System

Abstract

An object of the system according to the embodiment is to propose a personalized diet plan based on the lifestyle and health condition of the user.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects data related to the lifestyle, eating habits, and health condition of the user. The analysis unit analyzes the data collected by the collection unit and understands the characteristics of the user. The suggestion unit suggests a personalized diet plan based on the characteristics of the user understood by the analysis unit. The monitoring unit monitors an execution result of the plan proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately provided personalized diet plans based on a user's lifestyle and health status, and there is room for improvement.

[0005] The system according to the embodiment aims to propose a personalized diet plan based on the user's lifestyle and health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects data related to a user's lifestyle, eating habits, and health status. The analysis unit analyzes the data collected by the collection unit to understand the user's characteristics. The proposal unit proposes a personalized diet plan based on the user's characteristics understood by the analysis unit. The monitoring unit monitors the results of implementing the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose a personalized diet plan based on the user's lifestyle and health condition. [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 health management system according to an embodiment of the present invention uses a generative AI to understand a user's unique lifestyle, eating habits, and health status, propose a personalized diet plan, and extend healthy lifespan. The health management system collects data on the user's lifestyle, eating habits, and health status, and the generative AI analyzes this data to understand the user's characteristics. The generative AI then proposes an optimal diet plan for the user, and the user implements the proposed plan, and the generative AI monitors the results. For example, in the health management system, the user inputs data using a smartphone app. The health management system also collects data in real time using a wearable device. For example, the user enters the contents of their meals into the app, and the wearable device measures their exercise volume and heart rate. The health management system then analyzes the collected data using a generative AI. The generative AI uses a machine learning algorithm to analyze the user's data and find patterns. For example, it analyzes the user's eating habits and exercise habits to identify factors affecting their health status. The generative AI then proposes an optimal diet plan for the user. This plan includes the contents of meals, the type and frequency of exercise, etc. For example, the system suggests a balanced meal menu based on the user's eating habits and provides an appropriate exercise plan based on their exercise habits. Finally, the health management system has the user implement the proposed plan, and the generation AI monitors the results. The system collects data in real time using a wearable device and periodically provides feedback through the app. For example, the system monitors the user's exercise volume and dietary content and adjusts the plan as needed. This is expected to improve the user's health and extend their healthy lifespan. For example, by implementing the proposed plan, the user can maintain a healthy lifestyle and reduce the risk of disease. Furthermore, the generation AI monitors data in real time and adjusts the plan as needed, enabling more effective health management.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects data related to a user's lifestyle, eating habits, and health condition. The user's lifestyle may include, but is not limited to, daily activities, type of work, and hobbies. The collection unit receives data from the user using, for example, a smartphone app. The collection unit may also collect data in real time using a wearable device. For example, the user may enter meal details into the app, and the wearable device may measure the amount of exercise and heart rate. The analysis unit uses a generation AI to analyze the data collected by the collection unit and understand the user's characteristics. The analysis may be performed using, for example, a machine learning algorithm, but is not limited to, an example. For example, the generation AI may analyze the user's eating habits and exercise habits to identify factors that affect the user's health condition. The proposal unit uses the generation AI to propose a personalized diet plan based on the user's characteristics understood by the analysis unit. The proposed plan may include, for example, but is not limited to, meal details, type and frequency of exercise, and the like. For example, the suggestion unit suggests a balanced meal menu based on the user's eating habits. The suggestion unit can also provide an appropriate exercise plan based on the user's exercise habits. The monitoring unit monitors the execution results of the plan suggested by the suggestion unit. Monitoring can be performed, for example, by collecting data in real time using a wearable device and periodically providing feedback through an app, but is not limited to such examples. For example, the monitoring unit monitors the user's exercise volume and dietary content and adjusts the plan as necessary. This allows the health management system according to the embodiment to improve the user's health condition and extend healthy lifespan. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired by the wearable device to a generation AI, which can analyze the data and provide feedback.

[0030] The collection unit allows a user to input data using a smartphone app. The smartphone app may include, but is not limited to, a data input interface and a notification function. For example, the collection unit allows a user to input meal details into the smartphone app. The collection unit may also allow a user to input data related to exercise volume and health status into the smartphone app. For example, the collection unit may provide a function that allows a user to record daily meal details and the app automatically calculates calories and nutrients. The collection unit may also provide a function that allows a user to input the type and duration of exercise and the app evaluates the effectiveness of the exercise. This allows the collection unit to efficiently collect data by allowing a user to input data through the smartphone app. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may send the data input by the user to a generation AI, which may analyze the data and provide feedback.

[0031] The collection unit can collect data in real time using a wearable device. Examples of wearable devices include, but are not limited to, fitness trackers and smart watches. For example, the collection unit can collect a user's exercise amount and heart rate in real time using the wearable device. The collection unit can also monitor a user's sleep patterns and stress level using the wearable device. For example, the collection unit can use a fitness tracker to record a user's steps and calories burned in real time. The collection unit can also use a smart watch to measure a user's heart rate and blood pressure and collect data. In this way, the collection unit can collect data in real time using the wearable device. 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 send data acquired by the wearable device to a generation AI, which can analyze the data and provide feedback.

