optimal preventive measures.

[0029] A dementia prediction system according to an embodiment includes a data collection unit, an analysis unit, a prediction unit, and a suggestion unit. The data collection unit collects personal data of a user. For example, the data collection unit collects data such as medical history, vital signs (heart rate, blood pressure, etc.), dietary habits (calorie intake, nutritional balance, etc.), exercise history (exercise frequency, type of exercise, etc.), sleep (sleep time, sleep quality, etc.), daily schedule (time allocation for work and hobbies, etc.), hobbies and preferences (favorite activities, areas of interest, etc.), personality (personality assessment results, etc.), family structure (number of family members, etc.), pets (types and number of pets, etc.), friends (breadth and depth of friendships, etc.), and living environment (area where the user lives, housing conditions, etc.). The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may analyze the data using data mining technology to evaluate the user's health condition and lifestyle. The analysis unit may also analyze the data using statistical analysis technology to identify the user's risk factors. The analysis unit can also analyze data using a machine learning algorithm and construct a model for predicting the user's dementia risk. The prediction unit predicts the dementia risk based on the data analyzed by the analysis unit. For example, the prediction unit can evaluate the user's dementia risk using a scoring system. The prediction unit can also weight risk factors and predict the user's dementia risk. The prediction unit can also predict the user's dementia risk using a machine learning model. The suggestion unit suggests preventive measures based on the dementia risk predicted by the prediction unit. For example, the suggestion unit can suggest preventive exercises (walking, yoga, etc.). The suggestion unit can also suggest lifestyles (regular living, stress management, etc.). The suggestion unit can also suggest supplements or Chinese herbal medicines (vitamin D, omega-3 fatty acids, etc.). The suggestion unit can also suggest hobby activities (reading, puzzles, ballroom dancing, etc.). In this way, the dementia prediction system according to the embodiment can assess the user's dementia risk and suggest optimal preventive measures. For example, the user can prevent the onset of dementia by practicing the suggested preventive measures. The suggestion unit can also guide the user to service providers.For example, the suggestion unit may introduce fitness clubs and yoga studios related to preventive exercise. The suggestion unit may also introduce pharmacies and online shops that sell supplements and Chinese herbal medicines. The suggestion unit may also introduce classes and events related to hobby activities. This allows users to easily use the services they need.

[0030] The data collection unit analyzes the user's voice data and can evaluate the stress level and mental state from changes in voice tone and speaking style. The data collection unit, for example, collects voice data and develops an algorithm for analyzing changes in voice tone and speaking style. For example, it analyzes the pitch and speed of the voice to estimate the stress level. The data collection unit also collects voice data from devices the user uses on a daily basis (such as a smartphone or smart speaker) and evaluates the stress level and mental state. For example, it analyzes voice data during phone calls. The data collection unit also analyzes the voice data and evaluates the stress level based on the frequency of occurrence of specific keywords and phrases. For example, it determines that the stress level is high if negative words are used frequently. This makes it possible to evaluate the stress level and mental state from the voice data.

[0031] The data collection unit can analyze a user's SNS activity or online behavior to evaluate their social connection or isolation level. For example, the data collection unit can analyze the frequency of a user's SNS posts and comments to evaluate the strength of their social connection. For example, the data collection unit can measure their isolation level based on the frequency and content of interactions with friends. The data collection unit can also collect online behavior data and analyze the activity status of online communities and forums in which the user participates. For example, the data collection unit can evaluate their social connection level based on the frequency of participation and the content of posts. The data collection unit can also analyze the user's SNS network to evaluate the number and relationships of friends and followers. For example, the data collection unit can measure their isolation level based on the density and centrality of the network. This makes it possible to evaluate their social connection and isolation level from their SNS activity and online behavior.

[0032] The data collection unit can analyze data collected from IoT devices in the home to evaluate the user's lifestyle and health status. For example, the data collection unit can analyze the usage history of a smart refrigerator to evaluate the user's eating habits and nutritional balance. For example, the data collection unit can analyze eating habits based on the types of food in the refrigerator and their consumption frequency. The data collection unit can also analyze the usage patterns of smart lighting to evaluate the user's lifestyle and sleep patterns. For example, the data collection unit can estimate sleep time and lifestyle based on the on / off times of the lights. The data collection unit can also analyze the usage data of a smart speaker to evaluate the user's music and podcast listening habits. For example, the data collection unit can identify hobbies and areas of interest based on the playback history. This makes it possible to analyze data from IoT devices to evaluate the user's lifestyle and health status.

