System

Abstract

An object of the system according to the embodiment is to analyze customer data, evaluate frailty risk, and propose individually optimized care.SOLUTION: A system includes a collection part, an analysis part, a proposal part, a data combination part, and a data management part. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit and evaluates a frailty risk. The proposal unit proposes individually-optimized care based on the evaluation result obtained by the analysis unit. The data combiner combines the anonymized data of the enterprise and the platform. The data management unit manages the customer data in an environment where privacy protection is emphasized.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately assess frailty risk using customer data and propose individually optimized care, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze customer data, assess frailty risk, and propose individually optimized care. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a data integration unit, and a data management unit. The collection unit collects customer data. The analysis unit analyzes the data collected by the collection unit and assesses frailty risk. The proposal unit proposes individually optimized care based on the assessment results obtained by the analysis unit. The data integration unit combines anonymized data from companies and platforms. The data management unit manages customer data in an environment that emphasizes privacy protection. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer data, assess frailty risk, and propose individually optimized care. [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) A frailty risk assessment system according to an embodiment of the present invention is a system that collects and analyzes customer data and proposes individually optimized care. The frailty risk assessment system collects and analyzes customer data, assesses frailty risk, and proposes individually optimized care. The frailty risk assessment system also combines anonymized data from companies and platforms and manages customer data in an environment that emphasizes privacy protection. For example, the frailty risk assessment system collects customer data from various channels and devices. For example, the frailty risk assessment system collects and analyzes daily activity data and health data to detect frailty risk early. Next, the frailty risk assessment system combines the anonymized data from companies and platforms to delve deeper into each customer's frailty risk and the effectiveness of care. For example, specific exercise programs and nutritional guidance can be proposed. The frailty risk assessment system also manages customer data in an environment that emphasizes privacy protection. As a result, the frailty risk assessment system can collect, analyze, propose, combine, and manage customer data, thereby providing early detection of frailty risk and individually optimized care. As a result, the frailty risk assessment system is able to collect, analyze, make suggestions, combine, and manage customer data, thereby enabling early detection of frailty risk and providing individually optimized care. For example, the frailty risk assessment system collects and analyzes customer data, assesses frailty risk, and proposes individually optimized care. The frailty risk assessment system also combines anonymized data from companies and platforms, and manages customer data in an environment that emphasizes privacy protection. As a result, the frailty risk assessment system is able to collect, analyze, make suggestions, combine, and manage customer data, thereby enabling early detection of frailty risk and providing individually optimized care.

[0029] A frailty risk assessment system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a data integration unit, and a data management unit. The collection unit collects customer data. Examples of the customer data include, but are not limited to, personal information, purchase history, and health data. The collection unit aggregates customer data from various channels and devices. For example, the collection unit can collect data from smartphones, wearable devices, websites, and the like. The collection unit can also adjust the frequency and means of data collection. The analysis unit analyzes the data collected by the collection unit to assess frailty risk. The analysis is performed based on, for example, but not limited to, the algorithm used and the accuracy of the analysis. For example, the analysis unit analyzes daily activity data and health data to assess frailty risk. The analysis unit can analyze data such as the number of steps taken, the amount of exercise, sleep patterns, blood pressure, heart rate, and weight. The suggestion unit proposes individually optimized care based on the assessment results obtained by the analysis unit. The proposal is performed based on, for example, but not limited to, a specific exercise program or nutritional guidance. For example, the suggestion unit suggests a specific exercise program or nutritional guidance based on the analysis results. The suggestion unit can suggest, for example, aerobic exercise, strength training, a meal plan, and nutritional balance guidance. The data combination unit combines anonymized data from the company and the platform and provides it to the analysis unit. Data combination is performed, for example, based on, but not limited to, removing personally identifiable information or masking data. For example, the data combination unit combines anonymized data from the company and the platform and provides it to the analysis unit. The data management unit manages customer data in an environment that emphasizes privacy protection. Data management is performed, for example, based on, but not limited to, data encryption and access control. For example, the data management unit manages customer data in an environment that emphasizes privacy protection. As a result, the frailty risk assessment system according to the embodiment can collect, analyze, suggest, combine, and manage customer data, thereby enabling early detection of frailty risk and providing individually optimized care.

[0030] The collection unit can aggregate customer data from multiple channels and devices. The collection unit collects customer data from, for example, smartphones, wearable devices, websites, etc. For example, the collection unit can collect user activity data through a smartphone app. The collection unit can also collect health data such as heart rate and step count from a wearable device. Furthermore, the collection unit can collect customer behavior data based on website usage history. This allows for aggregating data from various channels and devices to create a more comprehensive customer profile. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data obtained from a smartphone app into a generation AI and have the generation AI analyze and aggregate the data.

[0031] The analysis unit can analyze daily activity data or health data to assess frailty risk. The analysis unit, for example, analyzes daily activity data. For example, the analysis unit analyzes data such as the number of steps, amount of exercise, and sleep patterns to assess frailty risk. The analysis unit can also analyze health data. For example, the analysis unit analyzes data such as blood pressure, heart rate, and weight to assess frailty risk. The analysis unit can also analyze a combination of daily activity data and health data. For example, the analysis unit analyzes a combination of step count and heart rate data to assess frailty risk. In this way, frailty risk can be accurately assessed by analyzing the daily activity data and health data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input daily activity data and health data into a generation AI and cause the generation AI to evaluate frailty risk.

[0032] The suggestion unit can suggest a specific exercise program or nutritional guidance based on the analysis results. The suggestion unit, for example, suggests a specific exercise program based on the analysis results. For example, the suggestion unit suggests an exercise program such as aerobic exercise or strength training. The suggestion unit can also suggest nutritional guidance based on the analysis results. For example, the suggestion unit suggests a meal plan or guidance on nutritional balance. Furthermore, the suggestion unit can also suggest a combination of an exercise program and nutritional guidance. For example, the suggestion unit suggests a combination of aerobic exercise and a meal plan. This makes it possible to provide individually optimized care by suggesting appropriate care based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the analysis results into a generation AI and cause the generation AI to suggest an exercise program or nutritional guidance.

[0033] The data combination unit can combine anonymized data from companies and platforms and provide it to the analysis unit. The data combination unit, for example, combines anonymized data from companies and platforms. For example, the data combination unit anonymizes data by deleting personally identifiable information and masking the data. The data combination unit can also provide the anonymized data to the analysis unit. For example, the data combination unit combines a company's customer data with platform usage data and provides it to the analysis unit. The data combination unit can also perform analysis based on the anonymized data. For example, the data combination unit analyzes customer behavior patterns based on the anonymized data. This allows for combining anonymous data to achieve deeper customer understanding and improve the effectiveness of care. Some or all of the above-mentioned processing in the data combination unit may be performed using, for example, AI, or may be performed without AI. For example, the data combination unit can input anonymized data into a generation AI and have the generation AI perform data combination and analysis.

[0034] The data management unit can manage customer data in an environment that emphasizes privacy protection. For example, the data management unit manages customer data in an environment that emphasizes privacy protection. For example, the data management unit achieves privacy protection by encrypting data and controlling access. The data management unit can also manage the storage location and access permissions of customer data. For example, the data management unit stores customer data on a secure server so that only specific users can access it. Furthermore, the data management unit can monitor the usage history of customer data and prevent unauthorized access. For example, the data management unit records an access log of customer data and detects abnormal access. This ensures data security by managing customer data in an environment that emphasizes privacy protection. Some or all of the above-mentioned processing in the data management unit may be performed using, or without, AI. For example, the data management unit may have a generation AI perform encryption of customer data and access control.