[0032] The analysis unit can use a machine learning algorithm to analyze the user's data and find patterns. Examples of machine learning algorithms include, but are not limited to, deep learning and support vector machines. For example, the analysis unit can use deep learning to analyze the user's eating habits and exercise habits and identify factors that affect their health status. The analysis unit can also use a support vector machine to assess the user's health risks. For example, the analysis unit can use deep learning to analyze the user's dietary data and identify nutritional imbalances. The analysis unit can also use a support vector machine to analyze the user's exercise data and assess the risk of insufficient or excessive exercise. In this way, using a machine learning algorithm, the analysis unit can find patterns in the user's data and understand their characteristics. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's data into a generation AI, which can analyze the data and find patterns.

[0033] The suggestion unit can suggest a balanced meal menu based on the user's eating habits. A balanced meal menu includes, but is not limited to, nutrient balance and calorie restriction. The suggestion unit can suggest a nutrient-balanced meal menu based on the user's eating habits. The suggestion unit can also suggest a meal menu that includes calorie restriction according to the user's health condition. For example, the suggestion unit can analyze the user's dietary data and suggest a meal menu that compensates for vitamin and mineral deficiencies. The suggestion unit can also suggest low-calorie and high-protein meal menus according to the user's health goals. This allows the suggestion of a balanced meal menu based on the user's eating habits to support a healthy diet. Some or all of the above-described processing by the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's dietary data into the generation AI, which can then suggest a balanced meal menu.

[0034] The suggestion unit can provide an appropriate exercise plan based on the user's exercise habits. An appropriate exercise plan includes, but is not limited to, the type, frequency, and intensity of exercise. The suggestion unit can provide an appropriate exercise plan based on, for example, the user's exercise habits. The suggestion unit can also adjust the intensity and frequency of exercise according to the user's health condition. For example, the suggestion unit can analyze the user's exercise data and provide aerobic exercise and strength training plans. The suggestion unit can also suggest how many times a week the user should exercise according to their health goals. This allows for effective exercise support by providing an appropriate exercise plan based on the user's exercise habits. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's exercise data into the generation AI, which can then suggest an appropriate exercise plan.

[0035] The monitoring unit can collect data in real time using a wearable device and periodically provide feedback through an app. Examples of feedback include, but are not limited to, notification frequency and feedback format. For example, the monitoring unit can collect the user's exercise volume and heart rate in real time using a wearable device and periodically provide feedback through an app. The monitoring unit can also monitor the user's diet and health status and adjust the plan as needed. For example, the monitoring unit can send a notification if the user is not exercising enough to encourage the user to exercise. The monitoring unit can also provide advice if the user's diet is unbalanced. In this way, by collecting data in real time using a wearable device and periodically providing feedback, the user's health status can be continuously monitored. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can send data acquired by the wearable device to a generation AI, which can analyze the data and provide feedback.

[0036] The collection unit can analyze the user's past data input history and select the optimal collection method. The optimal collection method can be, for example, based on the type of data and collection efficiency, but is not limited to these examples. For example, the collection unit can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The collection unit can also analyze patterns of data previously input by the user and determine the optimal collection timing. For example, the collection unit selects the type of data to collect during a specific time period from the user's past input history. This allows the optimal collection method to be selected by analyzing the user's past data input history. Some or all of the above-described 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 past data input history into a generation AI, which can select the optimal collection method.

[0037] The collection unit can filter data based on the user's current health condition and lifestyle when collecting data. Examples of filtering include, but are not limited to, the importance and relevance of data. For example, if the user is tired, the collection unit can temporarily suspend data collection and resume it after the user has rested. The collection unit can also set the optimal timing for data collection to match the user's lifestyle. For example, the collection unit can adjust the type of data to be collected based on the user's health condition. This allows more appropriate data to be collected by filtering the data based on the user's current health condition and lifestyle. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's health condition data into a generation AI, which can then filter and collect the data.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the collection unit can collect data using voice recognition technology. Alternatively, if the user prefers text input, the collection unit can collect data by providing a simple input form. For example, if the user prefers image input, the collection unit can collect data using image recognition technology. This allows efficient 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 the user's input data into a generation AI, which then selects the optimal collection means.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, when the user is in a specific area, the collection unit can prioritize collecting health data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's eating habits and exercise environment at the travel destination. For example, when the user is at home, the collection unit prioritizes collecting data related to daily life. In this way, by taking the user's geographical location information into account, highly relevant data can be prioritized. 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 into the generation AI, which can then prioritize collecting highly relevant data.

[0040] During data collection, the collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The collection unit can, for example, collect the meal details shared by the user on social media. The collection unit can also collect the user's exercise records on social media. For example, the collection unit can analyze the user's health-related posts on social media and collect related data. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described 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 social media data into a generation AI, which can collect related data.