[0033] The data collection unit collects health data of the user's pet and can evaluate the impact of interaction with the pet on the risk of dementia. The data collection unit, for example, collects health data of the pet (e.g., activity level and amount of food) and evaluates the impact of interaction with the user on the risk of dementia. For example, it analyzes whether the pet's activity level affects the amount of exercise the user does. The data collection unit also analyzes the pet's health condition and behavioral patterns and evaluates the impact on the user's mental state and stress level. For example, it measures the impact of the pet's health condition on the user's emotions. The data collection unit also collects data on daily activities with the pet (e.g., walks and play) and evaluates the impact on the user's daily rhythm and social connections. For example, it analyzes the impact of walking with the pet on the user's exercise habits. This makes it possible to evaluate the impact of interaction with the pet on the risk of dementia.

[0034] The prediction unit can analyze the user's genetic information and evaluate the impact of genetic factors on dementia risk. The prediction unit, for example, collects the user's genetic information and identifies gene mutations associated with dementia risk. For example, it analyzes mutations in the APOE gene and evaluates the risk. The prediction unit also evaluates the impact of family history and genetic factors on dementia risk based on the genetic information. For example, it analyzes the risk when there is a family member with dementia. The prediction unit also analyzes the genetic information and evaluates the impact of specific gene mutations on dementia risk. For example, it combines multiple gene mutations to calculate a risk score. This makes it possible to evaluate the impact of genetic factors on dementia risk from the genetic information.

[0035] The prediction unit can analyze the user's living environment and evaluate the impact of environmental factors on dementia risk. The prediction unit, for example, collects data on the user's living environment (e.g., noise level and air quality) and evaluates the impact on dementia risk. For example, it analyzes the risk of a user living in an area with a high noise level. The prediction unit also evaluates the impact of environmental factors on dementia risk based on the living environment data. For example, it analyzes the risk of a user living in an area with poor air quality. The prediction unit also analyzes the user's living environment data and evaluates the impact of specific environmental factors on dementia risk. For example, it analyzes the risk of a user living in an area with little green space. This makes it possible to evaluate the impact of environmental factors on dementia risk from the living environment.

[0036] The prediction unit collects data on the user's workplace environment and can evaluate the impact of workplace stress levels and interpersonal relationships on dementia risk. The prediction unit, for example, collects data on the user's workplace environment (e.g., stress levels and interpersonal relationships) and evaluates the impact on dementia risk. For example, it analyzes the risk of a user with a high workplace stress level. The prediction unit also evaluates the impact of stress levels and interpersonal relationships on dementia risk based on the workplace environment data. For example, it analyzes the risk of a user with poor workplace interpersonal relationships. The prediction unit also analyzes the user's workplace environment data and evaluates the impact of specific workplace factors on dementia risk. For example, it analyzes the impact of long working hours on dementia risk. This makes it possible to evaluate the impact of stress levels and interpersonal relationships on dementia risk from the workplace environment data.

[0037] The prediction unit can analyze the user's travel history and evaluate the impact of adapting to different cultures and environments on dementia risk. The prediction unit, for example, collects the user's travel history data and evaluates the impact of adapting to different cultures and environments on dementia risk. For example, it analyzes the risk of a user who travels abroad frequently. The prediction unit also evaluates the impact of adapting to different cultures and environments on dementia risk based on the travel history data. For example, it analyzes the risk of a user who has many cross-cultural experiences. The prediction unit also analyzes the user's travel history data and evaluates the impact of specific travel patterns on dementia risk. For example, it analyzes the impact of long-term travel on dementia risk. This makes it possible to evaluate the impact of adapting to different cultures and environments on dementia risk from the travel history.

[0038] The suggestion unit can analyze the user's dietary data and suggest specific meal menus for optimizing nutritional balance. The suggestion unit, for example, collects the user's dietary data and analyzes nutritional balance. For example, it evaluates the calorie intake and nutrient balance and suggests an optimal meal menu. Furthermore, the suggestion unit suggests meal menus to supplement a specific nutrient if the user is deficient in a particular nutrient based on the dietary data. For example, if the user is deficient in vitamin D, it suggests foods that are high in vitamin D. Furthermore, the suggestion unit analyzes the user's dietary data and suggests meal menus according to the user's health condition and lifestyle. For example, it suggests high-protein meals for a user who exercises a lot. In this way, it is possible to analyze the dietary data and suggest specific meal menus for optimizing nutritional balance.

[0039] The suggestion unit can analyze the user's exercise data and suggest an exercise program according to the user's individual physical fitness level. The suggestion unit, for example, collects the user's exercise data and evaluates the user's physical fitness level. For example, it calculates the physical fitness level based on the frequency and intensity of exercise and suggests an appropriate exercise program. The suggestion unit also suggests an exercise program if a specific exercise is effective based on the exercise data. For example, it suggests aerobic exercise to improve cardiopulmonary function. The suggestion unit also analyzes the user's exercise data and suggests an exercise program according to the user's health condition and lifestyle. For example, it suggests low-impact exercise that does not put strain on the joints. In this way, the exercise data can be analyzed and an exercise program according to the user's physical fitness level can be suggested.