[0035] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history. For example, the collection unit analyzes past collection dates and times and the type of collected data to select the optimal collection method. The collection unit can also collect data by preferentially using devices that the user has used in the past. For example, the collection unit can collect data during time periods when the user has provided data with high accuracy in the past. Furthermore, the collection unit can eliminate data collection methods that the user has avoided in the past and select other methods. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into a generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can filter data based on the user's current health condition and lifestyle when collecting data. The collection unit, for example, evaluates the user's current health condition. For example, the collection unit evaluates the user's health condition based on medical records or self-reports. The collection unit can also evaluate the user's lifestyle. For example, the collection unit evaluates the user's lifestyle based on lifestyle habits and home environment. Furthermore, the collection unit can filter data based on the user's current health condition and lifestyle when collecting data. For example, the collection unit temporarily stops data collection when the user is in poor health. The collection unit can also prioritize collecting exercise data when the user is exercising. Furthermore, the collection unit can collect heart rate and sleep data when the user is resting. This allows more appropriate data to be collected by filtering data according to the user's health condition and lifestyle. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's health condition and lifestyle to the generation AI and have the generation AI perform filtering.

[0037] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit, for example, evaluates the user's input method. For example, the collection unit evaluates input methods such as voice input, text input, and image input. The collection unit can also select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. If the user prefers text input, the collection unit can preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can preferentially collect image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method to the generation AI and cause the generation AI to select an appropriate collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, collects the user's geographical location information. For example, the collection unit collects the user's geographical location information using GPS data or a location information service. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting activity data at home. Also, when the user is out, the collection unit can prioritize collecting activity data while away from home. Furthermore, when the user is in a specific facility, the collection unit can prioritize collecting data related to the facility. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit analyzes the content of social media posts and the number of likes. The collection unit can also collect related data based on the user's social media activity. For example, the collection unit collects related health data based on the content of the user's social media posts. The collection unit can also collect related data by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect related data based on the user's social media check-in information. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method based on the user's past feedback when collecting data. The collection unit, for example, collects the user's past feedback. For example, the collection unit collects the user's ratings and comments. The collection unit can also customize the collection method based on the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use collection methods that the user previously preferred. Furthermore, the collection unit can eliminate collection methods that the user previously avoided and select other methods. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis taking into account the importance of the data. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit evaluates the importance of the data based on the frequency of use and impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms based on the data category. The analysis unit, for example, classifies the data category. For example, the analysis unit may classify the data into categories such as health data, behavioral data, and social media data. The analysis unit can also apply different analysis algorithms based on the data category. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply a behavior-related analysis algorithm to behavioral data. The analysis unit can also apply a social media-related analysis algorithm to social media data. This improves the accuracy of analysis by applying an analysis algorithm according to the data category. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, collects the user's past analysis results. For example, the analysis unit collects past evaluation scores and analysis reports. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority taking into account the time when the data was collected. The analysis unit, for example, evaluates the time when the data was collected. For example, the analysis unit evaluates the time when the data was collected based on the collection date and time and the collection frequency. The analysis unit can also determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also analyze current data with reference to past data. Furthermore, the analysis unit can prioritize analyzing data from a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis taking into account the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit evaluates the relevance of the data using correlation analysis or co-occurrence networks. The analysis unit can also adjust the order of analysis based on 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. Furthermore, the analysis unit can adjust 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-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on a questionnaire survey or past usage history. The analysis unit can also adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit uses a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis based on the user's level of expertise. This allows the analysis results to be better understood by adjusting the use of technical terms based on the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's level of expertise into a generation AI and have the generation AI use technical terms.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal taking into account the importance of care. The suggestion unit, for example, evaluates the importance of care. For example, the suggestion unit evaluates the importance of care based on an assessment by a medical professional or a risk score. The suggestion unit can also adjust the level of detail of the proposal based on the importance of care. For example, the suggestion unit makes a detailed proposal for care of high importance. The suggestion unit can also make a simplified proposal for care of low importance. Furthermore, the suggestion unit can also make a proposal with an appropriate level of detail for care of medium importance. As a result, adjusting the level of detail of the proposal based on the importance of care enables efficient care proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of care to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms based on the care category. The suggestion unit, for example, classifies care categories. For example, the suggestion unit classifies care categories into exercise care, nutrition care, mental care, etc. The suggestion unit can also apply different suggestion algorithms based on the care category. For example, the suggestion unit can apply an exercise-related suggestion algorithm to an exercise program. The suggestion unit can also apply a nutrition-related suggestion algorithm to nutritional guidance. The suggestion unit can also apply a mental care-related suggestion algorithm to mental care. In this way, by applying a suggestion algorithm according to the care category, the suggestion accuracy is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the care category to the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit, for example, collects the user's past suggestion results. For example, the suggestion unit collects the success rate of past suggestions and user feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit corrects the current suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. Furthermore, the suggestion unit can improve the accuracy of the suggestion by using the user's past suggestion results. In this way, the accuracy of the current suggestion is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposals taking into account the timing of care implementation. The suggestion unit, for example, evaluates the timing of care implementation. For example, the suggestion unit evaluates the timing of care implementation based on the start date and frequency of care implementation. The suggestion unit can also determine the priority of the proposals based on the timing of care implementation. For example, the suggestion unit prioritizes proposals that need to be implemented in the near future. The suggestion unit can also postpone proposals for care that will be implemented over the long term. Furthermore, the suggestion unit can also determine the priority of the proposals according to the timing of care implementation. In this way, by determining the priority of the proposals based on the timing of care implementation, care can be proposed at an appropriate time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the timing of care implementation to the generation AI and cause the generation AI to determine the priority of the proposals.

[0051] The suggestion unit can adjust the order of suggestions taking into account the relevance of care when making suggestions. The suggestion unit, for example, evaluates the relevance of care. For example, the suggestion unit evaluates the relevance of care using correlation analysis or a co-occurrence network. The suggestion unit can also adjust the order of suggestions based on the relevance of care. For example, the suggestion unit prioritizes suggesting highly relevant care. The suggestion unit can also postpone suggesting less relevant care. The suggestion unit can also adjust the order of suggestions according to the relevance of care. As a result, adjusting the order of suggestions based on the relevance of care enables efficient care suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of care to a generation AI and cause the generation AI to adjust the order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal based on the user's level of expertise. The suggestion unit, for example, evaluates the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise based on a questionnaire survey or past usage history. The suggestion unit can also adjust the use of technical terms in the proposal based on the user's level of expertise. For example, the suggestion unit uses a lot of technical terms if the user has technical expertise. The suggestion unit can also avoid technical terms if the user does not have technical expertise. The suggestion unit can also adjust the use of technical terms in the proposal based on the user's level of expertise. This allows the user to better understand the proposed content by adjusting the use of technical terms based on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise to a generation AI and cause the generation AI to use technical terms.

[0053] The data combination unit can improve the accuracy of data combination based on the interrelationships between data when combining data. The data combination unit, for example, evaluates the interrelationships between data. For example, the data combination unit evaluates the interrelationships between data using correlation analysis or co-occurrence networks. The data combination unit can also improve the accuracy of combination based on the interrelationships between data. For example, the data combination unit analyzes the interrelationships between data and preferentially combines highly related data. The data combination unit can also improve the accuracy of combination by taking the interrelationships between data into consideration. Furthermore, the data combination unit can adjust the combination algorithm based on the interrelationships between data. In this way, the accuracy of combination is improved by taking the interrelationships between data into consideration. Some or all of the above-mentioned processing in the data combination unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combination unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of combination.

[0054] When combining data, the data combining unit can perform the combination based on the attribute information of the data provider. The data combining unit, for example, collects attribute information of the data provider. For example, the data combining unit collects attribute information such as age, gender, and occupation. The data combining unit can also perform the combination based on the attribute information of the data provider. For example, the data combining unit preferentially combines highly relevant data based on the attribute information of the data provider. The data combining unit can also improve the accuracy of the combination by taking the attribute information of the data provider into consideration. Furthermore, the data combining unit can adjust the combination algorithm based on the attribute information of the data provider. This improves the accuracy of the combination by taking the attribute information of the data provider into consideration. Some or all of the above-mentioned processing in the data combining unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combining unit can input the attribute information of the data provider to the generation AI and cause the generation AI to improve the accuracy of the combination.