[0041] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. Feedback includes, but is not limited to, user opinions, ratings, and comments. For example, the collection unit can adjust the frequency of data collection based on the user's past feedback. The collection unit can also select the type of data to collect by referring to the user's past feedback. For example, the collection unit improves the data collection interface based on the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback. 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 user's feedback data into a generation AI, which can customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, the impact and relevance of the data. For example, the analysis unit can perform a detailed analysis of highly important data to provide deep insights. The analysis unit can also perform a simplified analysis of less important data to provide quick results. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which can then adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, health data, behavioral data, and environmental data. For example, the analysis unit can apply a nutritional balance analysis algorithm to dietary data. The analysis unit can also apply an exercise effect analysis algorithm to exercise data. For example, the analysis unit can apply a health risk analysis algorithm to health condition data. By applying different analysis algorithms depending on the data category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI, which can then apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past trends and patterns. For example, the analysis unit can improve the accuracy of current data analysis based on the user's past analysis results. The analysis unit can also find specific patterns from the user's past analysis results and reflect them in the current analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis results. This can improve the accuracy of the current analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine analysis priorities based on the time of data collection. The time of data collection includes, but is not limited to, the frequency and timing of collection. The analysis unit can, for example, prioritize analysis of the most recent data to provide real-time insights. The analysis unit can also improve the accuracy of current data analysis by referring to past data. For example, the analysis unit optimally allocates analysis resources according to the time of data collection. This allows real-time insights to be provided by determining analysis priorities based on the time of data collection. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the time of data collection into the generation AI, which can then determine the analysis priorities.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Data relevance includes, but is not limited to, correlation and causal relationships between data. For example, the analysis unit can prioritize analysis of highly relevant data and provide results quickly. The analysis unit can also postpone analysis of less relevant data and focus on analyzing important data. For example, the analysis unit optimally allocates analysis resources according to the relevance of the data. This allows for focusing on analyzing important data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Examples of technical terminology include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of more understandable analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0048] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the plan. The importance of the plan includes, but is not limited to, the impact and urgency of the plan. For example, the proposal unit can provide detailed proposals for highly important plans to provide deep insights. The proposal unit can also provide simplified proposals for less important plans to quickly provide results. For example, the proposal unit optimally allocates proposal resources according to the importance of the plan. By adjusting the level of detail of the proposal based on the importance of the plan, efficient proposals can be made. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the importance of the plan to the generation AI, which can then adjust the level of detail of the proposal.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. Examples of plan categories include, but are not limited to, short-term plans, long-term plans, and health plans. For example, the proposal unit can apply a nutritional balance proposal algorithm to a meal plan. The proposal unit can also apply an exercise effect proposal algorithm to an exercise plan. For example, the proposal unit can apply a health risk proposal algorithm to a health management plan. By applying different proposal algorithms depending on the category of the plan, more accurate proposals can be provided. Some or all of the above-described processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input the category of the plan into the generation AI, which can then apply an appropriate proposal algorithm.

[0050] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, for example, past successful and unsuccessful examples, but are not limited to such examples. The suggestion unit can improve the accuracy of the current proposal, for example, based on the user's past proposal results. The suggestion unit can also find specific patterns from the user's past proposal results and reflect them in the current proposal. For example, the suggestion unit can optimize the proposal algorithm by referring to the user's past proposal results. In this way, the accuracy of the current proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past proposal results into the generation AI, which can improve the accuracy of the proposal.

[0051] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the plan. The time of submission of the plan includes, but is not limited to, for example, the frequency and timing of submission. For example, the proposal unit can prioritize plans with high urgency and respond quickly. The proposal unit can also prioritize plans with upcoming submission dates and encourage the execution of the plans. For example, the proposal unit optimally allocates proposal resources according to the submission time. This allows for a quick response by determining the priority of proposals based on the time of submission of the plans. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the time of submission of the plan into the generation AI, which can then determine the priority of the proposals.

[0052] The suggestion unit can adjust the order of proposals based on the relevance of the plans when making a proposal. The relevance of the plans includes, but is not limited to, correlations and causal relationships between the plans. For example, the suggestion unit can prioritize proposing highly relevant plans and quickly provide results. The suggestion unit can also postpone less relevant plans and focus on proposing important plans. For example, the suggestion unit optimally allocates proposal resources according to the relevance of the plans. This allows the suggestion unit to focus on proposing important plans by adjusting the order of proposals based on the relevance of the plans. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the relevance of the plans into the generation AI, which can then adjust the order of proposals.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has technical expertise, the suggestion unit can provide the proposal using detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide the proposal in simple language. For example, the suggestion unit adjusts the way the proposal is expressed depending on the user's level of expertise. This allows the proposal to be more easily understood by adjusting the use of technical terminology in the proposal depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology in the proposal.

[0054] During monitoring, the monitoring unit can analyze the user's past behavioral history to select the optimal monitoring method. The past behavioral history includes, for example, past activity data and behavioral patterns, but is not limited to these examples. The monitoring unit can select the optimal monitoring method, for example, based on behaviors the user frequently performed in the past. The monitoring unit can also perform monitoring during a specific time period based on the user's past behavioral history. For example, the monitoring unit analyzes the user's past behavioral history to improve the accuracy of monitoring. This allows the optimal monitoring method to be selected by analyzing the user's past behavioral history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past behavioral history into a generation AI, which can select the optimal monitoring method.