[0040] The suggestion unit can suggest new hobbies and activities based on the user's hobbies and interests, which can be useful in preventing dementia. For example, the suggestion unit analyzes the user's hobbies and interests and suggests new hobbies and activities. For example, it can suggest books of a new genre to a user who likes reading. The suggestion unit also suggests activities that are effective in preventing dementia based on the hobbies and interests. For example, it can suggest brain training such as puzzles and crossword puzzles. The suggestion unit also analyzes the user's hobbies and interests and suggests activities to increase social connections. For example, it can suggest ballroom dancing or volunteer work. In this way, it is possible to suggest new hobbies and activities based on the hobbies and interests, which can be useful in preventing dementia.

[0041] The suggestion unit can suggest relaxation methods that match the user's lifestyle rhythm. The suggestion unit, for example, analyzes the user's lifestyle rhythm and suggests appropriate relaxation methods. For example, it suggests evening relaxation methods for a night-owl user. The suggestion unit also suggests relaxation methods that are effective in reducing stress based on the user's lifestyle rhythm. For example, it suggests deep breathing techniques and meditation. The suggestion unit also analyzes the user's lifestyle rhythm and suggests relaxation methods. For example, it suggests short relaxation methods that can be done in between work. In this way, it is possible to suggest relaxation methods that match the user's lifestyle rhythm.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The dementia prediction system can further analyze the user's sleep data and evaluate the impact of sleep quality on dementia risk. For example, the data collection unit monitors the user's sleep duration and sleep depth, and the analysis unit evaluates sleep quality based on this data. The prediction unit predicts that poor sleep quality may increase the risk of dementia, and the suggestion unit suggests measures to improve sleep quality. For example, it can suggest relaxing herbal tea or stretching before bed.

[0044] The dementia prediction system can further analyze the user's exercise data and evaluate the impact of exercise habits on dementia risk. For example, the data collection unit monitors the user's exercise frequency and intensity, and the analysis unit evaluates the user's exercise habits based on this data. The prediction unit predicts the possibility that a user's dementia risk will increase if they do not exercise regularly, and the suggestion unit suggests measures to improve the user's exercise habits. For example, it can suggest exercise programs such as walking or yoga.

[0045] The dementia prediction system can further analyze the user's dietary data to evaluate the impact of nutritional balance on dementia risk. For example, the data collection unit monitors the user's dietary content and calorie intake, and the analysis unit evaluates nutritional balance based on this data. The prediction unit predicts the possibility that poor nutritional balance will increase the risk of dementia, and the suggestion unit suggests measures to improve nutritional balance. For example, it can suggest foods rich in vitamin D and omega-3 fatty acids.

[0046] The dementia prediction system can further analyze a user's social connections and evaluate the impact of isolation on dementia risk. For example, the data collection unit monitors the user's social media activity and online behavior, and the analysis unit evaluates social connections based on this data. The prediction unit predicts that a high level of isolation may increase the risk of dementia, and the suggestion unit suggests measures to increase social connections. For example, ballroom dancing or volunteer activities could be suggested.

[0047] The dementia prediction system can further analyze the health data of the user's pet and evaluate the impact of interaction with the pet on the risk of dementia. For example, the data collection unit monitors the pet's activity level and food intake, and the analysis unit evaluates the interaction with the pet based on this data. The prediction unit predicts that little interaction with the pet may increase the risk of dementia, and the suggestion unit suggests measures to increase interaction with the pet. For example, it can suggest taking the pet for walks or playing.

[0048] The dementia prediction system can further analyze the user's work environment data to evaluate the impact of workplace stress levels and interpersonal relationships on dementia risk. For example, the data collection unit collects the user's work environment data (e.g., stress levels and interpersonal relationships), and the analysis unit evaluates the work environment based on this data. The prediction unit predicts the possibility that a high workplace stress level will increase the risk of dementia, and the suggestion unit suggests stress management methods and measures to improve the work environment. For example, it can suggest relaxation techniques and improving communication skills.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection unit collects personal data about the user. For example, the data collection unit collects data such as medical history, vital signs (heart rate, blood pressure, etc.), dietary habits (calorie intake, nutritional balance, etc.), exercise history (exercise frequency, type of exercise, etc.), sleep (sleep time, sleep quality, etc.), daily schedule (time allocation for work and hobbies, etc.), hobbies and preferences (favorite activities and areas of interest, etc.), personality (personality assessment results, etc.), family structure (number of family members and relationships, etc.), pets (types and number of pets, etc.), friends (breadth and depth of friendships, etc.), and living environment (area of ​​residence, housing conditions, etc.). Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may analyze the data using data mining technology to evaluate the user's health condition and lifestyle habits. The analysis unit may also analyze the data using statistical analysis technology to identify the user's risk factors. The analysis unit may also analyze the data using machine learning algorithms to build a model for predicting the user's dementia risk. Step 3: The prediction unit predicts the dementia risk based on the data analyzed by the analysis unit. For example, the prediction unit may use a scoring system to assess the user's dementia risk. The prediction unit may also weight risk factors to predict the user's dementia risk. The prediction unit may also use a machine learning model to predict the user's dementia risk. Step 4: The suggestion unit suggests preventive measures based on the dementia risk predicted by the prediction unit. For example, the suggestion unit suggests preventive exercises (walking, yoga, etc.). The suggestion unit can also suggest lifestyle changes (regular living, stress management, etc.). The suggestion unit can also suggest supplements or Chinese herbal medicines (vitamin D, omega-3 fatty acids, etc.). The suggestion unit can also suggest hobby activities (reading, puzzles, ballroom dancing, etc.). This allows the user to prevent the onset of dementia by practicing the suggested preventive measures. The suggestion unit can also guide the user to service providers. For example, the suggestion unit can introduce fitness clubs and yoga studios related to preventive exercises. The suggestion unit can also introduce pharmacies and online shops that sell supplements and Chinese herbal medicines. The suggestion unit can also introduce classes and events related to hobby activities. This allows the user to easily use the services they need.