[0055] The data combining unit can weight the data based on the frequency of data provision when combining data. The data combining unit, for example, evaluates the frequency of data provision. For example, the data combining unit evaluates the frequency of data provision based on the number of times data is provided or the interval between data provision. The data combining unit can also weight the data based on the frequency of data provision. For example, the data combining unit can prioritize combining data with a high frequency of provision. The data combining unit can also combine data with a low frequency of provision later. The data combining unit can also weight the data based on the frequency of data provision. In this way, weighting the data based on the frequency of data provision enables efficient data combination. Some or all of the above-described processing in the data combining unit may be performed using AI, for example, or may be performed without using AI. For example, the data combining unit can input the frequency of data provision to a generation AI and cause the generation AI to perform the weighting of the data provision.

[0056] When combining data, the data combining unit can perform the combining based on the geographic distribution of the data. The data combining unit, for example, evaluates the geographic distribution of the data. For example, the data combining unit evaluates the geographic distribution of the data based on regional data or geographic clusters. The data combining unit can also perform the combining based on the geographic distribution of the data. For example, the data combining unit can prioritize combining geographically close data. The data combining unit can also prioritize combining geographically distant data. Furthermore, the data combining unit can adjust the order of combining taking into account the geographic distribution of the data. In this way, by taking into account the geographic distribution of the data, highly related data can be prioritized to be combined. Some or all of the above-described processing in the data combining unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combining unit can input the geographic distribution of the data to the generation AI and cause the generation AI to adjust the order of combining.

[0057] When combining data, the data combining unit can improve the accuracy of the combination by referring to literature related to the data. The data combining unit, for example, refers to literature related to the data. For example, the data combining unit refers to academic papers and technical reports. The data combining unit can also improve the accuracy of the combination based on literature related to the data. For example, the data combining unit can adjust the combination algorithm based on the related literature. The data combining unit can also improve the accuracy of the combination by referring to the related literature. Furthermore, the data combining unit can set combination criteria based on the related literature. In this way, by referring to the related literature, the accuracy of the combination is improved. Some or all of the above-mentioned processing in the data combining unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combining unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the combination.

[0058] When combining data, the data combining unit can perform the combination based on the market value of the data. The data combining unit, for example, evaluates the market value of the data. For example, the data combining unit evaluates the market value of the data based on the trading price and demand forecast of the data. The data combining unit can also perform the combination based on the market value of the data. For example, the data combining unit prioritizes combining data with high market value. The data combining unit can also combine data with low market value later. Furthermore, the data combining unit can adjust the order of combination taking into account the market value of the data. In this way, by taking into account the market value of the data, data with high value can be combined preferentially. Some or all of the above-mentioned processing in the data combining unit may be performed using, or without, AI. For example, the data combining unit can input the market value of the data to the generation AI and cause the generation AI to adjust the order of combination.

[0059] The data management unit can adjust the level of detail of management during data management, taking into account the importance of the data. The data management unit, for example, evaluates the importance of the data. For example, the data management unit evaluates the importance of the data based on the frequency of use and the impact of the data. The data management unit can also adjust the level of detail of management based on the importance of the data. For example, the data management unit performs detailed management for data with high importance. The data management unit can also perform simplified management for data with low importance. Furthermore, the data management unit can also manage data with an appropriate level of detail for data with medium importance. This enables efficient data management by adjusting the level of detail of management based on the importance of the data. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the importance of the data to a generation AI and have the generation AI adjust the level of detail of management.

[0060] The data management unit can apply different management methods based on the data category during data management. The data management unit, for example, classifies data categories. For example, the data management unit classifies data into categories such as health data, behavioral data, and social media data. The data management unit can also apply different management methods based on the data category. For example, the data management unit can apply a health-related management method to health data. The data management unit can also apply a behavior-related management method to behavioral data. Furthermore, the data management unit can also apply a social media-related management method to social media data. In this way, by applying a management method according to the data category, management accuracy is improved. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the data category to the generation AI and cause the generation AI to apply an appropriate management method.

[0061] During data management, the data management unit can improve the accuracy of management based on the user's past management results. The data management unit, for example, collects the user's past management results. For example, the data management unit collects the success rate of past management and user feedback. The data management unit can also improve the accuracy of management based on the user's past management results. For example, the data management unit corrects the current management method based on the user's past management results. The data management unit can also adjust the management method by referring to the user's past management results. Furthermore, the data management unit can improve the accuracy of management by using the user's past management results. In this way, the accuracy of current management is improved by referring to the past management results. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the user's past management results into a generation AI and have the generation AI improve the accuracy of management.

[0062] During data management, the data management unit can determine management priorities taking into account the time when the data was collected. The data management unit, for example, evaluates the time when the data was collected. For example, the data management unit evaluates the time when the data was collected based on the collection date and time and the collection frequency. The data management unit can also determine management priorities based on the time when the data was collected. For example, the data management unit prioritizes managing the most recent data. The data management unit can also manage current data by referring to past data. Furthermore, the data management unit can prioritize managing data from a specific period. In this way, by determining management priorities based on the time when the data was collected, the most recent data can be managed preferentially. Some or all of the above-described processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the time when the data was collected into the generation AI and have the generation AI determine the management priorities.

[0063] The data management unit can adjust the management order taking into account the relevance of the data during data management. The data management unit, for example, evaluates the relevance of the data. For example, the data management unit evaluates the relevance of the data using correlation analysis or co-occurrence networks. The data management unit can also adjust the management order based on the relevance of the data. For example, the data management unit prioritizes management of highly relevant data. The data management unit can also manage less relevant data later. The data management unit can also adjust the management order according to the relevance of the data. This enables efficient data management by adjusting the management order based on the relevance of the data. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the relevance of the data to a generation AI and have the generation AI adjust the management order.

[0064] During data management, the data management unit can adjust the use of technical terms in the management based on the user's level of expertise. The data management unit, for example, evaluates the user's level of expertise. For example, the data management unit evaluates the user's level of expertise based on a questionnaire survey or past usage history. The data management unit can also adjust the use of technical terms in the management based on the user's level of expertise. For example, the data management unit uses a lot of technical terms if the user has technical expertise. The data management unit can also avoid technical terms if the user does not have technical expertise. The data management unit can also adjust the use of technical terms in the management based on the user's level of expertise. This allows the user to deepen their understanding of the management content by adjusting the use of technical terms based on the user's level of expertise. Some or all of the above-described processing in the data management unit can be performed using, for example, AI, or without AI. For example, the data management unit can input the user's level of expertise into a generation AI and have the generation AI use technical terms.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit can analyze past collection dates and times and the type of collected data to select the optimal collection method. The collection unit can also collect data by preferentially using devices that the user has used favorably in the past. For example, the collection unit can collect data during time periods when the user has provided data with high accuracy in the past. Furthermore, the collection unit can eliminate data collection methods that the user has avoided in the past and select other methods. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0067] The collection unit can filter data based on the user's current health condition and lifestyle when collecting data. For example, the collection unit evaluates the user's current health condition. For example, the collection unit evaluates the user's health condition based on medical records and self-reports. The collection unit can also evaluate the user's lifestyle. For example, the collection unit evaluates the user's lifestyle based on lifestyle habits and home environment. Furthermore, the collection unit can filter data based on the user's current health condition and lifestyle when collecting data. For example, the collection unit temporarily stops data collection when the user is in poor health. Furthermore, the collection unit can prioritize collecting exercise data when the user is exercising. Furthermore, the collection unit can collect heart rate and sleep data when the user is resting. This allows more appropriate data to be collected by filtering data according to the user's health condition and lifestyle. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's health condition and lifestyle to the generation AI and have the generation AI perform filtering.