[0055] During monitoring, the monitoring unit can customize the monitoring means based on the user's current health condition. Examples of the current health condition include, but are not limited to, weight, blood pressure, and blood glucose level. For example, if the user is tired, the monitoring unit can reduce the frequency of monitoring and resume monitoring after the user has rested. The monitoring unit can also adjust the monitoring means according to the user's health condition. For example, the monitoring unit can change the monitoring data collection method based on the user's health condition. This allows for more appropriate monitoring by customizing the monitoring means based on the user's current health condition. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's health condition data into a generation AI, which can then customize the monitoring means.

[0056] During monitoring, the monitoring unit can improve the monitoring method by reflecting user feedback. Feedback includes, for example, user opinions, evaluations, and comments, but is not limited to these examples. The monitoring unit can, for example, adjust the frequency of monitoring based on the feedback provided by the user. The monitoring unit can also improve the monitoring means by referring to the user feedback. For example, the monitoring unit improves the monitoring interface based on the user feedback. In this way, the monitoring method can be improved by reflecting the user feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input user feedback data into the generation AI, which can then improve the monitoring method.

[0057] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, when the user is in a specific area, the monitoring unit can prioritize monitoring health data related to that area. Furthermore, when the user is traveling, the monitoring unit can prioritize monitoring data related to the user's eating habits and exercise environment at the travel destination. For example, when the user is at home, the monitoring unit prioritizes monitoring data related to daily life. This allows the optimal monitoring method to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into a generation AI, which can select the optimal monitoring method.

[0058] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The monitoring unit can, for example, monitor the meal contents shared by the user on social media. The monitoring unit can also monitor the user's exercise records on social media. For example, the monitoring unit analyzes the user's health-related posts on social media and monitors related data. This allows for efficient monitoring of related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI, which then monitors the related data.

[0059] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. Feedback includes, but is not limited to, the user's opinions, ratings, and comments, for example. The monitoring unit can adjust the frequency of monitoring, for example, based on feedback provided by the user in the past. The monitoring unit can also improve the monitoring means by referring to the user's past feedback. For example, the monitoring unit improves the monitoring interface based on the user's feedback. In this way, the monitoring method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's feedback data into the generation AI, which can then customize the monitoring method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The health management system may further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit collects the user's sleep data and analyzes the quality and patterns of sleep. For example, it can monitor the user's sleep time, the ratio of deep sleep to light sleep, the number of awakenings during the night, etc. The sleep analysis unit can also provide advice to improve the quality of sleep based on the user's sleep data. For example, it can suggest ways to relax before bed or an appropriate sleeping environment. This can improve the user's sleep quality and is expected to improve their overall health.

[0062] The analysis unit can predict future health risks based on the user's past health data. For example, it can analyze past dietary habits, exercise habits, and health status data to identify potential future health risks. The analysis unit can also suggest preventive measures based on the predicted health risks. For example, it can suggest increasing intake of certain nutrients or increasing specific exercise. This allows the user to understand future health risks in advance and take appropriate measures.

[0063] The monitoring unit can select an optimal monitoring method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, when the user is in a specific area, the monitoring unit can prioritize monitoring health data related to that area. Furthermore, when the user is traveling, the monitoring unit can prioritize monitoring data related to the user's eating habits and exercise environment at the travel destination. For example, when the user is at home, the monitoring unit prioritizes monitoring data related to daily life. This allows the optimal monitoring method to be selected by taking into account the user's geographical location information.

[0064] The health management system may further include a social analysis unit that analyzes a user's social media activity. The social analysis unit analyzes the user's social media posts, the number of likes, comments, and so on to gain insights into the user's health condition and lifestyle. For example, the social analysis unit can collect the diet and exercise records shared by the user on social media and use them to help manage their health. The social analysis unit can also provide health advice based on the user's social media activity. For example, if a user posts about a healthy diet, the social analysis unit can send a message praising the user's behavior and encouraging them to continue. This allows for more personalized health management by analyzing the user's social media activity.

[0065] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0066] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, but are not limited to, past successes and failures. The suggestion unit can improve the accuracy of current suggestions, for example, based on the user's past suggestion results. The suggestion unit can also find specific patterns from the user's past suggestion results and reflect them in current suggestions. For example, the suggestion unit can optimize the suggestion algorithm by referring to the user's past suggestion results. In this way, the accuracy of current suggestions can be improved by referring to the user's past suggestion results.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects data about the user's lifestyle, eating habits, and health status. The user's lifestyle includes daily activities, type of work, hobbies, etc. The collection unit includes a method in which the user inputs data using a smartphone app and a method in which data is collected in real time using a wearable device. For example, the user inputs the details of their meals into the app, and the wearable device measures their exercise volume and heart rate. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and understand the user's characteristics. The analysis is performed using machine learning algorithms. For example, the generation AI analyzes the user's eating habits and exercise habits to identify factors that affect their health. Step 3: The suggestion unit uses the generation AI to propose a personalized diet plan based on the user's characteristics understood by the analysis unit. The proposed plan includes meal content, exercise type, and frequency. For example, the suggestion unit suggests a balanced meal menu based on the user's eating habits and provides an appropriate exercise plan based on the user's exercise habits. Step 4: The monitoring unit monitors the results of the implementation of the plan proposed by the proposal unit. Monitoring is performed by collecting data in real time using a wearable device and providing feedback periodically through an app. For example, the monitoring unit may monitor the user's exercise volume and dietary habits and adjust the plan as necessary. Some or all of the processing in the monitoring unit may be performed using AI.