[0051] (Example 2) The dementia prediction system according to an embodiment of the present invention is a system that uses personal data of an individual to predict the onset time and symptoms of dementia and, based on the prediction, proposes optimal preventive measures for each individual. This allows the dementia prediction system to assess the user's dementia risk and propose optimal preventive measures.

[0052] A dementia prediction system according to an embodiment includes a data collection unit, an analysis unit, a prediction unit, and a suggestion unit. The data collection unit collects personal data of a user. For example, the data collection unit collects data such as medical history, vital signs (heart rate, blood pressure, etc.), dietary habits (calorie intake, nutritional balance, etc.), exercise history (exercise frequency, type of exercise, etc.), sleep (sleep time, sleep quality, etc.), daily schedule (time allocation for work and hobbies, etc.), hobbies and preferences (favorite activities, areas of interest, etc.), personality (personality assessment results, etc.), family structure (number of family members, etc.), pets (types and number of pets, etc.), friends (breadth and depth of friendships, etc.), and living environment (area where the user lives, housing conditions, etc.). The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may analyze the data using data mining technology to evaluate the user's health condition and lifestyle. The analysis unit may also analyze the data using statistical analysis technology to identify the user's risk factors. The analysis unit can also analyze data using a machine learning algorithm and construct a model for predicting the user's dementia risk. The prediction unit predicts the dementia risk based on the data analyzed by the analysis unit. For example, the prediction unit can evaluate the user's dementia risk using a scoring system. The prediction unit can also weight risk factors and predict the user's dementia risk. The prediction unit can also predict the user's dementia risk using a machine learning model. The suggestion unit suggests preventive measures based on the dementia risk predicted by the prediction unit. For example, the suggestion unit can suggest preventive exercises (walking, yoga, etc.). The suggestion unit can also suggest lifestyles (regular living, stress management, etc.). The suggestion unit can also suggest supplements or Chinese herbal medicines (vitamin D, omega-3 fatty acids, etc.). The suggestion unit can also suggest hobby activities (reading, puzzles, ballroom dancing, etc.). In this way, the dementia prediction system according to the embodiment can assess the user's dementia risk and suggest optimal preventive measures. For example, the user can prevent the onset of dementia by practicing the suggested preventive measures. The suggestion unit can also guide the user to service providers.For example, the suggestion unit may introduce fitness clubs and yoga studios related to preventive exercise. The suggestion unit may also introduce pharmacies and online shops that sell supplements and Chinese herbal medicines. The suggestion unit may also introduce classes and events related to hobby activities. This allows users to easily use the services they need.

[0053] The data collection unit can analyze the user's emotional state in real time and evaluate the impact of emotional fluctuations on dementia risk. The data collection unit, for example, uses facial expression recognition technology to analyze the user's emotional state in real time. For example, it captures the user's facial expressions using a camera and quantifies emotional fluctuations. The data collection unit also uses voice analysis technology to analyze changes in the user's tone of voice and speaking style to evaluate the emotional state. For example, it collects voice data and estimates stress levels and mental state. The data collection unit also analyzes the user's social media activities and online behavior to evaluate emotional fluctuations. For example, it calculates an emotional score for the content of posts and comments and monitors emotional fluctuations. This makes it possible to evaluate the impact of emotional fluctuations on dementia risk.

[0054] The data collection unit analyzes the user's voice data and can evaluate the stress level and mental state from changes in voice tone and speaking style. The data collection unit, for example, collects voice data and develops an algorithm for analyzing changes in voice tone and speaking style. For example, it analyzes the pitch and speed of the voice to estimate the stress level. The data collection unit also collects voice data from devices the user uses on a daily basis (such as a smartphone or smart speaker) and evaluates the stress level and mental state. For example, it analyzes voice data during phone calls. The data collection unit also analyzes the voice data and evaluates the stress level based on the frequency of occurrence of specific keywords and phrases. For example, it determines that the stress level is high if negative words are used frequently. This makes it possible to evaluate the stress level and mental state from the voice data.

