system

Abstract

The system according to the embodiment aims to suggest appropriate exercises and community activities based on the living situation and physical condition of the elderly person. [Solution] A system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses. The analysis unit analyzes the information collected by the data collection unit. The proposal unit proposes exercises and community activities based on the analysis results obtained by the analysis unit. The monitoring unit monitors the effects of the exercises and community activities proposed by the proposal unit.

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 technologies do not adequately suggest appropriate exercise and community activities based on the living situation and physical condition of elderly people, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate exercises and community activities based on the living situation and physical condition of the elderly person. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses. The analysis unit analyzes the information collected by the data collection unit. The proposal unit proposes exercises and community activities based on the analysis results obtained by the analysis unit. The monitoring unit monitors the effects of the exercises and community activities proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate exercises and community activities based on the living situation and physical condition of the elderly person. [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) An advice system according to an embodiment of the present invention collects information about an elderly person's living situation, physical condition, chronic illnesses, etc., analyzes it using a generation AI, proposes optimal exercises and community activities, and monitors their effectiveness. The advice system collects information about an elderly person's living situation, physical condition, chronic illnesses, etc., analyzes it using a generation AI, and proposes optimal exercises and community activities. Furthermore, the advice system monitors the effectiveness of the proposed exercises and community activities and updates the advice as necessary. For example, the advice system collects information about an elderly person's daily activity level, diet, sleep patterns, medical history, etc. The advice system then analyzes the collected information using a generation AI. The collected information is input to the generation AI, which then analyzes it based on that information. For example, the generation AI receives a prompt such as "Please suggest the best exercise for this elderly person," and outputs the analysis results. Based on the analysis results, the generation AI then proposes light walking, stretching, local club activities, etc. The advice system then monitors the effectiveness of the proposed exercises and community activities. This makes it easier for elderly people to find exercises and community activities that suit them, improving their quality of life. Furthermore, implementation by local governments increases credibility, allowing elderly people to accept advice with confidence. This allows the advice system to extend the healthy lifespan of the elderly and improve their quality of life. For example, generative AI can be used at local government health consultation centers to provide individualized advice to the elderly. In this way, advice systems using generative AI can be an effective means of extending the healthy lifespan of the elderly and improving their quality of life.

[0029] The advice system according to the embodiment includes a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses. The information on the elderly person's living situation, physical condition, and chronic illnesses includes, but is not limited to, daily activity level, dietary content, sleep patterns, and medical history. For example, the data collection unit collects pedometer data to determine daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. The data collection unit can also collect medical records to determine medical history. For example, the data collection unit collects pedometer data to determine daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. The analysis unit analyzes the collected information using a generation AI. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit performs statistical analysis of the data to analyze the collected information. The analysis unit can also use a machine learning algorithm to analyze the collected information. The analysis unit can also use a generation AI to analyze the collected information. For example, the analysis unit performs statistical analysis of the data to analyze the collected information. The analysis unit can also use a machine learning algorithm to analyze the collected information. The analysis unit can also use a generation AI to analyze the collected information. The suggestion unit uses the generation AI to suggest exercises and community activities based on the analysis results. Suggestions are made based on, for example, the type of exercise and the content of the community activity, but are not limited to such examples. For example, the suggestion unit suggests light walking, stretching, local club activities, etc. based on the analysis results. The suggestion unit can also suggest the type of exercise and the content of the community activity based on the analysis results. The suggestion unit can also use the generation AI to suggest exercises and community activities based on the analysis results. For example, the suggestion unit suggests light walking, stretching, local club activities, etc. based on the analysis results.The suggestion unit can also suggest types of exercise and content of community activities based on the analysis results. The suggestion unit can also suggest exercises and community activities based on the analysis results using a generation AI. The monitoring unit monitors the effects of the proposed exercises and community activities. Monitoring is performed, for example, based on an effect measurement method and a monitoring frequency, but is not limited to such examples. For example, the monitoring unit monitors the effects of the proposed exercises and community activities based on an effect measurement method. The monitoring unit can also monitor the effects of the proposed exercises and community activities based on the monitoring frequency. The monitoring unit can also monitor the effects of the proposed exercises and community activities using a generation AI. For example, the monitoring unit monitors the effects of the proposed exercises and community activities based on an effect measurement method. The monitoring unit can also monitor the effects of the proposed exercises and community activities based on the monitoring frequency. The monitoring unit can also monitor the effects of the proposed exercises and community activities using a generation AI. As a result, the advice system according to the embodiment can propose optimal exercises and community activities based on the elderly person's living situation and physical condition, and monitor the effects, thereby extending healthy life expectancy.

[0030] The data collection unit can collect information such as the elderly person's daily activity level, dietary content, sleep patterns, and medical history. Examples of the daily activity level, dietary content, sleep patterns, and medical history include, but are not limited to, pedometer data, meal records, sleep tracker data, and medical records. For example, the data collection unit collects pedometer data to determine the elderly person's daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. The data collection unit can also collect medical records to determine medical history. For example, the data collection unit collects pedometer data to determine the elderly person's daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. By collecting information such as the elderly person's daily activity level, dietary content, sleep patterns, and medical history, analysis can be performed based on more detailed data. Some or all of the above-described processing in the data collection unit may be performed, for example, using or without a generation AI. For example, the data collection unit can input pedometer data into the generation AI and have the generation AI analyze daily activity levels.

[0031] The analysis unit allows the generation AI to perform analysis based on the collected information. The generation AI may perform analysis using technologies such as, but not limited to, natural language processing, image recognition, and predictive models. The analysis unit may analyze the collected information using, for example, natural language processing. The analysis unit may also analyze the collected information using image recognition. The analysis unit may also analyze the collected information using predictive models. For example, the analysis unit may analyze the collected information using natural language processing. The analysis unit may also analyze the collected information using image recognition. The analysis unit may also analyze the collected information using predictive models. This improves the accuracy of the analysis of the collected information by using the generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the collected information to the generation AI and cause the generation AI to output the analysis results.

[0032] The suggestion unit can suggest walking, stretching, or local club activities based on the analysis results. Examples of walking, stretching, and local club activities include, but are not limited to, walking distance and time, types of stretching, and details of club activities. The suggestion unit can suggest, for example, light walking, stretching, or local club activities based on the analysis results. The suggestion unit can also suggest walking distance and time, types of stretching, and details of club activities based on the analysis results. The suggestion unit can also use a generation AI to suggest walking, stretching, or local club activities based on the analysis results. For example, the suggestion unit can suggest light walking, stretching, or local club activities based on the analysis results. The suggestion unit can also suggest walking distance and time, types of stretching, and details of club activities based on the analysis results. The suggestion unit can also use a generation AI to suggest walking, stretching, or local club activities based on the analysis results. This allows for specific exercises and community activities to be suggested based on the analysis results, making suggestions that are easy for elderly people to follow. Some or all of the above-described processing by the suggestion unit can be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input the analysis results into the generation AI and have the generation AI output the proposal content.

[0033] The monitoring unit can monitor the effectiveness of the proposed exercise or community activity and update the advice as necessary. Methods of monitoring the effectiveness include, but are not limited to, changes in health indicators, participation frequency, and feedback collection methods. For example, the monitoring unit can monitor changes in health indicators and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also monitor participation frequency and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also collect feedback and evaluate the effectiveness of the proposed exercise or community activity. For example, the monitoring unit can monitor changes in health indicators and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also monitor participation frequency and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also collect feedback and evaluate the effectiveness of the proposed exercise or community activity. This enables continuous health management by monitoring the effectiveness of the proposed exercise or community activity and updating the advice as necessary. Some or all of the above-described processing in the monitoring unit can be performed, for example, using a generation AI or without using a generation AI. For example, the monitoring unit can input health index data into the generation AI and have the generation AI evaluate the effectiveness.