[0069] (Example 2) A health management system according to an embodiment of the present invention uses a generative AI to understand a user's unique lifestyle, eating habits, and health status, propose a personalized diet plan, and extend healthy lifespan. The health management system collects data on the user's lifestyle, eating habits, and health status, and the generative AI analyzes this data to understand the user's characteristics. The generative AI then proposes an optimal diet plan for the user, and the user implements the proposed plan, and the generative AI monitors the results. For example, in the health management system, the user inputs data using a smartphone app. The health management system also collects data in real time using a wearable device. For example, the user enters the contents of their meals into the app, and the wearable device measures their exercise volume and heart rate. The health management system then analyzes the collected data using a generative AI. The generative AI uses a machine learning algorithm to analyze the user's data and find patterns. For example, it analyzes the user's eating habits and exercise habits to identify factors affecting their health status. The generative AI then proposes an optimal diet plan for the user. This plan includes the contents of meals, the type and frequency of exercise, etc. For example, the system suggests a balanced meal menu based on the user's eating habits and provides an appropriate exercise plan based on their exercise habits. Finally, the health management system has the user implement the proposed plan, and the generation AI monitors the results. The system collects data in real time using a wearable device and periodically provides feedback through the app. For example, the system monitors the user's exercise volume and dietary content and adjusts the plan as needed. This is expected to improve the user's health and extend their healthy lifespan. For example, by implementing the proposed plan, the user can maintain a healthy lifestyle and reduce the risk of disease. Furthermore, the generation AI monitors data in real time and adjusts the plan as needed, enabling more effective health management.

[0070] A health management system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects data related to a user's lifestyle, eating habits, and health condition. The user's lifestyle may include, but is not limited to, daily activities, type of work, and hobbies. The collection unit receives data from the user using, for example, a smartphone app. The collection unit may also collect data in real time using a wearable device. For example, the user may enter meal details into the app, and the wearable device may measure the amount of exercise and heart rate. The analysis unit uses a generation AI to analyze the data collected by the collection unit and understand the user's characteristics. The analysis may be performed using, for example, a machine learning algorithm, but is not limited to, an example. For example, the generation AI may analyze the user's eating habits and exercise habits to identify factors that affect the user's health condition. The proposal unit uses the generation AI to propose a personalized diet plan based on the user's characteristics understood by the analysis unit. The proposed plan may include, for example, but is not limited to, meal details, type and frequency of exercise, and the like. For example, the suggestion unit suggests a balanced meal menu based on the user's eating habits. The suggestion unit can also provide an appropriate exercise plan based on the user's exercise habits. The monitoring unit monitors the execution results of the plan suggested by the suggestion unit. Monitoring can be performed, for example, by collecting data in real time using a wearable device and periodically providing feedback through an app, but is not limited to such examples. For example, the monitoring unit monitors the user's exercise volume and dietary content and adjusts the plan as necessary. This allows the health management system according to the embodiment to improve the user's health condition and extend healthy lifespan. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired by the wearable device to a generation AI, which can analyze the data and provide feedback.

[0071] The collection unit allows a user to input data using a smartphone app. The smartphone app may include, but is not limited to, a data input interface and a notification function. For example, the collection unit allows a user to input meal details into the smartphone app. The collection unit may also allow a user to input data related to exercise volume and health status into the smartphone app. For example, the collection unit may provide a function that allows a user to record daily meal details and the app automatically calculates calories and nutrients. The collection unit may also provide a function that allows a user to input the type and duration of exercise and the app evaluates the effectiveness of the exercise. This allows the collection unit to efficiently collect data by allowing a user to input data through the smartphone app. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may send the data input by the user to a generation AI, which may analyze the data and provide feedback.

[0072] The collection unit can collect data in real time using a wearable device. Examples of wearable devices include, but are not limited to, fitness trackers and smart watches. For example, the collection unit can collect a user's exercise amount and heart rate in real time using the wearable device. The collection unit can also monitor a user's sleep patterns and stress level using the wearable device. For example, the collection unit can use a fitness tracker to record a user's steps and calories burned in real time. The collection unit can also use a smart watch to measure a user's heart rate and blood pressure and collect data. In this way, the collection unit can collect data in real time using the wearable device. 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 send data acquired by the wearable device to a generation AI, which can analyze the data and provide feedback.

[0073] The analysis unit can use a machine learning algorithm to analyze the user's data and find patterns. Examples of machine learning algorithms include, but are not limited to, deep learning and support vector machines. For example, the analysis unit can use deep learning to analyze the user's eating habits and exercise habits and identify factors that affect their health status. The analysis unit can also use a support vector machine to assess the user's health risks. For example, the analysis unit can use deep learning to analyze the user's dietary data and identify nutritional imbalances. The analysis unit can also use a support vector machine to analyze the user's exercise data and assess the risk of insufficient or excessive exercise. In this way, using a machine learning algorithm, the analysis unit can find patterns in the user's data and understand their characteristics. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's data into a generation AI, which can analyze the data and find patterns.