[0055] The data collection unit can analyze a user's SNS activity or online behavior to evaluate their social connection or isolation level. For example, the data collection unit can analyze the frequency of a user's SNS posts and comments to evaluate the strength of their social connection. For example, the data collection unit can measure their isolation level based on the frequency and content of interactions with friends. The data collection unit can also collect online behavior data and analyze the activity status of online communities and forums in which the user participates. For example, the data collection unit can evaluate their social connection level based on the frequency of participation and the content of posts. The data collection unit can also analyze the user's SNS network to evaluate the number and relationships of friends and followers. For example, the data collection unit can measure their isolation level based on the density and centrality of the network. This makes it possible to evaluate their social connection and isolation level from their SNS activity and online behavior.

[0056] The data collection unit can analyze data collected from IoT devices in the home to evaluate the user's lifestyle and health status. For example, the data collection unit can analyze the usage history of a smart refrigerator to evaluate the user's eating habits and nutritional balance. For example, the data collection unit can analyze eating habits based on the types of food in the refrigerator and their consumption frequency. The data collection unit can also analyze the usage patterns of smart lighting to evaluate the user's lifestyle and sleep patterns. For example, the data collection unit can estimate sleep time and lifestyle based on the on / off times of the lights. The data collection unit can also analyze the usage data of a smart speaker to evaluate the user's music and podcast listening habits. For example, the data collection unit can identify hobbies and areas of interest based on the playback history. This makes it possible to analyze data from IoT devices to evaluate the user's lifestyle and health status.

[0057] The data collection unit collects health data of the user's pet and can evaluate the impact of interaction with the pet on the risk of dementia. The data collection unit, for example, collects health data of the pet (e.g., activity level and amount of food) and evaluates the impact of interaction with the user on the risk of dementia. For example, it analyzes whether the pet's activity level affects the amount of exercise the user does. The data collection unit also analyzes the pet's health condition and behavioral patterns and evaluates the impact on the user's mental state and stress level. For example, it measures the impact of the pet's health condition on the user's emotions. The data collection unit also collects data on daily activities with the pet (e.g., walks and play) and evaluates the impact on the user's daily rhythm and social connections. For example, it analyzes the impact of walking with the pet on the user's exercise habits. This makes it possible to evaluate the impact of interaction with the pet on the risk of dementia.

[0058] The data collection unit can use the emotion estimation function to analyze the emotional state of the user when performing daily activities and suggest stress reduction measures. The data collection unit, for example, analyzes the emotional state of the user when cooking and suggests stress reduction measures. For example, it analyzes facial expressions and voice while cooking and suggests relaxing music. The data collection unit also analyzes the emotional state of the user when cleaning and suggests stress reduction measures. For example, it analyzes movements and facial expressions while cleaning and suggests efficient cleaning methods. The data collection unit also analyzes the emotional state of the user when exercising and suggests stress reduction measures. For example, it analyzes heart rate and facial expressions during exercise and suggests appropriate exercise intensity and rest timing. In this way, it is possible to analyze the emotional state during daily activities and suggest stress reduction measures.

[0059] The prediction unit can analyze the user's emotional state and evaluate the impact of emotional fluctuations on dementia risk. The prediction unit, for example, uses facial expression recognition technology to analyze the user's emotional state. For example, it captures the user's facial expressions using a camera and quantifies emotional fluctuations. The prediction unit also uses voice analysis technology to analyze changes in the user's tone of voice and speaking style to evaluate the emotional state. For example, it collects voice data and estimates stress levels and mental state. The prediction unit also analyzes the user's social media activities and online behavior to evaluate emotional fluctuations. For example, it calculates an emotional score for the content of posts and comments and monitors emotional fluctuations. This makes it possible to evaluate the impact of emotional fluctuations on dementia risk.

[0060] The prediction unit can analyze the user's genetic information and evaluate the impact of genetic factors on dementia risk. The prediction unit, for example, collects the user's genetic information and identifies gene mutations associated with dementia risk. For example, it analyzes mutations in the APOE gene and evaluates the risk. The prediction unit also evaluates the impact of family history and genetic factors on dementia risk based on the genetic information. For example, it analyzes the risk when there is a family member with dementia. The prediction unit also analyzes the genetic information and evaluates the impact of specific gene mutations on dementia risk. For example, it combines multiple gene mutations to calculate a risk score. This makes it possible to evaluate the impact of genetic factors on dementia risk from the genetic information.