[0034] The data collection unit can analyze the elderly person's past data collection history and select an optimal collection method. Examples of optimal collection methods include, but are not limited to, the frequency of data collection, the device used, and the timing of collection. For example, the data collection unit selects a method that yields the most accurate data from the past data collection history. The data collection unit can also adjust the frequency of data collection based on the past data collection history. The data collection unit can also analyze the past data collection history and optimize the timing of data collection. For example, the data collection unit selects a method that yields the most accurate data from the past data collection history. The data collection unit can also adjust the frequency of data collection based on the past data collection history. The data collection unit can also analyze the past data collection history and optimize the timing of data collection. By selecting an optimal collection method based on the past data collection history, the accuracy of data collection is improved. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit can input the past data collection history into the generation AI and have the generation AI select an optimal collection method.

[0035] The data collection unit may filter data based on the elderly person's current living situation and areas of interest during data collection. Filtering criteria include, but are not limited to, the type of data to be collected, its importance, and relevance. For example, the data collection unit may prioritize collecting data related to activities in which the elderly person is interested. The data collection unit may also collect only necessary data depending on the elderly person's living situation. The data collection unit may also narrow down the targets of data collection based on the elderly person's areas of interest. For example, the data collection unit may prioritize collecting data related to activities in which the elderly person is interested. The data collection unit may also collect only necessary data depending on the elderly person's living situation. The data collection unit may also narrow down the targets of data collection based on the elderly person's areas of interest. By filtering data based on the elderly person's living situation and areas of interest, only necessary data can be collected. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit may input data related to the elderly person's areas of interest into the generation AI and have the generation AI perform filtering.

[0036] During data collection, the data collection unit can select the optimal collection means depending on the input method of the elderly person. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the elderly person prefers voice input, the data collection unit can collect data by voice. Furthermore, if the elderly person prefers text input, the data collection unit can also collect data by text. Furthermore, if the elderly person prefers image input, the data collection unit can also collect data by image. For example, if the elderly person prefers voice input, the data collection unit can collect data by voice. Furthermore, if the elderly person prefers text input, the data collection unit can also collect data by text. Furthermore, if the elderly person prefers image input, the data collection unit can also collect data by image. This improves the efficiency of data collection by selecting the optimal collection means depending on the elderly person's input method. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit can input the elderly person's input data into the generation AI and have the generation AI select the optimal collection means.

[0037] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the elderly person. Examples of geographical location information include, but are not limited to, GPS data, address information, and movement history. For example, when the elderly person is at home, the data collection unit prioritizes collecting data related to activities at home. Furthermore, when the elderly person is out, the data collection unit can prioritize collecting data related to activities while out. Furthermore, when the elderly person is in a specific facility, the data collection unit can prioritize collecting data related to activities at the facility. For example, when the elderly person is at home, the data collection unit prioritizes collecting data related to activities at home. Furthermore, when the elderly person is out, the data collection unit can prioritize collecting data related to activities while out. Furthermore, when the elderly person is in a specific facility, the data collection unit can prioritize collecting data related to activities at the facility. In this way, by collecting data by taking into account the geographical location information of the elderly person, highly relevant data can be prioritized. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the data collection unit can input the geographic location information of elderly people into the generation AI and have the generation AI collect highly relevant data.

[0038] During data collection, the data collection unit may analyze the social media activities of the elderly person and collect related data. Social media activities include, but are not limited to, post content, comments, and the number of likes. For example, the data collection unit may collect data related to activities shared by the elderly person on social media. The data collection unit may also analyze the content of posts by the elderly person on social media and collect related data. The data collection unit may also collect related data by referring to the activities of the elderly person's friends on social media. For example, the data collection unit may collect data related to activities shared by the elderly person on social media. The data collection unit may also analyze the content of posts by the elderly person on social media and collect related data. The data collection unit may also collect related data by referring to the activities of the elderly person's friends on social media. This allows for efficient collection of related data by analyzing the social media activities of the elderly person. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit may input data on the elderly person's social media activities into the generation AI and cause the generation AI to collect related data.

[0039] The data collection unit can customize the data collection method by reflecting the elderly person's past feedback during data collection. Examples of past feedback include, but are not limited to, survey results, user comments, and ratings. The data collection unit can adjust the data collection method, for example, based on feedback previously provided by the elderly person. The data collection unit can also adjust the frequency of data collection by reflecting the elderly person's past feedback. The data collection unit can also optimize the target of data collection based on the elderly person's past feedback. For example, the data collection unit can adjust the data collection method based on the elderly person's past feedback. The data collection unit can also adjust the frequency of data collection by reflecting the elderly person's past feedback. The data collection unit can also optimize the target of data collection based on the elderly person's past feedback. Thus, the data collection method can be optimized by reflecting the elderly person's past feedback. Some or all of the above-described processing in the data collection unit can be performed using, or without, a generation AI. For example, the data collection unit can input the elderly person's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Criteria for the importance of data include, but are not limited to, health risk, urgency, and relevance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, health data, activity data, and environmental data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply an activity-related analysis algorithm to activity data. The analysis unit can also apply a social-related analysis algorithm to social data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply an activity-related analysis algorithm to activity data. The analysis unit can also apply a social-related analysis algorithm to social data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the elderly person. Past analysis results include, but are not limited to, past health checkup results, exercise history, and dietary records. For example, the analysis unit corrects the current analysis result based on the elderly person's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the elderly person's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the elderly person's past analysis results. For example, the analysis unit corrects the current analysis result based on the elderly person's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the elderly person's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the elderly person's past analysis results. In this way, the accuracy of the analysis is improved by referring to the elderly person's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the elderly person's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The data collection time includes, but is not limited to, the latest data, past data, seasonal data, etc. The analysis unit, for example, prioritizes the analysis of the latest data. The analysis unit can also analyze current data with reference to past data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also analyze current data with reference to past data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the latest data can be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, correlation, causal relationship, and co-occurrence relationship. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the elderly person's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the elderly person has technical expertise, the analysis unit can provide the analysis results using technical terms. Furthermore, if the elderly person does not have technical expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the elderly person's level of expertise. For example, if the elderly person has technical expertise, the analysis unit can provide the analysis results using technical terms. Furthermore, if the elderly person does not have technical expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the elderly person's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the elderly person's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the elderly person's level of expertise into the generation AI and have the generation AI adjust the use of technical terms in the analysis.