[0074] The suggestion unit can suggest a balanced meal menu based on the user's eating habits. A balanced meal menu includes, but is not limited to, nutrient balance and calorie restriction. The suggestion unit can suggest a nutrient-balanced meal menu based on the user's eating habits. The suggestion unit can also suggest a meal menu that includes calorie restriction according to the user's health condition. For example, the suggestion unit can analyze the user's dietary data and suggest a meal menu that compensates for vitamin and mineral deficiencies. The suggestion unit can also suggest low-calorie and high-protein meal menus according to the user's health goals. This allows the suggestion of a balanced meal menu based on the user's eating habits to support a healthy diet. Some or all of the above-described processing by the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's dietary data into the generation AI, which can then suggest a balanced meal menu.

[0075] The suggestion unit can provide an appropriate exercise plan based on the user's exercise habits. An appropriate exercise plan includes, but is not limited to, the type, frequency, and intensity of exercise. The suggestion unit can provide an appropriate exercise plan based on, for example, the user's exercise habits. The suggestion unit can also adjust the intensity and frequency of exercise according to the user's health condition. For example, the suggestion unit can analyze the user's exercise data and provide aerobic exercise and strength training plans. The suggestion unit can also suggest how many times a week the user should exercise according to their health goals. This allows for effective exercise support by providing an appropriate exercise plan based on the user's exercise habits. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's exercise data into the generation AI, which can then suggest an appropriate exercise plan.

[0076] The monitoring unit can collect data in real time using a wearable device and periodically provide feedback through an app. Examples of feedback include, but are not limited to, notification frequency and feedback format. For example, the monitoring unit can collect the user's exercise volume and heart rate in real time using a wearable device and periodically provide feedback through an app. The monitoring unit can also monitor the user's diet and health status and adjust the plan as needed. For example, the monitoring unit can send a notification if the user is not exercising enough to encourage the user to exercise. The monitoring unit can also provide advice if the user's diet is unbalanced. In this way, by collecting data in real time using a wearable device and periodically providing feedback, the user's health status can be continuously monitored. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can send data acquired by the wearable device to a generation AI, which can analyze the data and provide feedback.

[0077] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can collect detailed data to obtain more information. For example, if the user is in a hurry, the collection unit can perform simplified data collection to quickly obtain necessary information. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on 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, for example, 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 collection unit may be performed using AI, or without AI. For example, the collection unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and adjust the timing of data collection.

[0078] The collection unit can analyze the user's past data input history and select the optimal collection method. The optimal collection method can be, for example, based on the type of data and collection efficiency, but is not limited to these examples. For example, the collection unit can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The collection unit can also analyze patterns of data previously input by the user and determine the optimal collection timing. For example, the collection unit selects the type of data to collect during a specific time period from the user's past input history. This allows the optimal collection method to be selected by analyzing the user's past data input history. Some or all of the above-described 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 past data input history into a generation AI, which can select the optimal collection method.

[0079] The collection unit can filter data based on the user's current health condition and lifestyle when collecting data. Examples of filtering include, but are not limited to, the importance and relevance of data. For example, if the user is tired, the collection unit can temporarily suspend data collection and resume it after the user has rested. The collection unit can also set the optimal timing for data collection to match the user's lifestyle. For example, the collection unit can adjust the type of data to be collected based on the user's health condition. This allows more appropriate data to be collected by filtering the data based on the user's current health condition and lifestyle. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's health condition data into a generation AI, which can then filter and collect the data.

[0080] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the collection unit can collect data using voice recognition technology. Alternatively, if the user prefers text input, the collection unit can collect data by providing a simple input form. For example, if the user prefers image input, the collection unit can collect data using image recognition technology. This allows efficient 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 the user's input data into a generation AI, which then selects the optimal collection means.

[0081] 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. Data priority can include, but is not limited to, the importance of the data and the timing of collection. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed data related to the user's health condition. For example, if the user is in a hurry, the collection unit prioritizes collecting the minimum amount of data necessary. This allows for prioritized collection of more important data by determining the priority of data to be collected based on the user's emotions. 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then prioritize the data.

[0082] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, when the user is in a specific area, the collection unit can prioritize collecting health data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the user's eating habits and exercise environment at the travel destination. For example, when the user is at home, the collection unit prioritizes collecting data related to daily life. In this way, by taking the user's geographical location information into account, highly relevant data can be prioritized. 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 into the generation AI, which can then prioritize collecting highly relevant data.

[0083] During data collection, the collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The collection unit can, for example, collect the meal details shared by the user on social media. The collection unit can also collect the user's exercise records on social media. For example, the collection unit can analyze the user's health-related posts on social media and collect related data. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described 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 social media data into a generation AI, which can collect related data.