[0061] The prediction unit can analyze the user's living environment and evaluate the impact of environmental factors on dementia risk. The prediction unit, for example, collects data on the user's living environment (e.g., noise level and air quality) and evaluates the impact on dementia risk. For example, it analyzes the risk of a user living in an area with a high noise level. The prediction unit also evaluates the impact of environmental factors on dementia risk based on the living environment data. For example, it analyzes the risk of a user living in an area with poor air quality. The prediction unit also analyzes the user's living environment data and evaluates the impact of specific environmental factors on dementia risk. For example, it analyzes the risk of a user living in an area with little green space. This makes it possible to evaluate the impact of environmental factors on dementia risk from the living environment.

[0062] The prediction unit collects data on the user's workplace environment and can evaluate the impact of workplace stress levels and interpersonal relationships on dementia risk. The prediction unit, for example, collects data on the user's workplace environment (e.g., stress levels and interpersonal relationships) and evaluates the impact on dementia risk. For example, it analyzes the risk of a user with a high workplace stress level. The prediction unit also evaluates the impact of stress levels and interpersonal relationships on dementia risk based on the workplace environment data. For example, it analyzes the risk of a user with poor workplace interpersonal relationships. The prediction unit also analyzes the user's workplace environment data and evaluates the impact of specific workplace factors on dementia risk. For example, it analyzes the impact of long working hours on dementia risk. This makes it possible to evaluate the impact of stress levels and interpersonal relationships on dementia risk from the workplace environment data.

[0063] The prediction unit can analyze the user's travel history and evaluate the impact of adapting to different cultures and environments on dementia risk. The prediction unit, for example, collects the user's travel history data and evaluates the impact of adapting to different cultures and environments on dementia risk. For example, it analyzes the risk of a user who travels abroad frequently. The prediction unit also evaluates the impact of adapting to different cultures and environments on dementia risk based on the travel history data. For example, it analyzes the risk of a user who has many cross-cultural experiences. The prediction unit also analyzes the user's travel history data and evaluates the impact of specific travel patterns on dementia risk. For example, it analyzes the impact of long-term travel on dementia risk. This makes it possible to evaluate the impact of adapting to different cultures and environments on dementia risk from the travel history.

[0064] The prediction unit uses the emotion estimation function to analyze the emotional state of the user when performing a specific activity and evaluate the impact of the activity on the risk of dementia. The prediction unit, for example, analyzes the emotional state of the user when exercising and evaluates the impact of the activity on the risk of dementia. For example, the prediction unit analyzes the risk based on the emotional score during exercise. The prediction unit also analyzes the emotional state of the user when performing a hobby activity and evaluates the impact of the activity on the risk of dementia. For example, the prediction unit analyzes the risk based on the emotional score during the hobby activity. The prediction unit also analyzes the emotional state of the user when performing a social activity and evaluates the impact of the activity on the risk of dementia. For example, the prediction unit analyzes the risk based on the emotional score during the social activity. This makes it possible to analyze the emotional state during a specific activity and evaluate the impact of the activity on the risk of dementia.

[0065] The suggestion unit can analyze the user's emotional state and suggest preventive measures according to emotional fluctuations. For example, the suggestion unit analyzes the user's emotional state and suggests preventive exercises according to emotional fluctuations. For example, if stress is high, it suggests yoga, which has a relaxing effect. The suggestion unit also suggests lifestyle improvements according to emotional fluctuations based on the emotional state. For example, if negative emotions persist, it suggests stress management methods. The suggestion unit also analyzes the user's emotional state and suggests supplements or Chinese herbal medicine according to emotional fluctuations. For example, if stress is high, it suggests supplements with a relaxing effect. In this way, preventive measures can be suggested according to emotional fluctuations.

[0066] The suggestion unit can analyze the user's dietary data and suggest specific meal menus for optimizing nutritional balance. The suggestion unit, for example, collects the user's dietary data and analyzes nutritional balance. For example, it evaluates the calorie intake and nutrient balance and suggests an optimal meal menu. Furthermore, the suggestion unit suggests meal menus to supplement a specific nutrient if the user is deficient in a particular nutrient based on the dietary data. For example, if the user is deficient in vitamin D, it suggests foods that are high in vitamin D. Furthermore, the suggestion unit analyzes the user's dietary data and suggests meal menus according to the user's health condition and lifestyle. For example, it suggests high-protein meals for a user who exercises a lot. In this way, it is possible to analyze the dietary data and suggest specific meal menus for optimizing nutritional balance.

[0067] The suggestion unit can analyze the user's exercise data and suggest an exercise program according to the user's individual physical fitness level. The suggestion unit, for example, collects the user's exercise data and evaluates the user's physical fitness level. For example, it calculates the physical fitness level based on the frequency and intensity of exercise and suggests an appropriate exercise program. The suggestion unit also suggests an exercise program if a specific exercise is effective based on the exercise data. For example, it suggests aerobic exercise to improve cardiopulmonary function. The suggestion unit also analyzes the user's exercise data and suggests an exercise program according to the user's health condition and lifestyle. For example, it suggests low-impact exercise that does not put strain on the joints. In this way, the exercise data can be analyzed and an exercise program according to the user's physical fitness level can be suggested.