[0068] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, the collection unit evaluates the user's input method. For example, the collection unit evaluates input methods such as voice input, text input, and image input. The collection unit can also select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. If the user prefers text input, the collection unit can preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can preferentially collect image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method to the generation AI and cause the generation AI to select an appropriate collection means.

[0069] During analysis, the analysis unit can apply different analysis algorithms based on the category of data. For example, the analysis unit classifies the data into categories such as health data, behavioral data, and social media data. The analysis unit can also apply different analysis algorithms based on the category of data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply a behavior-related analysis algorithm to behavioral data. The analysis unit can also apply a social media-related analysis algorithm to social media data. This improves the accuracy of analysis by applying an analysis algorithm according to the data category. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0070] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit collects the user's past analysis results. For example, the analysis unit collects past evaluation scores and analysis reports. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit corrects the current analysis results based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0071] When making a proposal, the suggestion unit can adjust the level of detail of the proposal taking into account the importance of care. For example, the suggestion unit evaluates the importance of care. For example, the suggestion unit evaluates the importance of care based on an assessment by a medical professional or a risk score. The suggestion unit can also adjust the level of detail of the proposal based on the importance of care. For example, the suggestion unit makes a detailed proposal for care of high importance. The suggestion unit can also make a simplified proposal for care of low importance. Furthermore, the suggestion unit can also make a proposal with an appropriate level of detail for care of medium importance. As a result, adjusting the level of detail of the proposal based on the importance of care enables efficient care proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of care to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0072] When making a suggestion, the suggestion unit can apply different suggestion algorithms based on the care category. For example, the suggestion unit classifies the care category. For example, the suggestion unit may classify the care into categories such as exercise care, nutrition care, and mental care. The suggestion unit can also apply different suggestion algorithms based on the care category. For example, the suggestion unit can apply an exercise-related suggestion algorithm to an exercise program. The suggestion unit can also apply a nutrition-related suggestion algorithm to nutritional guidance. The suggestion unit can also apply a mental care-related suggestion algorithm to mental care. In this way, by applying a suggestion algorithm according to the care category, the suggestion accuracy is improved. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the care category to a generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0073] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit collects the user's past suggestion results. For example, the suggestion unit collects the success rate of past suggestions and user feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit corrects the current suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. Furthermore, the suggestion unit can improve the accuracy of the suggestion by using the user's past suggestion results. In this way, the accuracy of the current suggestion is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0074] When making a proposal, the suggestion unit can determine the priority of the proposal taking into account the timing of care implementation. For example, the suggestion unit evaluates the timing of care implementation. For example, the suggestion unit evaluates the timing of care implementation based on the start date and frequency of care implementation. The suggestion unit can also determine the priority of the proposal based on the timing of care implementation. For example, the suggestion unit prioritizes proposals that need to be implemented in the near future. The suggestion unit can also postpone proposals for care that will be implemented over the long term. Furthermore, the suggestion unit can also determine the priority of the proposals according to the timing of care implementation. In this way, by determining the priority of the proposals based on the timing of care implementation, care can be proposed at an appropriate time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the timing of care implementation to the generation AI and cause the generation AI to determine the priority of the proposals.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects customer data. This data may include, for example, personal information, purchase history, and health data. The collection unit aggregates data from various channels and devices, such as smartphones, wearable devices, and websites. The collection unit can also adjust the frequency and means of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit and assesses frailty risk. The analysis unit analyzes daily activity data and health data, and assesses frailty risk using data such as the number of steps taken, amount of exercise, sleep patterns, blood pressure, heart rate, and weight. Step 3: The proposal unit proposes individually optimized care based on the evaluation results obtained by the analysis unit. The proposal unit proposes specific exercise programs and nutritional guidance, including aerobic exercise, strength training, meal plans, and nutritional balance guidance. Step 4: The data combiner combines the anonymized data from the companies and the platform and provides it to the analytics department. Data combiner removes personally identifiable information and masks the data. Step 5: The data management department manages customer data in an environment that emphasizes privacy protection. Data management is based on data encryption and access control.

[0077] (Example 2) A frailty risk assessment system according to an embodiment of the present invention is a system that collects and analyzes customer data and proposes individually optimized care. The frailty risk assessment system collects and analyzes customer data, assesses frailty risk, and proposes individually optimized care. The frailty risk assessment system also combines anonymized data from companies and platforms and manages customer data in an environment that emphasizes privacy protection. For example, the frailty risk assessment system collects customer data from various channels and devices. For example, the frailty risk assessment system collects and analyzes daily activity data and health data to detect frailty risk early. Next, the frailty risk assessment system combines the anonymized data from companies and platforms to delve deeper into each customer's frailty risk and the effectiveness of care. For example, specific exercise programs and nutritional guidance can be proposed. The frailty risk assessment system also manages customer data in an environment that emphasizes privacy protection. As a result, the frailty risk assessment system can collect, analyze, propose, combine, and manage customer data, thereby providing early detection of frailty risk and individually optimized care. As a result, the frailty risk assessment system is able to collect, analyze, make suggestions, combine, and manage customer data, thereby enabling early detection of frailty risk and providing individually optimized care. For example, the frailty risk assessment system collects and analyzes customer data, assesses frailty risk, and proposes individually optimized care. The frailty risk assessment system also combines anonymized data from companies and platforms, and manages customer data in an environment that emphasizes privacy protection. As a result, the frailty risk assessment system is able to collect, analyze, make suggestions, combine, and manage customer data, thereby enabling early detection of frailty risk and providing individually optimized care.

[0078] A frailty risk assessment system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a data integration unit, and a data management unit. The collection unit collects customer data. Examples of the customer data include, but are not limited to, personal information, purchase history, and health data. The collection unit aggregates customer data from various channels and devices. For example, the collection unit can collect data from smartphones, wearable devices, websites, and the like. The collection unit can also adjust the frequency and means of data collection. The analysis unit analyzes the data collected by the collection unit to assess frailty risk. The analysis is performed based on, for example, but not limited to, the algorithm used and the accuracy of the analysis. For example, the analysis unit analyzes daily activity data and health data to assess frailty risk. The analysis unit can analyze data such as the number of steps taken, the amount of exercise, sleep patterns, blood pressure, heart rate, and weight. The suggestion unit proposes individually optimized care based on the assessment results obtained by the analysis unit. The proposal is performed based on, for example, but not limited to, a specific exercise program or nutritional guidance. For example, the suggestion unit suggests a specific exercise program or nutritional guidance based on the analysis results. The suggestion unit can suggest, for example, aerobic exercise, strength training, a meal plan, and nutritional balance guidance. The data combination unit combines anonymized data from the company and the platform and provides it to the analysis unit. Data combination is performed, for example, based on, but not limited to, removing personally identifiable information or masking data. For example, the data combination unit combines anonymized data from the company and the platform and provides it to the analysis unit. The data management unit manages customer data in an environment that emphasizes privacy protection. Data management is performed, for example, based on, but not limited to, data encryption and access control. For example, the data management unit manages customer data in an environment that emphasizes privacy protection. As a result, the frailty risk assessment system according to the embodiment can collect, analyze, suggest, combine, and manage customer data, thereby enabling early detection of frailty risk and providing individually optimized care.

[0079] The collection unit can aggregate customer data from multiple channels and devices. The collection unit collects customer data from, for example, smartphones, wearable devices, websites, etc. For example, the collection unit can collect user activity data through a smartphone app. The collection unit can also collect health data such as heart rate and step count from a wearable device. Furthermore, the collection unit can collect customer behavior data based on website usage history. This allows for aggregating data from various channels and devices to create a more comprehensive customer profile. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data obtained from a smartphone app into a generation AI and have the generation AI analyze and aggregate the data.