[0046] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the activity. Criteria for the importance of an activity include, but are not limited to, health benefits, social impact, and personal preferences. For example, the suggestion unit can provide detailed suggestions for highly important activities. The suggestion unit can also provide brief suggestions for less important activities. The suggestion unit can also determine the priority of the suggestion based on the importance of the activity. For example, the suggestion unit can provide detailed suggestions for highly important activities. The suggestion unit can also provide brief suggestions for less important activities. The suggestion unit can also determine the priority of the suggestion based on the importance of the activity. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the activity. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the importance of the activity to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0047] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the activity category. Examples of activity categories include, but are not limited to, exercise, hobbies, and social activities. For example, the suggestion unit can apply an exercise-related suggestion algorithm to exercise. The suggestion unit can also apply a community-related suggestion algorithm to community activities. The suggestion unit can also apply a health management-related suggestion algorithm to health management. For example, the suggestion unit can apply an exercise-related suggestion algorithm to exercise. The suggestion unit can also apply a community-related suggestion algorithm to community activities. The suggestion unit can also apply a health management-related suggestion algorithm to health management. By applying different suggestion algorithms depending on the activity category, the accuracy of the suggestion is improved. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the activity category to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0048] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the elderly person's past proposal results. Past proposal results include, but are not limited to, for example, implementation status, effect evaluation, and feedback. For example, the suggestion unit corrects the current proposal based on the elderly person's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the elderly person's past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the elderly person's past proposal results. For example, the suggestion unit corrects the current proposal based on the elderly person's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the elderly person's past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the elderly person's past proposal results. In this way, the accuracy of the proposal is improved by referring to the elderly person's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the elderly person's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0049] When making a proposal, the suggestion unit can determine the priority of the proposal based on the timing of the activity. Examples of the timing of the activity include, but are not limited to, the season, the time of day, and the timing of an event. For example, the suggestion unit can prioritize the proposal for an activity to be performed soon. The suggestion unit can also postpone the proposal for an activity to be performed over a long period of time. The suggestion unit can also adjust the priority of the proposal depending on the timing of the activity. For example, the suggestion unit can prioritize the proposal for an activity to be performed soon. The suggestion unit can also postpone the proposal for an activity to be performed over a long period of time. The suggestion unit can also adjust the priority of the proposal depending on the timing of the activity. In this way, determining the priority of the proposal based on the timing of the activity enables efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the timing of the activity to the generation AI and cause the generation AI to determine the priority of the proposal.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of activities when making suggestions. Examples of the relevance of activities include, but are not limited to, correlation, causal relationship, and co-occurrence relationship. For example, the suggestion unit prioritizes suggesting highly related activities. The suggestion unit can also postpone less related activities. The suggestion unit can also optimize the order of suggestions based on the relevance of activities. For example, the suggestion unit prioritizes suggesting highly related activities. The suggestion unit can also postpone less related activities. The suggestion unit can also optimize the order of suggestions based on the relevance of activities. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of activities. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the relevance of activities to the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the elderly person's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and expert. For example, if the elderly person has technical expertise, the suggestion unit can provide the proposal using technical terms. Furthermore, if the elderly person does not have technical expertise, the suggestion unit can provide the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the elderly person's level of expertise. For example, if the elderly person has technical expertise, the suggestion unit can provide the proposal using technical terms. Furthermore, if the elderly person does not have technical expertise, the suggestion unit can provide the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the elderly person's level of expertise. In this way, by adjusting the use of technical terms in the proposal according to the elderly person's level of expertise, it is possible to provide an easy-to-understand proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the elderly person's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the proposal.

[0052] During monitoring, the monitoring unit can optimize the current monitoring method by referring to past monitoring data. Past monitoring data includes, but is not limited to, past health indicators, exercise history, and diet records. For example, the monitoring unit adjusts the current monitoring method based on the past monitoring data. The monitoring unit can also optimize the monitoring frequency by referring to the past monitoring data. The monitoring unit can also optimize the monitoring target by using the past monitoring data. For example, the monitoring unit adjusts the current monitoring method based on the past monitoring data. The monitoring unit can also optimize the monitoring frequency by referring to the past monitoring data. The monitoring unit can also optimize the monitoring target by using the past monitoring data. In this way, optimizing the current monitoring method based on the past monitoring data improves the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input past monitoring data into the generation AI and cause the generation AI to optimize the current monitoring method.

[0053] During monitoring, the monitoring unit can update the monitoring data by reflecting feedback from the elderly. Examples of feedback include, but are not limited to, survey results, user comments, and ratings. For example, the monitoring unit updates the monitoring data based on feedback provided by the elderly. The monitoring unit can also adjust the frequency of monitoring by reflecting the feedback from the elderly. The monitoring unit can also optimize the target of monitoring based on the feedback from the elderly. For example, the monitoring unit updates the monitoring data based on feedback provided by the elderly. The monitoring unit can also adjust the frequency of monitoring by reflecting the feedback from the elderly. The monitoring unit can also optimize the target of monitoring based on the feedback from the elderly. This allows the monitoring data to be kept up to date by reflecting the feedback from the elderly. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the feedback from the elderly into the generation AI and cause the generation AI to update the monitoring data.

[0054] During monitoring, the monitoring unit can analyze the elderly person's lifestyle rhythm and suggest optimal monitoring timing. Lifestyle rhythms include, but are not limited to, sleep patterns, meal times, and activity times. The monitoring unit can optimize the monitoring timing based on the elderly person's lifestyle rhythm. The monitoring unit can also analyze the elderly person's activity patterns and suggest optimal monitoring timing. The monitoring unit can also adjust the monitoring frequency based on the elderly person's lifestyle rhythm. For example, the monitoring unit can optimize the monitoring timing based on the elderly person's lifestyle rhythm. The monitoring unit can also analyze the elderly person's activity patterns and suggest optimal monitoring timing. The monitoring unit can also adjust the monitoring frequency based on the elderly person's lifestyle rhythm. This enables efficient monitoring by optimizing the monitoring timing based on the elderly person's lifestyle rhythm. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input the elderly person's lifestyle rhythm data into the generation AI and have the generation AI suggest monitoring timing.

[0055] During monitoring, the monitoring unit can weight the monitoring data based on the time when the activity was performed. Examples of the time when the activity was performed include, but are not limited to, seasons, time periods, and event timing. For example, the monitoring unit can assign a higher weight to the most recently performed activity. The monitoring unit can also assign a lower weight to the activity performed over a long period of time. The monitoring unit can also adjust the weighting of the monitoring data according to the time when the activity was performed. For example, the monitoring unit can assign a higher weight to the most recently performed activity. The monitoring unit can also assign a lower weight to the activity performed over a long period of time. The monitoring unit can also adjust the weighting of the monitoring data according to the time when the activity was performed. In this way, by weighting the monitoring data based on the time when the activity was performed, important data can be prioritized for monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data on the time when the activity was performed to the generation AI and cause the generation AI to weight the monitoring data.

[0056] During monitoring, the monitoring unit can integrate information from different data sources to enrich the monitoring data. Examples of different data sources include, but are not limited to, wearable devices, medical records, and social media. For example, the monitoring unit can integrate data from health devices to enrich the monitoring data. The monitoring unit can also integrate data from social media to enrich the monitoring data. The monitoring unit can also integrate data from medical institutions to enrich the monitoring data. For example, the monitoring unit can integrate data from health devices to enrich the monitoring data. The monitoring unit can also integrate data from social media to enrich the monitoring data. The monitoring unit can also integrate data from medical institutions to enrich the monitoring data. In this way, by integrating information from different data sources, the monitoring data can be enriched and more detailed monitoring can be performed. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input information from different data sources into the generation AI and have the generation AI integrate the monitoring data.

[0057] The monitoring unit can perform monitoring while taking into account the geographical location information of the elderly person. Geographical location information includes, but is not limited to, GPS data, address information, and movement history. For example, when the elderly person is at home, the monitoring unit monitors activities at home. Furthermore, when the elderly person is out, the monitoring unit can monitor activities while away from home. Furthermore, when the elderly person is in a specific facility, the monitoring unit can monitor activities at the facility. For example, when the elderly person is at home, the monitoring unit monitors activities at home. Furthermore, when the elderly person is out, the monitoring unit can monitor activities while away from home. Furthermore, when the elderly person is in a specific facility, the monitoring unit can monitor activities at the facility. This enables more appropriate monitoring by taking into account the geographical location information of the elderly person. Some or all of the above-described processing by the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the geographical location information of the elderly person to the generation AI and have the generation AI perform monitoring.

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

[0059] The data collection unit can also understand the hobbies and interests of the elderly person and customize the content of data collection based on that. For example, if the elderly person is interested in gardening, gardening-related activity data can be prioritized for collection. If the elderly person enjoys music, music-related activity data can also be collected. Furthermore, if the elderly person likes to travel, travel-related data can be collected and their health status can be monitored during travel. This allows for more personalized advice by customizing data collection based on the elderly person's hobbies and interests.