[0068] The suggestion unit can suggest new hobbies and activities based on the user's hobbies and interests, which can be useful in preventing dementia. For example, the suggestion unit analyzes the user's hobbies and interests and suggests new hobbies and activities. For example, it can suggest books of a new genre to a user who likes reading. The suggestion unit also suggests activities that are effective in preventing dementia based on the hobbies and interests. For example, it can suggest brain training such as puzzles and crossword puzzles. The suggestion unit also analyzes the user's hobbies and interests and suggests activities to increase social connections. For example, it can suggest ballroom dancing or volunteer work. In this way, it is possible to suggest new hobbies and activities based on the hobbies and interests, which can be useful in preventing dementia.

[0069] The suggestion unit can suggest relaxation methods that match the user's lifestyle rhythm. The suggestion unit, for example, analyzes the user's lifestyle rhythm and suggests appropriate relaxation methods. For example, it suggests evening relaxation methods for a night-owl user. The suggestion unit also suggests relaxation methods that are effective in reducing stress based on the user's lifestyle rhythm. For example, it suggests deep breathing techniques and meditation. The suggestion unit also analyzes the user's lifestyle rhythm and suggests relaxation methods. For example, it suggests short relaxation methods that can be done in between work. In this way, it is possible to suggest relaxation methods that match the user's lifestyle rhythm.

[0070] The suggestion unit can use the emotion estimation function to analyze the emotional state of the user when taking a specific preventive measure and suggest the optimal timing for taking it. The suggestion unit, for example, analyzes the emotional state of the user when exercising and suggests taking it at the optimal timing. For example, it suggests exercising during a time when positive emotions are high. The suggestion unit can also use the emotion estimation function to analyze the emotional state of the user when taking a relaxation method and suggest taking it at the optimal timing. For example, it suggests relaxation during a time when stress is high. The suggestion unit can also analyze the emotional state of the user when taking a hobby activity and suggest taking it at the optimal timing. For example, it suggests hobby activity during a time when positive emotions are high. In this way, it is possible to analyze the emotional state of the user when taking a specific preventive measure and suggest taking it at the optimal timing.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The dementia prediction system can further analyze the user's sleep data and evaluate the impact of sleep quality on dementia risk. For example, the data collection unit monitors the user's sleep duration and sleep depth, and the analysis unit evaluates sleep quality based on this data. The prediction unit predicts that poor sleep quality may increase the risk of dementia, and the suggestion unit suggests measures to improve sleep quality. For example, it can suggest relaxing herbal tea or stretching before bed.

[0073] The dementia prediction system can further analyze the user's emotional state and evaluate the impact of emotional fluctuations on dementia risk. For example, the data collection unit calculates an emotional score for the user's social media posts and comments, and the analysis unit evaluates emotional fluctuations based on this data. The prediction unit predicts that the risk of dementia may increase if emotional fluctuations are large, and the suggestion unit proposes measures to stabilize emotions. For example, it can suggest stress management methods and relaxation techniques.

[0074] The dementia prediction system can further analyze the user's exercise data and evaluate the impact of exercise habits on dementia risk. For example, the data collection unit monitors the user's exercise frequency and intensity, and the analysis unit evaluates the user's exercise habits based on this data. The prediction unit predicts the possibility that a user's dementia risk will increase if they do not exercise regularly, and the suggestion unit suggests measures to improve the user's exercise habits. For example, it can suggest exercise programs such as walking or yoga.

[0075] The dementia prediction system can further analyze the user's dietary data to evaluate the impact of nutritional balance on dementia risk. For example, the data collection unit monitors the user's dietary content and calorie intake, and the analysis unit evaluates nutritional balance based on this data. The prediction unit predicts the possibility that poor nutritional balance will increase the risk of dementia, and the suggestion unit suggests measures to improve nutritional balance. For example, it can suggest foods rich in vitamin D and omega-3 fatty acids.

[0076] The dementia prediction system can further analyze a user's social connections and evaluate the impact of isolation on dementia risk. For example, the data collection unit monitors the user's social media activity and online behavior, and the analysis unit evaluates social connections based on this data. The prediction unit predicts that a high level of isolation may increase the risk of dementia, and the suggestion unit suggests measures to increase social connections. For example, ballroom dancing or volunteer activities could be suggested.

[0077] The dementia prediction system can further analyze the user's emotional state and evaluate the impact of emotional fluctuations on dementia risk. For example, the data collection unit collects the user's voice data, and the analysis unit analyzes changes in voice tone and speaking style. The prediction unit predicts that the risk of dementia may increase if emotional fluctuations are large, and the suggestion unit suggests measures to stabilize emotions. For example, it can suggest relaxing music or meditation.