[0080] The analysis unit can analyze daily activity data or health data to assess frailty risk. The analysis unit, for example, analyzes daily activity data. For example, the analysis unit analyzes data such as the number of steps, amount of exercise, and sleep patterns to assess frailty risk. The analysis unit can also analyze health data. For example, the analysis unit analyzes data such as blood pressure, heart rate, and weight to assess frailty risk. The analysis unit can also analyze a combination of daily activity data and health data. For example, the analysis unit analyzes a combination of step count and heart rate data to assess frailty risk. In this way, frailty risk can be accurately assessed by analyzing the daily activity data and health data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input daily activity data and health data into a generation AI and cause the generation AI to evaluate frailty risk.

[0081] The suggestion unit can suggest a specific exercise program or nutritional guidance based on the analysis results. The suggestion unit, for example, suggests a specific exercise program based on the analysis results. For example, the suggestion unit suggests an exercise program such as aerobic exercise or strength training. The suggestion unit can also suggest nutritional guidance based on the analysis results. For example, the suggestion unit suggests a meal plan or guidance on nutritional balance. Furthermore, the suggestion unit can also suggest a combination of an exercise program and nutritional guidance. For example, the suggestion unit suggests a combination of aerobic exercise and a meal plan. This makes it possible to provide individually optimized care by suggesting appropriate care based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the analysis results into a generation AI and cause the generation AI to suggest an exercise program or nutritional guidance.

[0082] The data combination unit can combine anonymized data from companies and platforms and provide it to the analysis unit. The data combination unit, for example, combines anonymized data from companies and platforms. For example, the data combination unit anonymizes data by deleting personally identifiable information and masking the data. The data combination unit can also provide the anonymized data to the analysis unit. For example, the data combination unit combines a company's customer data with platform usage data and provides it to the analysis unit. The data combination unit can also perform analysis based on the anonymized data. For example, the data combination unit analyzes customer behavior patterns based on the anonymized data. This allows for combining anonymous data to achieve deeper customer understanding and improve the effectiveness of care. Some or all of the above-mentioned processing in the data combination unit may be performed using, for example, AI, or may be performed without AI. For example, the data combination unit can input anonymized data into a generation AI and have the generation AI perform data combination and analysis.

[0083] The data management unit can manage customer data in an environment that emphasizes privacy protection. For example, the data management unit manages customer data in an environment that emphasizes privacy protection. For example, the data management unit achieves privacy protection by encrypting data and controlling access. The data management unit can also manage the storage location and access permissions of customer data. For example, the data management unit stores customer data on a secure server so that only specific users can access it. Furthermore, the data management unit can monitor the usage history of customer data and prevent unauthorized access. For example, the data management unit records an access log of customer data and detects abnormal access. This ensures data security by managing customer data in an environment that emphasizes privacy protection. Some or all of the above-mentioned processing in the data management unit may be performed using, or without, AI. For example, the data management unit may have a generation AI perform encryption of customer data and access control.

[0084] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using facial expression recognition or voice analysis. The collection unit can also adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can reduce the frequency of data collection when the user is stressed, thereby reducing the user's burden. The collection unit can also increase the frequency of data collection when the user is relaxed, thereby collecting more detailed data. Furthermore, the collection unit can temporarily stop data collection when the user is in a hurry and resume it later. This allows the user's burden to be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history. For example, the collection unit analyzes past collection dates and times and the type of collected data to select the optimal collection method. The collection unit can also collect data by preferentially using devices that the user has used in the past. For example, the collection unit can collect data during time periods when the user has provided data with high accuracy in the past. Furthermore, the collection unit can eliminate data collection methods that the user has avoided in the past and select other methods. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into a generation AI and cause the generation AI to select the optimal collection method.

[0086] The collection unit can filter data based on the user's current health condition and lifestyle when collecting data. The collection unit, for example, evaluates the user's current health condition. For example, the collection unit evaluates the user's health condition based on medical records or self-reports. The collection unit can also evaluate the user's lifestyle. For example, the collection unit evaluates the user's lifestyle based on lifestyle habits and home environment. Furthermore, the collection unit can filter data based on the user's current health condition and lifestyle when collecting data. For example, the collection unit temporarily stops data collection when the user is in poor health. The collection unit can also prioritize collecting exercise data when the user is exercising. Furthermore, the collection unit can collect heart rate and sleep data when the user is resting. This allows more appropriate data to be collected by filtering data according to the user's health condition and lifestyle. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's health condition and lifestyle to the generation AI and have the generation AI perform filtering.

[0087] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit, for example, evaluates the user's input method. For example, the collection unit evaluates input methods such as voice input, text input, and image input. The collection unit can also select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. If the user prefers text input, the collection unit can preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can preferentially collect image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method to the generation AI and cause the generation AI to select an appropriate collection means.

[0088] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using facial expression recognition or voice analysis. The collection unit can also determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related data. If the user is relaxed, the collection unit can also prioritize collecting relaxation-related data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data related to urgent situations. In this way, by determining the priority of data according to the user's emotions, more important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, collects the user's geographical location information. For example, the collection unit collects the user's geographical location information using GPS data or a location information service. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting activity data at home. Also, when the user is out, the collection unit can prioritize collecting activity data while away from home. Furthermore, when the user is in a specific facility, the collection unit can prioritize collecting data related to the facility. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to collect highly relevant data.

[0090] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit analyzes the content of social media posts and the number of likes. The collection unit can also collect related data based on the user's social media activity. For example, the collection unit collects related health data based on the content of the user's social media posts. The collection unit can also collect related data by referring to the activities of the user's friends on social media. Furthermore, the collection unit can collect related data based on the user's social media check-in information. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect related data.

[0091] The collection unit can customize the collection method based on the user's past feedback when collecting data. The collection unit, for example, collects the user's past feedback. For example, the collection unit collects the user's ratings and comments. The collection unit can also customize the collection method based on the user's past feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use collection methods that the user previously preferred. Furthermore, the collection unit can eliminate collection methods that the user previously avoided and select other methods. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0092] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using facial expression recognition or voice analysis. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a simple, highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a summary analysis result when the user is in a hurry. This allows the analysis result to be adjusted according to the user's emotion, thereby deepening understanding of the analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis taking into account the importance of the data. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit evaluates the importance of the data based on the frequency of use and impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0094] During analysis, the analysis unit can apply different analysis algorithms based on the data category. The analysis unit, for example, classifies the data category. For example, the analysis unit may classify the data into categories such as health data, behavioral data, and social media data. The analysis unit can also apply different analysis algorithms based on the data category. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply a behavior-related analysis algorithm to behavioral data. The analysis unit can also apply a social media-related analysis algorithm to social media data. This improves the accuracy of analysis by applying an analysis algorithm according to the data category. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, collects the user's past analysis results. For example, the analysis unit collects past evaluation scores and analysis reports. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using facial expression recognition or voice analysis. The analysis unit can also adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can perform a short and to-the-point analysis when the user is in a hurry. The analysis unit can also perform a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform a visually stimulating analysis when the user is excited. This allows the analysis length to be adjusted according to the user's emotion, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0097] During analysis, the analysis unit can determine the analysis priority taking into account the time when the data was collected. The analysis unit, for example, evaluates the time when the data was collected. For example, the analysis unit evaluates the time when the data was collected based on the collection date and time and the collection frequency. The analysis unit can also determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also analyze current data with reference to past data. Furthermore, the analysis unit can prioritize analyzing data from a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and have the generation AI determine the analysis priority.

[0098] During analysis, the analysis unit can adjust the order of analysis taking into account the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit evaluates the relevance of the data using correlation analysis or co-occurrence networks. The analysis unit can also adjust the order of analysis based on 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. Furthermore, the analysis unit can adjust 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-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0099] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on a questionnaire survey or past usage history. The analysis unit can also adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit uses a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis based on the user's level of expertise. This allows the analysis results to be better understood by adjusting the use of technical terms based on the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's level of expertise into a generation AI and have the generation AI use technical terms.