[0060] The analysis unit can assess the elderly person's level of social isolation based on the collected information and, if necessary, enhance recommendations for community activities. For example, the analysis unit can analyze the elderly person's frequency of interactions with friends and family from the collected data, and if the level of isolation is high, suggest local club activities or volunteer activities. The analysis unit can also analyze social media activity data and encourage participation in online communities. Furthermore, the analysis unit can suggest support through regular phone calls or visits based on the results of the isolation assessment. This can prevent social isolation among the elderly and maintain their mental health.

[0061] The suggestion unit can also propose meal plans to improve the nutritional status of elderly people based on the analysis results. For example, the suggestion unit can analyze the collected food records and, if the nutritional balance is unbalanced, propose a balanced meal plan. The suggestion unit can also propose appropriate ingredients and recipes taking into account the elderly person's chronic illnesses and allergy information. Furthermore, the suggestion unit can also suggest recipes that use local ingredients and encourage people to enjoy local food culture. This can improve the nutritional status of elderly people and maintain their health.

[0062] The monitoring unit can also utilize a biofeedback device when monitoring the effectiveness of the suggested exercise or community activity. For example, the monitoring unit collects biofeedback data, such as heart rate, blood pressure, and stress level, to evaluate the effectiveness of the suggested activity. The monitoring unit can also adjust the intensity and frequency of exercise based on the biofeedback data. Furthermore, the monitoring unit can provide biofeedback data in real time to enable the elderly person to understand their own health condition. This allows the effectiveness of the suggested activity to be monitored more accurately and appropriate advice to be provided.

[0063] When monitoring the effectiveness of proposed exercise or community activities, the monitoring unit can also integrate information from different data sources to enrich the monitoring data. For example, data from wearable devices, medical records, social media activity data, etc. can be integrated to conduct more detailed monitoring. The monitoring unit can also integrate information from different data sources in real time to evaluate the effectiveness immediately. Furthermore, the monitoring unit can comprehensively evaluate the effectiveness of the proposal based on information from different data sources and update advice as necessary. In this way, integrating information from different data sources enriches the monitoring data and enables more accurate effectiveness evaluation.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses. Specifically, information such as daily activity level, dietary content, sleep patterns, and medical history is collected. For example, pedometer data is collected to understand daily activity level, food records are collected to understand dietary content, sleep tracker data is collected to understand sleep patterns, and medical records are collected to understand medical history. Step 2: The analysis unit uses the generative AI to analyze the collected information. This analysis is carried out using statistical analysis of the data and machine learning algorithms. This allows the analysis to identify patterns and trends in the collected information and provide detailed analysis results on the elderly's living conditions and physical condition. Step 3: The suggestion unit uses generative AI to suggest exercises and community activities based on the analysis results. Suggestions are made based on the type of exercise and the content of the community activity. For example, light walking, stretching, or local club activities are suggested. Step 4: The Monitoring Department monitors the effectiveness of the proposed exercise and community activities. Monitoring is carried out based on the effectiveness measurement method and monitoring frequency. This will evaluate the impact of the proposed exercise and community activities on the health of the elderly and adjust the proposals as necessary.

[0066] (Example 2) An advice system according to an embodiment of the present invention collects information about an elderly person's living situation, physical condition, chronic illnesses, etc., analyzes it using a generation AI, proposes optimal exercises and community activities, and monitors their effectiveness. The advice system collects information about an elderly person's living situation, physical condition, chronic illnesses, etc., analyzes it using a generation AI, and proposes optimal exercises and community activities. Furthermore, the advice system monitors the effectiveness of the proposed exercises and community activities and updates the advice as necessary. For example, the advice system collects information about an elderly person's daily activity level, diet, sleep patterns, medical history, etc. The advice system then analyzes the collected information using a generation AI. The collected information is input to the generation AI, which then analyzes it based on that information. For example, the generation AI receives a prompt such as "Please suggest the best exercise for this elderly person," and outputs the analysis results. Based on the analysis results, the generation AI then proposes light walking, stretching, local club activities, etc. The advice system then monitors the effectiveness of the proposed exercises and community activities. This makes it easier for elderly people to find exercises and community activities that suit them, improving their quality of life. Furthermore, implementation by local governments increases credibility, allowing elderly people to accept advice with confidence. This allows the advice system to extend the healthy lifespan of the elderly and improve their quality of life. For example, generative AI can be used at local government health consultation centers to provide individualized advice to the elderly. In this way, advice systems using generative AI can be an effective means of extending the healthy lifespan of the elderly and improving their quality of life.

[0067] The advice system according to the embodiment includes a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses. The information on the elderly person's living situation, physical condition, and chronic illnesses includes, but is not limited to, daily activity level, dietary content, sleep patterns, and medical history. For example, the data collection unit collects pedometer data to determine daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. The data collection unit can also collect medical records to determine medical history. For example, the data collection unit collects pedometer data to determine daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. The analysis unit analyzes the collected information using a generation AI. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit performs statistical analysis of the data to analyze the collected information. The analysis unit can also use a machine learning algorithm to analyze the collected information. The analysis unit can also use a generation AI to analyze the collected information. For example, the analysis unit performs statistical analysis of the data to analyze the collected information. The analysis unit can also use a machine learning algorithm to analyze the collected information. The analysis unit can also use a generation AI to analyze the collected information. The suggestion unit uses the generation AI to suggest exercises and community activities based on the analysis results. Suggestions are made based on, for example, the type of exercise and the content of the community activity, but are not limited to such examples. For example, the suggestion unit suggests light walking, stretching, local club activities, etc. based on the analysis results. The suggestion unit can also suggest the type of exercise and the content of the community activity based on the analysis results. The suggestion unit can also use the generation AI to suggest exercises and community activities based on the analysis results. For example, the suggestion unit suggests light walking, stretching, local club activities, etc. based on the analysis results.The suggestion unit can also suggest types of exercise and content of community activities based on the analysis results. The suggestion unit can also suggest exercises and community activities based on the analysis results using a generation AI. The monitoring unit monitors the effects of the proposed exercises and community activities. Monitoring is performed, for example, based on an effect measurement method and a monitoring frequency, but is not limited to such examples. For example, the monitoring unit monitors the effects of the proposed exercises and community activities based on an effect measurement method. The monitoring unit can also monitor the effects of the proposed exercises and community activities based on the monitoring frequency. The monitoring unit can also monitor the effects of the proposed exercises and community activities using a generation AI. For example, the monitoring unit monitors the effects of the proposed exercises and community activities based on an effect measurement method. The monitoring unit can also monitor the effects of the proposed exercises and community activities based on the monitoring frequency. The monitoring unit can also monitor the effects of the proposed exercises and community activities using a generation AI. As a result, the advice system according to the embodiment can propose optimal exercises and community activities based on the elderly person's living situation and physical condition, and monitor the effects, thereby extending healthy life expectancy.

[0068] The data collection unit can collect information such as the elderly person's daily activity level, dietary content, sleep patterns, and medical history. Examples of the daily activity level, dietary content, sleep patterns, and medical history include, but are not limited to, pedometer data, meal records, sleep tracker data, and medical records. For example, the data collection unit collects pedometer data to determine the elderly person's daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. The data collection unit can also collect medical records to determine medical history. For example, the data collection unit collects pedometer data to determine the elderly person's daily activity level. The data collection unit can also collect meal records to determine dietary content. The data collection unit can also collect sleep tracker data to determine sleep patterns. By collecting information such as the elderly person's daily activity level, dietary content, sleep patterns, and medical history, analysis can be performed based on more detailed data. Some or all of the above-described processing in the data collection unit may be performed, for example, using or without a generation AI. For example, the data collection unit can input pedometer data into the generation AI and have the generation AI analyze daily activity levels.