[0084] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. Feedback includes, but is not limited to, user opinions, ratings, and comments. For example, the collection unit can adjust the frequency of data collection based on the user's past feedback. The collection unit can also select the type of data to collect by referring to the user's past feedback. For example, the collection unit improves the data collection interface based on the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback. 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 user's feedback data into a generation AI, which can customize the collection method.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is stressed, the analysis unit can provide simple, visually easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results to promote deeper understanding. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on 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, for example, 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-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, the impact and relevance of the data. For example, the analysis unit can perform a detailed analysis of highly important data to provide deep insights. The analysis unit can also perform a simplified analysis of less important data to provide quick results. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which can then adjust the level of detail of the analysis.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, health data, behavioral data, and environmental data. For example, the analysis unit can apply a nutritional balance analysis algorithm to dietary data. The analysis unit can also apply an exercise effect analysis algorithm to exercise data. For example, the analysis unit can apply a health risk analysis algorithm to health condition data. By applying different analysis algorithms depending on the data category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI, which can then apply an appropriate analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past trends and patterns. For example, the analysis unit can improve the accuracy of current data analysis based on the user's past analysis results. The analysis unit can also find specific patterns from the user's past analysis results and reflect them in the current analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis results. This can improve the accuracy of the current analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result to promote deeper understanding. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the length of the analysis.

[0090] During analysis, the analysis unit can determine analysis priorities based on the time of data collection. The time of data collection includes, but is not limited to, the frequency and timing of collection. The analysis unit can, for example, prioritize analysis of the most recent data to provide real-time insights. The analysis unit can also improve the accuracy of current data analysis by referring to past data. For example, the analysis unit optimally allocates analysis resources according to the time of data collection. This allows real-time insights to be provided by determining analysis priorities based on the time of data collection. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the time of data collection into the generation AI, which can then determine the analysis priorities.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Data relevance includes, but is not limited to, correlation and causal relationships between data. For example, the analysis unit can prioritize analysis of highly relevant data and provide results quickly. The analysis unit can also postpone analysis of less relevant data and focus on analyzing important data. For example, the analysis unit optimally allocates analysis resources according to the relevance of the data. This allows for focusing on analyzing important data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the order of analysis.

[0092] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Examples of technical terminology include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for the provision of more understandable analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0093] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is stressed, the suggestion unit can provide simple, visually easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions to promote deeper understanding. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. This allows for adjusting the way suggestions are presented based on the user's emotions, thereby providing more appropriate suggestions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the suggestion unit may be performed using, for example, the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then adjust the way suggestions are presented.

[0094] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the plan. The importance of the plan includes, but is not limited to, the impact and urgency of the plan. For example, the proposal unit can provide detailed proposals for highly important plans to provide deep insights. The proposal unit can also provide simplified proposals for less important plans to quickly provide results. For example, the proposal unit optimally allocates proposal resources according to the importance of the plan. By adjusting the level of detail of the proposal based on the importance of the plan, efficient proposals can be made. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the importance of the plan to the generation AI, which can then adjust the level of detail of the proposal.

[0095] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. Examples of plan categories include, but are not limited to, short-term plans, long-term plans, and health plans. For example, the proposal unit can apply a nutritional balance proposal algorithm to a meal plan. The proposal unit can also apply an exercise effect proposal algorithm to an exercise plan. For example, the proposal unit can apply a health risk proposal algorithm to a health management plan. By applying different proposal algorithms depending on the category of the plan, more accurate proposals can be provided. Some or all of the above-described processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input the category of the plan into the generation AI, which can then apply an appropriate proposal algorithm.

[0096] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, for example, past successful and unsuccessful examples, but are not limited to such examples. The suggestion unit can improve the accuracy of the current proposal, for example, based on the user's past proposal results. The suggestion unit can also find specific patterns from the user's past proposal results and reflect them in the current proposal. For example, the suggestion unit can optimize the proposal algorithm by referring to the user's past proposal results. In this way, the accuracy of the current proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past proposal results into the generation AI, which can improve the accuracy of the proposal.

[0097] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Specific methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions to promote deeper understanding. For example, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows for adjusting the length of the suggestion based on the user's emotion to provide more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then adjust the length of the suggestion.

[0098] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the plan. The time of submission of the plan includes, but is not limited to, for example, the frequency and timing of submission. For example, the proposal unit can prioritize plans with high urgency and respond quickly. The proposal unit can also prioritize plans with upcoming submission dates and encourage the execution of the plans. For example, the proposal unit optimally allocates proposal resources according to the submission time. This allows for a quick response by determining the priority of proposals based on the time of submission of the plans. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the time of submission of the plan into the generation AI, which can then determine the priority of the proposals.

[0099] The suggestion unit can adjust the order of proposals based on the relevance of the plans when making a proposal. The relevance of the plans includes, but is not limited to, correlations and causal relationships between the plans. For example, the suggestion unit can prioritize proposing highly relevant plans and quickly provide results. The suggestion unit can also postpone less relevant plans and focus on proposing important plans. For example, the suggestion unit optimally allocates proposal resources according to the relevance of the plans. This allows the suggestion unit to focus on proposing important plans by adjusting the order of proposals based on the relevance of the plans. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the relevance of the plans into the generation AI, which can then adjust the order of proposals.

[0100] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has technical expertise, the suggestion unit can provide the proposal using detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide the proposal in simple language. For example, the suggestion unit adjusts the way the proposal is expressed depending on the user's level of expertise. This allows the proposal to be more easily understood by adjusting the use of technical terminology in the proposal depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology in the proposal.