[0100] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This allows the suggestion unit to adjust the way in which suggestions are expressed based on the user's emotion, thereby providing optimal suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0101] When making a proposal, the suggestion unit can adjust the level of detail of the proposal taking into account the importance of care. The suggestion unit, for example, evaluates the importance of care. For example, the suggestion unit evaluates the importance of care based on an assessment by a medical professional or a risk score. The suggestion unit can also adjust the level of detail of the proposal based on the importance of care. For example, the suggestion unit makes a detailed proposal for care of high importance. The suggestion unit can also make a simplified proposal for care of low importance. Furthermore, the suggestion unit can also make a proposal with an appropriate level of detail for care of medium importance. As a result, adjusting the level of detail of the proposal based on the importance of care enables efficient care proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of care to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0102] When making a suggestion, the suggestion unit can apply different suggestion algorithms based on the care category. The suggestion unit, for example, classifies care categories. For example, the suggestion unit classifies care categories into exercise care, nutrition care, mental care, etc. The suggestion unit can also apply different suggestion algorithms based on the care category. For example, the suggestion unit can apply an exercise-related suggestion algorithm to an exercise program. The suggestion unit can also apply a nutrition-related suggestion algorithm to nutritional guidance. The suggestion unit can also apply a mental care-related suggestion algorithm to mental care. In this way, by applying a suggestion algorithm according to the care category, the suggestion accuracy is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the care category to the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0103] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. The suggestion unit, for example, collects the user's past suggestion results. For example, the suggestion unit collects the success rate of past suggestions and user feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit corrects the current suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. Furthermore, the suggestion unit can improve the accuracy of the suggestion by using the user's past suggestion results. In this way, the accuracy of the current suggestion is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0104] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide a short and to-the-point suggestion when the user is in a hurry. The suggestion unit can also provide a detailed suggestion when the user is relaxed. Furthermore, the suggestion unit can also provide a visually stimulating suggestion when the user is excited. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, thereby providing the optimal suggestion for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0105] When making a proposal, the suggestion unit can determine the priority of the proposals taking into account the timing of care implementation. The suggestion unit, for example, evaluates the timing of care implementation. For example, the suggestion unit evaluates the timing of care implementation based on the start date and frequency of care implementation. The suggestion unit can also determine the priority of the proposals based on the timing of care implementation. For example, the suggestion unit prioritizes proposals that need to be implemented in the near future. The suggestion unit can also postpone proposals for care that will be implemented over the long term. Furthermore, the suggestion unit can also determine the priority of the proposals according to the timing of care implementation. In this way, by determining the priority of the proposals based on the timing of care implementation, care can be proposed at an appropriate time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the timing of care implementation to the generation AI and cause the generation AI to determine the priority of the proposals.

[0106] The suggestion unit can adjust the order of suggestions taking into account the relevance of care when making suggestions. The suggestion unit, for example, evaluates the relevance of care. For example, the suggestion unit evaluates the relevance of care using correlation analysis or a co-occurrence network. The suggestion unit can also adjust the order of suggestions based on the relevance of care. For example, the suggestion unit prioritizes suggesting highly relevant care. The suggestion unit can also postpone suggesting less relevant care. The suggestion unit can also adjust the order of suggestions according to the relevance of care. As a result, adjusting the order of suggestions based on the relevance of care enables efficient care suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the relevance of care to a generation AI and cause the generation AI to adjust the order of suggestions.

[0107] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal based on the user's level of expertise. The suggestion unit, for example, evaluates the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise based on a questionnaire survey or past usage history. The suggestion unit can also adjust the use of technical terms in the proposal based on the user's level of expertise. For example, the suggestion unit uses a lot of technical terms if the user has technical expertise. The suggestion unit can also avoid technical terms if the user does not have technical expertise. The suggestion unit can also adjust the use of technical terms in the proposal based on the user's level of expertise. This allows the user to better understand the proposed content by adjusting the use of technical terms based on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise to a generation AI and cause the generation AI to use technical terms.

[0108] The data integration unit can estimate the user's emotions and adjust the data integration criteria based on the estimated user emotions. The data integration unit, for example, estimates the user's emotions. For example, the data integration unit estimates the user's emotions using facial expression recognition or voice analysis. The data integration unit can also adjust the data integration criteria based on the estimated user emotions. For example, if the user is nervous, the data integration unit uses simple, highly visible data integration criteria. If the user is relaxed, the data integration unit can use detailed data integration criteria. If the user is in a hurry, the data integration unit can also use data integration criteria that focus on the main points. This allows the data integration criteria to be adjusted according to the user's emotions, thereby achieving optimal data integration for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data integration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data combination unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0109] The data combination unit can improve the accuracy of data combination based on the interrelationships between data when combining data. The data combination unit, for example, evaluates the interrelationships between data. For example, the data combination unit evaluates the interrelationships between data using correlation analysis or co-occurrence networks. The data combination unit can also improve the accuracy of combination based on the interrelationships between data. For example, the data combination unit analyzes the interrelationships between data and preferentially combines highly related data. The data combination unit can also improve the accuracy of combination by taking the interrelationships between data into consideration. Furthermore, the data combination unit can adjust the combination algorithm based on the interrelationships between data. In this way, the accuracy of combination is improved by taking the interrelationships between data into consideration. Some or all of the above-mentioned processing in the data combination unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combination unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of combination.

[0110] When combining data, the data combining unit can perform the combination based on the attribute information of the data provider. The data combining unit, for example, collects attribute information of the data provider. For example, the data combining unit collects attribute information such as age, gender, and occupation. The data combining unit can also perform the combination based on the attribute information of the data provider. For example, the data combining unit preferentially combines highly relevant data based on the attribute information of the data provider. The data combining unit can also improve the accuracy of the combination by taking the attribute information of the data provider into consideration. Furthermore, the data combining unit can adjust the combination algorithm based on the attribute information of the data provider. This improves the accuracy of the combination by taking the attribute information of the data provider into consideration. Some or all of the above-mentioned processing in the data combining unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combining unit can input the attribute information of the data provider to the generation AI and cause the generation AI to improve the accuracy of the combination.

[0111] The data combining unit can weight the data based on the frequency of data provision when combining data. The data combining unit, for example, evaluates the frequency of data provision. For example, the data combining unit evaluates the frequency of data provision based on the number of times data is provided or the interval between data provision. The data combining unit can also weight the data based on the frequency of data provision. For example, the data combining unit can prioritize combining data with a high frequency of provision. The data combining unit can also combine data with a low frequency of provision later. The data combining unit can also weight the data based on the frequency of data provision. In this way, weighting the data based on the frequency of data provision enables efficient data combination. Some or all of the above-described processing in the data combining unit may be performed using AI, for example, or may be performed without using AI. For example, the data combining unit can input the frequency of data provision to a generation AI and cause the generation AI to perform the weighting of the data provision.

[0112] The data combining unit can estimate the user's emotion and determine the priority of data to be combined based on the estimated user's emotion. The data combining unit, for example, estimates the user's emotion. For example, the data combining unit estimates the user's emotion using facial expression recognition or voice analysis. The data combining unit can also determine the priority of data to be combined based on the estimated user's emotion. For example, if the user is feeling stressed, the data combining unit can preferentially combine stress-related data. If the user is relaxed, the data combining unit can also preferentially combine relaxation-related data. Furthermore, if the user is in a hurry, the data combining unit can preferentially combine data related to urgent situations. In this way, by determining the priority of data according to the user's emotion, more important data can be preferentially combined. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data combining unit may be performed using, for example, an AI, or without using an AI. For example, the data combination unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0113] When combining data, the data combining unit can perform the combining based on the geographic distribution of the data. The data combining unit, for example, evaluates the geographic distribution of the data. For example, the data combining unit evaluates the geographic distribution of the data based on regional data or geographic clusters. The data combining unit can also perform the combining based on the geographic distribution of the data. For example, the data combining unit can prioritize combining geographically close data. The data combining unit can also prioritize combining geographically distant data. Furthermore, the data combining unit can adjust the order of combining taking into account the geographic distribution of the data. In this way, by taking into account the geographic distribution of the data, highly related data can be prioritized to be combined. Some or all of the above-described processing in the data combining unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combining unit can input the geographic distribution of the data to the generation AI and cause the generation AI to adjust the order of combining.