[0069] The analysis unit allows the generation AI to perform analysis based on the collected information. The generation AI may perform analysis using technologies such as, but not limited to, natural language processing, image recognition, and predictive models. The analysis unit may analyze the collected information using, for example, natural language processing. The analysis unit may also analyze the collected information using image recognition. The analysis unit may also analyze the collected information using predictive models. For example, the analysis unit may analyze the collected information using natural language processing. The analysis unit may also analyze the collected information using image recognition. The analysis unit may also analyze the collected information using predictive models. This improves the accuracy of the analysis of the collected information by using the generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the collected information to the generation AI and cause the generation AI to output the analysis results.

[0070] The suggestion unit can suggest walking, stretching, or local club activities based on the analysis results. Examples of walking, stretching, and local club activities include, but are not limited to, walking distance and time, types of stretching, and details of club activities. The suggestion unit can suggest, for example, light walking, stretching, or local club activities based on the analysis results. The suggestion unit can also suggest walking distance and time, types of stretching, and details of club activities based on the analysis results. The suggestion unit can also use a generation AI to suggest walking, stretching, or local club activities based on the analysis results. For example, the suggestion unit can suggest light walking, stretching, or local club activities based on the analysis results. The suggestion unit can also suggest walking distance and time, types of stretching, and details of club activities based on the analysis results. The suggestion unit can also use a generation AI to suggest walking, stretching, or local club activities based on the analysis results. This allows for specific exercises and community activities to be suggested based on the analysis results, making suggestions that are easy for elderly people to follow. Some or all of the above-described processing by the suggestion unit can be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input the analysis results into the generation AI and have the generation AI output the proposal content.

[0071] The monitoring unit can monitor the effectiveness of the proposed exercise or community activity and update the advice as necessary. Methods of monitoring the effectiveness include, but are not limited to, changes in health indicators, participation frequency, and feedback collection methods. For example, the monitoring unit can monitor changes in health indicators and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also monitor participation frequency and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also collect feedback and evaluate the effectiveness of the proposed exercise or community activity. For example, the monitoring unit can monitor changes in health indicators and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also monitor participation frequency and evaluate the effectiveness of the proposed exercise or community activity. The monitoring unit can also collect feedback and evaluate the effectiveness of the proposed exercise or community activity. This enables continuous health management by monitoring the effectiveness of the proposed exercise or community activity and updating the advice as necessary. Some or all of the above-described processing in the monitoring unit can be performed, for example, using a generation AI or without using a generation AI. For example, the monitoring unit can input health index data into the generation AI and have the generation AI evaluate the effectiveness.

[0072] The data collection unit can estimate the elderly person's emotions and adjust the timing of data collection based on the estimated emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the data collection unit can collect data during times when the elderly person is relaxed to avoid stress. The data collection unit can also collect data during times when the elderly person is active to accurately grasp the amount of activity. The data collection unit can also collect data during times when the elderly person is resting to accurately record the sleep patterns. For example, the data collection unit can collect data during times when the elderly person is relaxed to avoid stress. The data collection unit can also collect data during times when the elderly person is active to accurately grasp the amount of activity. The data collection unit can also collect data during times when the elderly person is resting to accurately record the sleep patterns. By adjusting the timing of data collection according to the elderly person's emotions, stress can be avoided and accurate data can be collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the data collection unit may input facial expression data of an elderly person into the generation AI and cause the generation AI to estimate emotions.

[0073] The data collection unit can analyze the elderly person's past data collection history and select an optimal collection method. Examples of optimal collection methods include, but are not limited to, the frequency of data collection, the device used, and the timing of collection. For example, the data collection unit selects a method that yields the most accurate data from the past data collection history. The data collection unit can also adjust the frequency of data collection based on the past data collection history. The data collection unit can also analyze the past data collection history and optimize the timing of data collection. For example, the data collection unit selects a method that yields the most accurate data from the past data collection history. The data collection unit can also adjust the frequency of data collection based on the past data collection history. The data collection unit can also analyze the past data collection history and optimize the timing of data collection. By selecting an optimal collection method based on the past data collection history, the accuracy of data collection is improved. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit can input the past data collection history into the generation AI and have the generation AI select an optimal collection method.

[0074] The data collection unit may filter data based on the elderly person's current living situation and areas of interest during data collection. Filtering criteria include, but are not limited to, the type of data to be collected, its importance, and relevance. For example, the data collection unit may prioritize collecting data related to activities in which the elderly person is interested. The data collection unit may also collect only necessary data depending on the elderly person's living situation. The data collection unit may also narrow down the targets of data collection based on the elderly person's areas of interest. For example, the data collection unit may prioritize collecting data related to activities in which the elderly person is interested. The data collection unit may also collect only necessary data depending on the elderly person's living situation. The data collection unit may also narrow down the targets of data collection based on the elderly person's areas of interest. By filtering data based on the elderly person's living situation and areas of interest, only necessary data can be collected. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit may input data related to the elderly person's areas of interest into the generation AI and have the generation AI perform filtering.

[0075] During data collection, the data collection unit can select the optimal collection means depending on the input method of the elderly person. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the elderly person prefers voice input, the data collection unit can collect data by voice. Furthermore, if the elderly person prefers text input, the data collection unit can also collect data by text. Furthermore, if the elderly person prefers image input, the data collection unit can also collect data by image. For example, if the elderly person prefers voice input, the data collection unit can collect data by voice. Furthermore, if the elderly person prefers text input, the data collection unit can also collect data by text. Furthermore, if the elderly person prefers image input, the data collection unit can also collect data by image. This improves the efficiency of data collection by selecting the optimal collection means depending on the elderly person's input method. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit can input the elderly person's input data into the generation AI and have the generation AI select the optimal collection means.

[0076] The data collection unit can estimate the elderly person's emotions and determine the priority of data to be collected based on the estimated emotions. Criteria for determining data priority include, but are not limited to, the importance of the data, collection timing, and relevance. For example, if the elderly person is stressed, the data collection unit can prioritize collecting stress-related data. Furthermore, if the elderly person is relaxed, the data collection unit can prioritize collecting relaxation-related data. Furthermore, if the elderly person is active, the data collection unit can prioritize collecting activity-related data. For example, if the elderly person is stressed, the data collection unit can prioritize collecting stress-related data. Furthermore, if the elderly person is relaxed, the data collection unit can prioritize collecting relaxation-related data. Furthermore, if the elderly person is active, the data collection unit can prioritize collecting activity-related data. Thus, by determining the priority of data to be collected according to the elderly person's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data collection unit may input emotion data of the elderly person into the generation AI and have the generation AI determine the priority of the data to be collected.

[0077] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the elderly person. Examples of geographical location information include, but are not limited to, GPS data, address information, and movement history. For example, when the elderly person is at home, the data collection unit prioritizes collecting data related to activities at home. Furthermore, when the elderly person is out, the data collection unit can prioritize collecting data related to activities while out. Furthermore, when the elderly person is in a specific facility, the data collection unit can prioritize collecting data related to activities at the facility. For example, when the elderly person is at home, the data collection unit prioritizes collecting data related to activities at home. Furthermore, when the elderly person is out, the data collection unit can prioritize collecting data related to activities while out. Furthermore, when the elderly person is in a specific facility, the data collection unit can prioritize collecting data related to activities at the facility. In this way, by collecting data by taking into account the geographical location information of the elderly person, highly relevant data can be prioritized. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the data collection unit can input the geographic location information of elderly people into the generation AI and have the generation AI collect highly relevant data.