[0078] The dementia prediction system can further analyze the health data of the user's pet and evaluate the impact of interaction with the pet on the risk of dementia. For example, the data collection unit monitors the pet's activity level and food intake, and the analysis unit evaluates the interaction with the pet based on this data. The prediction unit predicts that little interaction with the pet may increase the risk of dementia, and the suggestion unit suggests measures to increase interaction with the pet. For example, it can suggest taking the pet for walks or playing.

[0079] The dementia prediction system can further analyze the user's emotional state and evaluate the impact of emotional fluctuations on dementia risk. For example, the data collection unit captures the user's facial expressions, and the analysis unit quantifies the changes in facial expressions. The prediction unit predicts that the risk of dementia may increase if the emotional fluctuations are large, and the suggestion unit proposes measures to stabilize emotions. For example, it can suggest relaxing aromatherapy or massage.

[0080] The dementia prediction system can further analyze the user's work environment data to evaluate the impact of workplace stress levels and interpersonal relationships on dementia risk. For example, the data collection unit collects the user's work environment data (e.g., stress levels and interpersonal relationships), and the analysis unit evaluates the work environment based on this data. The prediction unit predicts the possibility that a high workplace stress level will increase the risk of dementia, and the suggestion unit suggests stress management methods and measures to improve the work environment. For example, it can suggest relaxation techniques and improving communication skills.

[0081] The dementia prediction system can also analyze a user's emotional state and evaluate the impact of emotional fluctuations on dementia risk. For example, the data collection unit monitors the user's social media activity and online behavior, and the analysis unit calculates an emotional score for the content of posts and comments. The prediction unit predicts that large emotional fluctuations may increase the risk of dementia, and the suggestion unit suggests measures to stabilize emotions. For example, it can suggest stress management methods and relaxation techniques.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection unit collects personal data about the user. For example, the data collection unit collects data such as medical history, vital signs (heart rate, blood pressure, etc.), dietary habits (calorie intake, nutritional balance, etc.), exercise history (exercise frequency, type of exercise, etc.), sleep (sleep time, sleep quality, etc.), daily schedule (time allocation for work and hobbies, etc.), hobbies and preferences (favorite activities and areas of interest, etc.), personality (personality assessment results, etc.), family structure (number of family members and relationships, etc.), pets (types and number of pets, etc.), friends (breadth and depth of friendships, etc.), and living environment (area of ​​residence, housing conditions, etc.). Step 2: The analysis unit analyzes the personal data collected by the data collection unit. For example, the analysis unit may analyze the data using data mining technology to evaluate the user's health condition and lifestyle habits. The analysis unit may also analyze the data using statistical analysis technology to identify the user's risk factors. The analysis unit may also analyze the data using machine learning algorithms to build a model for predicting the user's dementia risk. Step 3: The prediction unit predicts the dementia risk based on the data analyzed by the analysis unit. For example, the prediction unit may use a scoring system to assess the user's dementia risk. The prediction unit may also weight risk factors to predict the user's dementia risk. The prediction unit may also use a machine learning model to predict the user's dementia risk. Step 4: The suggestion unit suggests preventive measures based on the dementia risk predicted by the prediction unit. For example, the suggestion unit suggests preventive exercises (walking, yoga, etc.). The suggestion unit can also suggest lifestyle changes (regular living, stress management, etc.). The suggestion unit can also suggest supplements or Chinese herbal medicines (vitamin D, omega-3 fatty acids, etc.). The suggestion unit can also suggest hobby activities (reading, puzzles, ballroom dancing, etc.). This allows the user to prevent the onset of dementia by practicing the suggested preventive measures. The suggestion unit can also guide the user to service providers. For example, the suggestion unit can introduce fitness clubs and yoga studios related to preventive exercises. The suggestion unit can also introduce pharmacies and online shops that sell supplements and Chinese herbal medicines. The suggestion unit can also introduce classes and events related to hobby activities. This allows the user to easily use the services they need.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects personal data; an analysis unit that analyzes the personal data collected by the data collection unit; a prediction unit that predicts a dementia risk based on the data analyzed by the analysis unit; a proposal unit that proposes preventive measures based on the dementia risk predicted by the prediction unit. A system characterized by:

2. The data collection unit Analyzing the user's emotional state in real time and assessing the impact of emotional fluctuations on the risk of dementia 2. The system of claim 1.

3. The data collection unit Analyzes the user's voice data and assesses stress levels and mental state based on changes in tone and speaking style 2. The system of claim 1.

4. The data collection unit Analyzing users' social networking activities or online behavior to assess their social connectedness or isolation 2. The system of claim 1.

5. The data collection unit Analyzing data collected from IoT devices in the home to evaluate daily rhythms and health conditions 2. The system of claim 1.

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