[0114] When combining data, the data combining unit can improve the accuracy of the combination by referring to literature related to the data. The data combining unit, for example, refers to literature related to the data. For example, the data combining unit refers to academic papers and technical reports. The data combining unit can also improve the accuracy of the combination based on literature related to the data. For example, the data combining unit can adjust the combination algorithm based on the related literature. The data combining unit can also improve the accuracy of the combination by referring to the related literature. Furthermore, the data combining unit can set combination criteria based on the related literature. In this way, by referring to the related literature, the accuracy of the combination is improved. Some or all of the above-mentioned processing in the data combining unit may be performed using, for example, AI, or may be performed without using AI. For example, the data combining unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the combination.

[0115] When combining data, the data combining unit can perform the combination based on the market value of the data. The data combining unit, for example, evaluates the market value of the data. For example, the data combining unit evaluates the market value of the data based on the trading price and demand forecast of the data. The data combining unit can also perform the combination based on the market value of the data. For example, the data combining unit prioritizes combining data with high market value. The data combining unit can also combine data with low market value later. Furthermore, the data combining unit can adjust the order of combination taking into account the market value of the data. In this way, by taking into account the market value of the data, data with high value can be combined preferentially. Some or all of the above-mentioned processing in the data combining unit may be performed using, or without, AI. For example, the data combining unit can input the market value of the data to the generation AI and cause the generation AI to adjust the order of combination.

[0116] The data management unit can estimate the user's emotions and adjust the data management method based on the estimated user emotions. The data management unit, for example, estimates the user's emotions. For example, the data management unit estimates the user's emotions using facial expression recognition or voice analysis. The data management unit can also adjust the data management method based on the estimated user emotions. For example, the data management unit can provide a simple, highly visible data management method when the user is nervous. The data management unit can also provide a detailed data management method when the user is relaxed. Furthermore, the data management unit can also provide a data management method that focuses on the main points when the user is in a hurry. This allows the data management method to be adjusted according to the user's emotions, thereby achieving optimal data management for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data management unit can be performed using, for example, AI, or without AI. For example, the data management unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0117] The data management unit can adjust the level of detail of management during data management, taking into account the importance of the data. The data management unit, for example, evaluates the importance of the data. For example, the data management unit evaluates the importance of the data based on the frequency of use and the impact of the data. The data management unit can also adjust the level of detail of management based on the importance of the data. For example, the data management unit performs detailed management for data with high importance. The data management unit can also perform simplified management for data with low importance. Furthermore, the data management unit can also manage data with an appropriate level of detail for data with medium importance. This enables efficient data management by adjusting the level of detail of management based on the importance of the data. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the importance of the data to a generation AI and have the generation AI adjust the level of detail of management.

[0118] The data management unit can apply different management methods based on the data category during data management. The data management unit, for example, classifies data categories. For example, the data management unit classifies data into categories such as health data, behavioral data, and social media data. The data management unit can also apply different management methods based on the data category. For example, the data management unit can apply a health-related management method to health data. The data management unit can also apply a behavior-related management method to behavioral data. Furthermore, the data management unit can also apply a social media-related management method to social media data. In this way, by applying a management method according to the data category, management accuracy is improved. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the data category to the generation AI and cause the generation AI to apply an appropriate management method.

[0119] During data management, the data management unit can improve the accuracy of management based on the user's past management results. The data management unit, for example, collects the user's past management results. For example, the data management unit collects the success rate of past management and user feedback. The data management unit can also improve the accuracy of management based on the user's past management results. For example, the data management unit corrects the current management method based on the user's past management results. The data management unit can also adjust the management method by referring to the user's past management results. Furthermore, the data management unit can improve the accuracy of management by using the user's past management results. In this way, the accuracy of current management is improved by referring to the past management results. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the user's past management results into a generation AI and have the generation AI improve the accuracy of management.

[0120] The data management unit can estimate the user's emotion and determine the priority of data to be managed based on the estimated user's emotion. The data management unit, for example, estimates the user's emotion. For example, the data management unit estimates the user's emotion using facial expression recognition or voice analysis. The data management unit can also determine the priority of data to be managed based on the estimated user's emotion. For example, if the user is feeling stressed, the data management unit can prioritize managing stress-related data. If the user is relaxed, the data management unit can also prioritize managing relaxation-related data. Furthermore, if the user is in a hurry, the data management unit can prioritize managing data related to urgent situations. In this way, by determining the priority of data according to the user's emotion, more important data can be managed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data management unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0121] During data management, the data management unit can determine management priorities taking into account the time when the data was collected. The data management unit, for example, evaluates the time when the data was collected. For example, the data management unit evaluates the time when the data was collected based on the collection date and time and the collection frequency. The data management unit can also determine management priorities based on the time when the data was collected. For example, the data management unit prioritizes managing the most recent data. The data management unit can also manage current data by referring to past data. Furthermore, the data management unit can prioritize managing data from a specific period. In this way, by determining management priorities based on the time when the data was collected, the most recent data can be managed preferentially. Some or all of the above-described processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the time when the data was collected into the generation AI and have the generation AI determine the management priorities.

[0122] The data management unit can adjust the management order taking into account the relevance of the data during data management. The data management unit, for example, evaluates the relevance of the data. For example, the data management unit evaluates the relevance of the data using correlation analysis or co-occurrence networks. The data management unit can also adjust the management order based on the relevance of the data. For example, the data management unit prioritizes management of highly relevant data. The data management unit can also manage less relevant data later. The data management unit can also adjust the management order according to the relevance of the data. This enables efficient data management by adjusting the management order based on the relevance of the data. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input the relevance of the data to a generation AI and have the generation AI adjust the management order.