[0078] During data collection, the data collection unit may analyze the social media activities of the elderly person and collect related data. Social media activities include, but are not limited to, post content, comments, and the number of likes. For example, the data collection unit may collect data related to activities shared by the elderly person on social media. The data collection unit may also analyze the content of posts by the elderly person on social media and collect related data. The data collection unit may also collect related data by referring to the activities of the elderly person's friends on social media. For example, the data collection unit may collect data related to activities shared by the elderly person on social media. The data collection unit may also analyze the content of posts by the elderly person on social media and collect related data. The data collection unit may also collect related data by referring to the activities of the elderly person's friends on social media. This allows for efficient collection of related data by analyzing the social media activities of the elderly person. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit may input data on the elderly person's social media activities into the generation AI and cause the generation AI to collect related data.

[0079] The data collection unit can customize the data collection method by reflecting the elderly person's past feedback during data collection. Examples of past feedback include, but are not limited to, survey results, user comments, and ratings. The data collection unit can adjust the data collection method, for example, based on feedback previously provided by the elderly person. The data collection unit can also adjust the frequency of data collection by reflecting the elderly person's past feedback. The data collection unit can also optimize the target of data collection based on the elderly person's past feedback. For example, the data collection unit can adjust the data collection method based on the elderly person's past feedback. The data collection unit can also adjust the frequency of data collection by reflecting the elderly person's past feedback. The data collection unit can also optimize the target of data collection based on the elderly person's past feedback. Thus, the data collection method can be optimized by reflecting the elderly person's past feedback. Some or all of the above-described processing in the data collection unit can be performed using, or without, a generation AI. For example, the data collection unit can input the elderly person's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the elderly person's emotions and adjust the way the analysis is presented based on the estimated emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the analysis unit can provide detailed analysis results when the elderly person is relaxed. Furthermore, the analysis unit can provide concise and to-the-point analysis results when the elderly person is stressed. Furthermore, the analysis unit can provide visually stimulating analysis results when the elderly person is excited. For example, the analysis unit can provide detailed analysis results when the elderly person is relaxed. Furthermore, the analysis unit can provide concise and to-the-point analysis results when the elderly person is stressed. Furthermore, the analysis unit can provide visually stimulating analysis results when the elderly person is excited. Thus, by adjusting the way the analysis is presented based on the elderly person's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the elderly person's emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Criteria for the importance of data include, but are not limited to, health risk, urgency, and relevance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, health data, activity data, and environmental data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply an activity-related analysis algorithm to activity data. The analysis unit can also apply a social-related analysis algorithm to social data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply an activity-related analysis algorithm to activity data. The analysis unit can also apply a social-related analysis algorithm to social data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the elderly person. Past analysis results include, but are not limited to, past health checkup results, exercise history, and dietary records. For example, the analysis unit corrects the current analysis result based on the elderly person's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the elderly person's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the elderly person's past analysis results. For example, the analysis unit corrects the current analysis result based on the elderly person's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the elderly person's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the elderly person's past analysis results. In this way, the accuracy of the analysis is improved by referring to the elderly person's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the elderly person's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the elderly person's emotions and adjust the length of the analysis based on the estimated emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the elderly person is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the elderly person is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the elderly person is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the elderly person is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the elderly person is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the elderly person is excited, the analysis unit can provide a visually stimulating analysis result. Thus, by adjusting the length of the analysis according to the elderly person's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the emotion data of the elderly person into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The data collection time includes, but is not limited to, the latest data, past data, seasonal data, etc. The analysis unit, for example, prioritizes the analysis of the latest data. The analysis unit can also analyze current data with reference to past data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also analyze current data with reference to past data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the latest data can be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, correlation, causal relationship, and co-occurrence relationship. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the elderly person's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the elderly person has technical expertise, the analysis unit can provide the analysis results using technical terms. Furthermore, if the elderly person does not have technical expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the elderly person's level of expertise. For example, if the elderly person has technical expertise, the analysis unit can provide the analysis results using technical terms. Furthermore, if the elderly person does not have technical expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the elderly person's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the elderly person's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the elderly person's level of expertise into the generation AI and have the generation AI adjust the use of technical terms in the analysis.

[0088] The suggestion unit can estimate the elderly person's emotions and adjust the way in which suggestions are expressed based on the estimated emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can provide detailed suggestions when the elderly person is relaxed. Furthermore, the suggestion unit can provide concise and to-the-point suggestions when the elderly person is stressed. Furthermore, the suggestion unit can provide visually stimulating suggestions when the elderly person is excited. For example, the suggestion unit can provide detailed suggestions when the elderly person is relaxed. Furthermore, the suggestion unit can provide concise and to-the-point suggestions when the elderly person is stressed. Furthermore, the suggestion unit can provide visually stimulating suggestions when the elderly person is excited. This allows for adjusting the way in which suggestions are expressed based on the elderly person's emotions, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input emotion data of the elderly person to the generation AI and cause the generation AI to adjust the way the suggestion is expressed.

[0089] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the activity. Criteria for the importance of an activity include, but are not limited to, health benefits, social impact, and personal preferences. For example, the suggestion unit can provide detailed suggestions for highly important activities. The suggestion unit can also provide brief suggestions for less important activities. The suggestion unit can also determine the priority of the suggestion based on the importance of the activity. For example, the suggestion unit can provide detailed suggestions for highly important activities. The suggestion unit can also provide brief suggestions for less important activities. The suggestion unit can also determine the priority of the suggestion based on the importance of the activity. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the activity. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the importance of the activity to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0090] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the activity category. Examples of activity categories include, but are not limited to, exercise, hobbies, and social activities. For example, the suggestion unit can apply an exercise-related suggestion algorithm to exercise. The suggestion unit can also apply a community-related suggestion algorithm to community activities. The suggestion unit can also apply a health management-related suggestion algorithm to health management. For example, the suggestion unit can apply an exercise-related suggestion algorithm to exercise. The suggestion unit can also apply a community-related suggestion algorithm to community activities. The suggestion unit can also apply a health management-related suggestion algorithm to health management. By applying different suggestion algorithms depending on the activity category, the accuracy of the suggestion is improved. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the activity category to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0091] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the elderly person's past proposal results. Past proposal results include, but are not limited to, for example, implementation status, effect evaluation, and feedback. For example, the suggestion unit corrects the current proposal based on the elderly person's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the elderly person's past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the elderly person's past proposal results. For example, the suggestion unit corrects the current proposal based on the elderly person's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the elderly person's past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the elderly person's past proposal results. In this way, the accuracy of the proposal is improved by referring to the elderly person's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the elderly person's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0092] The suggestion unit can estimate the emotion of the elderly person and adjust the length of the suggestion based on the estimated emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the elderly person is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. Also, if the elderly person is relaxed, the suggestion unit can provide a detailed suggestion. Also, if the elderly person is excited, the suggestion unit can provide a visually stimulating suggestion. For example, if the elderly person is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. Also, if the elderly person is relaxed, the suggestion unit can provide a detailed suggestion. Also, if the elderly person is excited, the suggestion unit can provide a visually stimulating suggestion. In this way, by adjusting the length of the suggestion according to the emotion of the elderly person, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input emotion data of the elderly person to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0093] When making a proposal, the suggestion unit can determine the priority of the proposal based on the timing of the activity. Examples of the timing of the activity include, but are not limited to, the season, the time of day, and the timing of an event. For example, the suggestion unit can prioritize the proposal for an activity to be performed soon. The suggestion unit can also postpone the proposal for an activity to be performed over a long period of time. The suggestion unit can also adjust the priority of the proposal depending on the timing of the activity. For example, the suggestion unit can prioritize the proposal for an activity to be performed soon. The suggestion unit can also postpone the proposal for an activity to be performed over a long period of time. The suggestion unit can also adjust the priority of the proposal depending on the timing of the activity. In this way, determining the priority of the proposal based on the timing of the activity enables efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the timing of the activity to the generation AI and cause the generation AI to determine the priority of the proposal.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of activities when making suggestions. Examples of the relevance of activities include, but are not limited to, correlation, causal relationship, and co-occurrence relationship. For example, the suggestion unit prioritizes suggesting highly related activities. The suggestion unit can also postpone less related activities. The suggestion unit can also optimize the order of suggestions based on the relevance of activities. For example, the suggestion unit prioritizes suggesting highly related activities. The suggestion unit can also postpone less related activities. The suggestion unit can also optimize the order of suggestions based on the relevance of activities. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of activities. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the relevance of activities to the generation AI and cause the generation AI to adjust the order of suggestions.

[0095] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the elderly person's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and expert. For example, if the elderly person has technical expertise, the suggestion unit can provide the proposal using technical terms. Furthermore, if the elderly person does not have technical expertise, the suggestion unit can provide the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the elderly person's level of expertise. For example, if the elderly person has technical expertise, the suggestion unit can provide the proposal using technical terms. Furthermore, if the elderly person does not have technical expertise, the suggestion unit can provide the proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the elderly person's level of expertise. In this way, by adjusting the use of technical terms in the proposal according to the elderly person's level of expertise, it is possible to provide an easy-to-understand proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the elderly person's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the proposal.

[0096] The monitoring unit can estimate the elderly person's emotions and adjust the monitoring method based on the estimated emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the monitoring unit can perform detailed monitoring when the elderly person is relaxed. Furthermore, the monitoring unit can perform concise and to-the-point monitoring when the elderly person is stressed. Furthermore, the monitoring unit can perform visually stimulating monitoring when the elderly person is excited. For example, the monitoring unit can perform detailed monitoring when the elderly person is relaxed. Furthermore, the monitoring unit can perform concise and to-the-point monitoring when the elderly person is stressed. Furthermore, the monitoring unit can perform visually stimulating monitoring when the elderly person is excited. This enables more appropriate monitoring by adjusting the monitoring method according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input the elderly person's emotion data into the generation AI and have the generation AI adjust the monitoring method.

[0097] During monitoring, the monitoring unit can optimize the current monitoring method by referring to past monitoring data. Past monitoring data includes, but is not limited to, past health indicators, exercise history, and diet records. For example, the monitoring unit adjusts the current monitoring method based on the past monitoring data. The monitoring unit can also optimize the monitoring frequency by referring to the past monitoring data. The monitoring unit can also optimize the monitoring target by using the past monitoring data. For example, the monitoring unit adjusts the current monitoring method based on the past monitoring data. The monitoring unit can also optimize the monitoring frequency by referring to the past monitoring data. The monitoring unit can also optimize the monitoring target by using the past monitoring data. In this way, optimizing the current monitoring method based on the past monitoring data improves the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input past monitoring data into the generation AI and cause the generation AI to optimize the current monitoring method.

[0098] During monitoring, the monitoring unit can update the monitoring data by reflecting feedback from the elderly. Examples of feedback include, but are not limited to, survey results, user comments, and ratings. For example, the monitoring unit updates the monitoring data based on feedback provided by the elderly. The monitoring unit can also adjust the frequency of monitoring by reflecting the feedback from the elderly. The monitoring unit can also optimize the target of monitoring based on the feedback from the elderly. For example, the monitoring unit updates the monitoring data based on feedback provided by the elderly. The monitoring unit can also adjust the frequency of monitoring by reflecting the feedback from the elderly. The monitoring unit can also optimize the target of monitoring based on the feedback from the elderly. This allows the monitoring data to be kept up to date by reflecting the feedback from the elderly. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the feedback from the elderly into the generation AI and cause the generation AI to update the monitoring data.

[0099] During monitoring, the monitoring unit can analyze the elderly person's lifestyle rhythm and suggest optimal monitoring timing. Lifestyle rhythms include, but are not limited to, sleep patterns, meal times, and activity times. The monitoring unit can optimize the monitoring timing based on the elderly person's lifestyle rhythm. The monitoring unit can also analyze the elderly person's activity patterns and suggest optimal monitoring timing. The monitoring unit can also adjust the monitoring frequency based on the elderly person's lifestyle rhythm. For example, the monitoring unit can optimize the monitoring timing based on the elderly person's lifestyle rhythm. The monitoring unit can also analyze the elderly person's activity patterns and suggest optimal monitoring timing. The monitoring unit can also adjust the monitoring frequency based on the elderly person's lifestyle rhythm. This enables efficient monitoring by optimizing the monitoring timing based on the elderly person's lifestyle rhythm. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input the elderly person's lifestyle rhythm data into the generation AI and have the generation AI suggest monitoring timing.

[0100] The monitoring unit can estimate the elderly person's emotions and adjust the monitoring frequency based on the estimated emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the monitoring unit can increase the monitoring frequency when the elderly person is relaxed. The monitoring unit can also decrease the monitoring frequency when the elderly person is stressed. The monitoring unit can also adjust the monitoring frequency when the elderly person is excited. For example, the monitoring unit can increase the monitoring frequency when the elderly person is relaxed. The monitoring unit can also decrease the monitoring frequency when the elderly person is stressed. The monitoring unit can also adjust the monitoring frequency when the elderly person is excited. This enables more appropriate monitoring by adjusting the monitoring frequency according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input the elderly person's emotion data into the generation AI and have the generation AI adjust the monitoring frequency.

[0101] During monitoring, the monitoring unit can weight the monitoring data based on the time when the activity was performed. Examples of the time when the activity was performed include, but are not limited to, seasons, time periods, and event timing. For example, the monitoring unit can assign a higher weight to the most recently performed activity. The monitoring unit can also assign a lower weight to the activity performed over a long period of time. The monitoring unit can also adjust the weighting of the monitoring data according to the time when the activity was performed. For example, the monitoring unit can assign a higher weight to the most recently performed activity. The monitoring unit can also assign a lower weight to the activity performed over a long period of time. The monitoring unit can also adjust the weighting of the monitoring data according to the time when the activity was performed. In this way, by weighting the monitoring data based on the time when the activity was performed, important data can be prioritized for monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data on the time when the activity was performed to the generation AI and cause the generation AI to weight the monitoring data.

[0102] During monitoring, the monitoring unit can integrate information from different data sources to enrich the monitoring data. Examples of different data sources include, but are not limited to, wearable devices, medical records, and social media. For example, the monitoring unit can integrate data from health devices to enrich the monitoring data. The monitoring unit can also integrate data from social media to enrich the monitoring data. The monitoring unit can also integrate data from medical institutions to enrich the monitoring data. For example, the monitoring unit can integrate data from health devices to enrich the monitoring data. The monitoring unit can also integrate data from social media to enrich the monitoring data. The monitoring unit can also integrate data from medical institutions to enrich the monitoring data. In this way, by integrating information from different data sources, the monitoring data can be enriched and more detailed monitoring can be performed. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input information from different data sources into the generation AI and have the generation AI integrate the monitoring data.