[0123] During data management, the data management unit can adjust the use of technical terms in the management based on the user's level of expertise. The data management unit, for example, evaluates the user's level of expertise. For example, the data management unit evaluates the user's level of expertise based on a questionnaire survey or past usage history. The data management unit can also adjust the use of technical terms in the management based on the user's level of expertise. For example, the data management unit uses a lot of technical terms if the user has technical expertise. The data management unit can also avoid technical terms if the user does not have technical expertise. The data management unit can also adjust the use of technical terms in the management based on the user's level of expertise. This allows the user to deepen their understanding of the management content by adjusting the use of technical terms based on the user's level of expertise. Some or all of the above-described processing in the data management unit can be performed using, for example, AI, or without AI. For example, the data management unit can input the user's level of expertise into a generation AI and have the generation AI use technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, data combination unit, and data management unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 or microphone 38B of the smart device 14. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to assess frailty risk. The suggestion unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes individually optimized care based on the analysis results. The data combination unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, combines anonymized data from companies and platforms. The data management unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, manages customer data in an environment that emphasizes privacy protection. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, data combination unit, and data management unit, described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to assess frailty risk. The suggestion unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes individually optimized care based on the analysis results. The data combination unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, combines anonymized data from companies and platforms. The data management unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, manages customer data in an environment that emphasizes privacy protection. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, data combination unit, and data management unit, described above, is implemented, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 or microphone 238 of the headset-type terminal 314. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to assess frailty risk. The suggestion unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes individually optimized care based on the analysis results. The data combination unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, combines anonymized data from companies and platforms. The data management unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, manages customer data in an environment that emphasizes privacy protection. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, data combination unit, and data management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect customer data using the camera 42 and microphone 238 of the robot 414. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to assess frailty risk. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes individually optimized care based on the analysis results. The data combination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, combines anonymized data from companies and platforms. The data management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, manages customer data in an environment that emphasizes privacy protection.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions using facial expression recognition or voice analysis. The collection unit can also adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. If the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This allows the user's burden to be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0126] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit estimates the user's emotions using facial expression recognition or voice analysis. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the analysis result to be adjusted according to the user's emotions, thereby deepening understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0127] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion. If the user is relaxed, the suggestion unit can provide a detailed suggestion. If the user is in a hurry, the suggestion unit can also provide a suggestion that focuses on the main points. This allows the suggestion to be optimally tailored to the user by adjusting the way the suggestion is expressed based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, 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 can be performed using, for example, an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0128] The data integration unit can estimate the user's emotions and adjust the data integration criteria based on the estimated user emotions. For example, the data integration unit estimates the user's emotions using facial expression recognition or voice analysis. The data integration unit can also adjust the data integration criteria based on the estimated user emotions. For example, if the user is nervous, the data integration unit uses simple, highly visible data integration criteria. If the user is relaxed, the data integration unit can use detailed data integration criteria. If the user is in a hurry, the data integration unit can also use data integration criteria that focus on the main points. This allows the data integration criteria to be adjusted according to the user's emotions, thereby achieving optimal data integration for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data integration unit can be performed, for example, using AI or without AI. For example, the data combination unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0129] The data management unit can estimate a user's emotions and adjust the data management method based on the estimated user emotions. For example, the data management unit estimates the user's emotions using facial expression recognition or voice analysis. The data management unit can also adjust the data management method based on the estimated user emotions. For example, if the user is nervous, the data management unit can provide a simple, highly visible data management method. If the user is relaxed, the data management unit can provide a detailed data management method. Furthermore, if the user is in a hurry, the data management unit can provide a data management method that focuses on the main points. This allows the data management method to be adjusted according to the user's emotions, thereby achieving optimal data management for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data management unit can be performed using, for example, AI, or without AI. For example, the data management unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0130] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit can analyze past collection dates and times and the type of collected data to select the optimal collection method. The collection unit can also collect data by preferentially using devices that the user has used favorably in the past. For example, the collection unit can collect data during time periods when the user has provided data with high accuracy in the past. Furthermore, the collection unit can eliminate data collection methods that the user has avoided in the past and select other methods. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0131] The collection unit can filter data based on the user's current health condition and lifestyle when collecting data. For example, the collection unit evaluates the user's current health condition. For example, the collection unit evaluates the user's health condition based on medical records and self-reports. The collection unit can also evaluate the user's lifestyle. For example, the collection unit evaluates the user's lifestyle based on lifestyle habits and home environment. Furthermore, the collection unit can filter data based on the user's current health condition and lifestyle when collecting data. For example, the collection unit temporarily stops data collection when the user is in poor health. Furthermore, the collection unit can prioritize collecting exercise data when the user is exercising. Furthermore, the collection unit can collect heart rate and sleep data when the user is resting. This allows more appropriate data to be collected by filtering data according to the user's health condition and lifestyle. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's health condition and lifestyle to the generation AI and have the generation AI perform filtering.

[0132] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, the collection unit evaluates the user's input method. For example, the collection unit evaluates input methods such as voice input, text input, and image input. The collection unit can also select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. If the user prefers text input, the collection unit can preferentially collect text data. Furthermore, if the user prefers image input, the collection unit can preferentially collect image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method to the generation AI and cause the generation AI to select an appropriate collection means.

[0133] During analysis, the analysis unit can apply different analysis algorithms based on the category of data. For example, the analysis unit classifies the data into categories such as health data, behavioral data, and social media data. The analysis unit can also apply different analysis algorithms based on the category of data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply a behavior-related analysis algorithm to behavioral data. The analysis unit can also apply a social media-related analysis algorithm to social media data. This improves the accuracy of analysis by applying an analysis algorithm according to the data category. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0134] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit collects the user's past analysis results. For example, the analysis unit collects past evaluation scores and analysis reports. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit corrects the current analysis results based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0135] When making a proposal, the suggestion unit can adjust the level of detail of the proposal taking into account the importance of care. For example, the suggestion unit evaluates the importance of care. For example, the suggestion unit evaluates the importance of care based on an assessment by a medical professional or a risk score. The suggestion unit can also adjust the level of detail of the proposal based on the importance of care. For example, the suggestion unit makes a detailed proposal for care of high importance. The suggestion unit can also make a simplified proposal for care of low importance. Furthermore, the suggestion unit can also make a proposal with an appropriate level of detail for care of medium importance. As a result, adjusting the level of detail of the proposal based on the importance of care enables efficient care proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the importance of care to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0136] When making a suggestion, the suggestion unit can apply different suggestion algorithms based on the care category. For example, the suggestion unit classifies the care category. For example, the suggestion unit may classify the care into categories such as exercise care, nutrition care, and mental care. The suggestion unit can also apply different suggestion algorithms based on the care category. For example, the suggestion unit can apply an exercise-related suggestion algorithm to an exercise program. The suggestion unit can also apply a nutrition-related suggestion algorithm to nutritional guidance. The suggestion unit can also apply a mental care-related suggestion algorithm to mental care. In this way, by applying a suggestion algorithm according to the care category, the suggestion accuracy is improved. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the care category to a generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0137] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit collects the user's past suggestion results. For example, the suggestion unit collects the success rate of past suggestions and user feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's past suggestion results. For example, the suggestion unit corrects the current suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. Furthermore, the suggestion unit can improve the accuracy of the suggestion by using the user's past suggestion results. In this way, the accuracy of the current suggestion is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0138] When making a proposal, the suggestion unit can determine the priority of the proposal taking into account the timing of care implementation. For example, the suggestion unit evaluates the timing of care implementation. For example, the suggestion unit evaluates the timing of care implementation based on the start date and frequency of care implementation. The suggestion unit can also determine the priority of the proposal based on the timing of care implementation. For example, the suggestion unit prioritizes proposals that need to be implemented in the near future. The suggestion unit can also postpone proposals for care that will be implemented over the long term. Furthermore, the suggestion unit can also determine the priority of the proposals according to the timing of care implementation. In this way, by determining the priority of the proposals based on the timing of care implementation, care can be proposed at an appropriate time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the timing of care implementation to the generation AI and cause the generation AI to determine the priority of the proposals.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The collection unit collects customer data. This data may include, for example, personal information, purchase history, and health data. The collection unit aggregates data from various channels and devices, such as smartphones, wearable devices, and websites. The collection unit can also adjust the frequency and means of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit and assesses frailty risk. The analysis unit analyzes daily activity data and health data, and assesses frailty risk using data such as the number of steps taken, amount of exercise, sleep patterns, blood pressure, heart rate, and weight. Step 3: The proposal unit proposes individually optimized care based on the evaluation results obtained by the analysis unit. The proposal unit proposes specific exercise programs and nutritional guidance, including aerobic exercise, strength training, meal plans, and nutritional balance guidance. Step 4: The data combiner combines the anonymized data from the companies and the platform and provides it to the analytics department. Data combiner removes personally identifiable information and masks the data. Step 5: The data management department manages customer data in an environment that emphasizes privacy protection. Data management is based on data encryption and access control.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Explanation of symbols]

[0213] 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 collection unit that collects customer data; an analysis unit that analyzes the data collected by the collection unit and evaluates frailty risk; a suggestion unit that proposes individually optimized care based on the evaluation results obtained by the analysis unit; a data combiner that combines anonymized data from companies and platforms; A data management unit that manages customer data in an environment that emphasizes privacy protection. A system characterized by:

2. The collecting unit Aggregate customer data from multiple channels and devices 2. The system of claim 1.

3. The analysis unit Analyzing daily activity or health data to assess frailty risk 2. The system of claim 1.

4. The proposal unit Recommend specific exercise programs or nutritional advice based on the analysis results 2. The system of claim 1.

5. The data combining unit Combine anonymized data from companies and platforms and provide it to the analytics department 2. The system of claim 1.

6. The data management unit Manage customer data in a privacy-focused environment 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past data collection history and select the appropriate collection method 2. The system of claim 1.

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