[0103] The monitoring unit can perform monitoring while taking into account the geographical location information of the elderly person. Geographical location information includes, but is not limited to, GPS data, address information, and movement history. For example, when the elderly person is at home, the monitoring unit monitors activities at home. Furthermore, when the elderly person is out, the monitoring unit can monitor activities while away from home. Furthermore, when the elderly person is in a specific facility, the monitoring unit can monitor activities at the facility. For example, when the elderly person is at home, the monitoring unit monitors activities at home. Furthermore, when the elderly person is out, the monitoring unit can monitor activities while away from home. Furthermore, when the elderly person is in a specific facility, the monitoring unit can monitor activities at the facility. This enables more appropriate monitoring by taking into account the geographical location information of the elderly person. Some or all of the above-described processing by the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the geographical location information of the elderly person to the generation AI and have the generation AI perform monitoring. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, suggestion unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests exercises and community activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14 and monitors the effects of the suggested exercises and community activities. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, suggestion unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests exercises or community activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and monitors the effects of the suggested exercises or community activities. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, suggestion unit, and monitoring unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests exercises and community activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and monitors the effects of the suggested exercises and community activities. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, suggestion unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests exercises and community activities based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and monitors the effects of the suggested exercises and community activities.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The data collection unit can also understand the hobbies and interests of the elderly person and customize the content of data collection based on that. For example, if the elderly person is interested in gardening, gardening-related activity data can be prioritized for collection. If the elderly person enjoys music, music-related activity data can also be collected. Furthermore, if the elderly person likes to travel, travel-related data can be collected and their health status can be monitored during travel. This allows for more personalized advice by customizing data collection based on the elderly person's hobbies and interests.

[0106] The analysis unit can assess the elderly person's level of social isolation based on the collected information and, if necessary, enhance recommendations for community activities. For example, the analysis unit can analyze the elderly person's frequency of interactions with friends and family from the collected data, and if the level of isolation is high, suggest local club activities or volunteer activities. The analysis unit can also analyze social media activity data and encourage participation in online communities. Furthermore, the analysis unit can suggest support through regular phone calls or visits based on the results of the isolation assessment. This can prevent social isolation among the elderly and maintain their mental health.

[0107] The suggestion unit can also propose meal plans to improve the nutritional status of elderly people based on the analysis results. For example, the suggestion unit can analyze the collected food records and, if the nutritional balance is unbalanced, propose a balanced meal plan. The suggestion unit can also propose appropriate ingredients and recipes taking into account the elderly person's chronic illnesses and allergy information. Furthermore, the suggestion unit can also suggest recipes that use local ingredients and encourage people to enjoy local food culture. This can improve the nutritional status of elderly people and maintain their health.

[0108] The monitoring unit can also utilize a biofeedback device when monitoring the effectiveness of the suggested exercise or community activity. For example, the monitoring unit collects biofeedback data, such as heart rate, blood pressure, and stress level, to evaluate the effectiveness of the suggested activity. The monitoring unit can also adjust the intensity and frequency of exercise based on the biofeedback data. Furthermore, the monitoring unit can provide biofeedback data in real time to enable the elderly person to understand their own health condition. This allows the effectiveness of the suggested activity to be monitored more accurately and appropriate advice to be provided.

[0109] The data collection unit can also estimate the emotions of the elderly person and adjust the data collection method based on the estimated emotions. For example, if the elderly person is feeling stressed, the frequency of data collection can be reduced, and if the elderly person is relaxed, detailed data can be collected. Also, if the elderly person is excited, activity data can be collected preferentially. Furthermore, the timing of data collection can be adjusted according to the elderly person's emotions to avoid stress. This makes it possible to collect data that takes the elderly person's emotions into consideration, resulting in more accurate data.

[0110] The analysis unit can also estimate the emotions of the elderly person and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the elderly person is relaxed, detailed analysis results can be provided, and if the elderly person is stressed, concise analysis results that focus on the main points can be provided. Also, if the elderly person is excited, visually stimulating analysis results can be provided. Furthermore, the timing of the presentation of the analysis results can be adjusted according to the elderly person's emotions, and information can be provided at the optimal timing. This makes it possible to present analysis results that take the elderly person's emotions into consideration, allowing for more effective advice to be provided.

[0111] The suggestion unit can also estimate the elderly person's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the elderly person is relaxed, it can suggest detailed exercise plans or community activities, and if the elderly person is stressed, it can suggest simple, easy-to-follow suggestions. If the elderly person is excited, it can also make visually appealing suggestions. Furthermore, it can adjust the timing of the suggestions according to the elderly person's emotions and make suggestions at the optimal time. This makes it possible to make suggestions that take the elderly person's emotions into consideration, improving the likelihood of the suggestions being accepted.

[0112] The monitoring unit can also estimate the elderly person's emotions and adjust the frequency and method of monitoring based on the estimated emotions. For example, if the elderly person is relaxed, detailed monitoring can be performed, and if the elderly person is stressed, brief monitoring can be performed. Also, if the elderly person is excited, visually stimulating monitoring can be performed. Furthermore, the timing of monitoring can be adjusted according to the elderly person's emotions, and monitoring can be performed at the optimal timing. This enables monitoring that takes the elderly person's emotions into consideration, and more accurate data can be collected.

[0113] The data collection unit can also estimate the emotions of the elderly person and determine the priority of data to be collected based on the estimated emotions. For example, if the elderly person is feeling stressed, stress-related data can be collected with priority, and if the elderly person is relaxed, relaxation-related data can be collected with priority. Also, if the elderly person is active, activity-related data can be collected with priority. Furthermore, the type of data to be collected can be adjusted according to the elderly person's emotions, and optimal data can be collected. This makes it possible to collect data according to the elderly person's emotions, and to prioritize the collection of more important data.

[0114] When monitoring the effectiveness of proposed exercise or community activities, the monitoring unit can also integrate information from different data sources to enrich the monitoring data. For example, data from wearable devices, medical records, social media activity data, etc. can be integrated to conduct more detailed monitoring. The monitoring unit can also integrate information from different data sources in real time to evaluate the effectiveness immediately. Furthermore, the monitoring unit can comprehensively evaluate the effectiveness of the proposal based on information from different data sources and update advice as necessary. In this way, integrating information from different data sources enriches the monitoring data and enables more accurate effectiveness evaluation.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The data collection unit collects information on the elderly person's living situation, physical condition, and chronic illnesses. Specifically, information such as daily activity level, dietary content, sleep patterns, and medical history is collected. For example, pedometer data is collected to understand daily activity level, food records are collected to understand dietary content, sleep tracker data is collected to understand sleep patterns, and medical records are collected to understand medical history. Step 2: The analysis unit uses the generative AI to analyze the collected information. This analysis is carried out using statistical analysis of the data and machine learning algorithms. This allows the analysis to identify patterns and trends in the collected information and provide detailed analysis results on the elderly's living conditions and physical condition. Step 3: The suggestion unit uses generative AI to suggest exercises and community activities based on the analysis results. Suggestions are made based on the type of exercise and the content of the community activity. For example, light walking, stretching, or local club activities are suggested. Step 4: The Monitoring Department monitors the effectiveness of the proposed exercise and community activities. Monitoring is carried out based on the effectiveness measurement method and monitoring frequency. This will evaluate the impact of the proposed exercise and community activities on the health of the elderly and adjust the proposals as necessary.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 department that collects information on the living conditions, physical condition, and chronic illnesses of elderly people; an analysis unit that analyzes the information collected by the data collection unit; a suggestion unit that suggests exercise or community activities based on the analysis results obtained by the analysis unit; a monitoring unit that monitors the effects of the exercise or community activity suggested by the suggestion unit. A system characterized by:

2. The data collection unit Collect information on the elderly person's daily activity levels, diet, sleep patterns, medical history, etc.

2. The system of claim 1.

3. The analysis unit Generative AI analyzes the collected information 2. The system of claim 1.

4. The proposal unit Based on the analysis results, suggestions for walking, stretching, and local club activities are made available.

2. The system of claim 1.

5. The monitoring unit Monitor the effectiveness of proposed physical activities and community activities and update advice as necessary 2. The system of claim 1.

6. The data collection unit Estimate the emotions of the elderly and adjust the timing of data collection based on the estimated emotions.

2. The system of claim 1.

7. The data collection unit Analyze the past data collection history of elderly people and select the optimal collection method 2. The system of claim 1.

8. The data collection unit When collecting data, filtering is performed based on the elderly person's current living situation and areas of interest.

2. The system of claim 1.

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