[0101] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the monitoring unit can reduce the frequency of monitoring and perform monitoring when the user is relaxed. Furthermore, if the user is relaxed, the monitoring unit can perform detailed monitoring to obtain more information. For example, if the user is in a hurry, the monitoring unit can perform simplified monitoring to quickly obtain necessary information. This allows for more appropriate monitoring by adjusting the monitoring method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the monitoring unit may be performed using AI, or may be performed without AI. For example, the monitoring unit can input the user's emotion data into the generation AI, which can then adjust the monitoring method.

[0102] During monitoring, the monitoring unit can analyze the user's past behavioral history to select the optimal monitoring method. The past behavioral history includes, for example, past activity data and behavioral patterns, but is not limited to these examples. The monitoring unit can select the optimal monitoring method, for example, based on behaviors the user frequently performed in the past. The monitoring unit can also perform monitoring during a specific time period based on the user's past behavioral history. For example, the monitoring unit analyzes the user's past behavioral history to improve the accuracy of monitoring. This allows the optimal monitoring method to be selected by analyzing the user's past behavioral history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past behavioral history into a generation AI, which can select the optimal monitoring method.

[0103] During monitoring, the monitoring unit can customize the monitoring means based on the user's current health condition. Examples of the current health condition include, but are not limited to, weight, blood pressure, and blood glucose level. For example, if the user is tired, the monitoring unit can reduce the frequency of monitoring and resume monitoring after the user has rested. The monitoring unit can also adjust the monitoring means according to the user's health condition. For example, the monitoring unit can change the monitoring data collection method based on the user's health condition. This allows for more appropriate monitoring by customizing the monitoring means based on the user's current health condition. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's health condition data into a generation AI, which can then customize the monitoring means.

[0104] During monitoring, the monitoring unit can improve the monitoring method by reflecting user feedback. Feedback includes, for example, user opinions, evaluations, and comments, but is not limited to these examples. The monitoring unit can, for example, adjust the frequency of monitoring based on the feedback provided by the user. The monitoring unit can also improve the monitoring means by referring to the user feedback. For example, the monitoring unit improves the monitoring interface based on the user feedback. In this way, the monitoring method can be improved by reflecting the user feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input user feedback data into the generation AI, which can then improve the monitoring method.

[0105] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring related to stress reduction. Furthermore, if the user is relaxed, the monitoring unit can prioritize detailed health monitoring. For example, if the user is in a hurry, the monitoring unit can prioritize the minimum necessary monitoring. Thus, by determining the monitoring priorities based on the user's emotions, more important monitoring can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI, which can then determine the monitoring priorities.

[0106] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, when the user is in a specific area, the monitoring unit can prioritize monitoring health data related to that area. Furthermore, when the user is traveling, the monitoring unit can prioritize monitoring data related to the user's eating habits and exercise environment at the travel destination. For example, when the user is at home, the monitoring unit prioritizes monitoring data related to daily life. This allows the optimal monitoring method to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into a generation AI, which can select the optimal monitoring method.

[0107] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The monitoring unit can, for example, monitor the meal contents shared by the user on social media. The monitoring unit can also monitor the user's exercise records on social media. For example, the monitoring unit analyzes the user's health-related posts on social media and monitors related data. This allows for efficient monitoring of related data by analyzing the user's social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media data into a generation AI, which then monitors the related data.

[0108] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. Feedback includes, but is not limited to, the user's opinions, ratings, and comments, for example. The monitoring unit can adjust the frequency of monitoring, for example, based on feedback provided by the user in the past. The monitoring unit can also improve the monitoring means by referring to the user's past feedback. For example, the monitoring unit improves the monitoring interface based on the user's feedback. In this way, the monitoring method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's feedback data into the generation AI, which can then customize the monitoring method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle, eating habits, and health status using the camera 42 and microphone 38B of the smart device 14. The collection unit is also implemented by the specific processing unit 290 of the data processing device 12 and collects data entered by the user through the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to understand the user's characteristics. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a personalized diet plan based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14 and collects data from the wearable device in real time and monitors the execution results of the proposed plan. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle, dietary habits, and health status using the camera 42 and microphone 238 of the smart glasses 214. The collection unit is also implemented by the specific processing unit 290 of the data processing device 12 and collects data input by the user through the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using generative AI to understand the user's characteristics. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a personalized diet plan based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collects data from the wearable device in real time and monitors the execution results of the proposed plan. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is implemented by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle, eating habits, and health status using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit is also implemented by the specific processing unit 290 of the data processing device 12 and collects data input by the user through the headset-type terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to understand the user's characteristics. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a personalized diet plan based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and collects data from the wearable device in real time and monitors the execution results of the proposed plan. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data related to the user's lifestyle, eating habits, and health status using the camera 42 and microphone 238 of the robot 414. The collection unit is also realized by the specific processing unit 290 of the data processing device 12 and collects data input by the user through the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using generative AI to understand the user's characteristics. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a personalized diet plan based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and collects data from the wearable device in real time and monitors the execution results of the proposed plan.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The health management system may further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit collects the user's sleep data and analyzes the quality and patterns of sleep. For example, it can monitor the user's sleep time, the ratio of deep sleep to light sleep, the number of awakenings during the night, etc. The sleep analysis unit can also provide advice to improve the quality of sleep based on the user's sleep data. For example, it can suggest ways to relax before bed or an appropriate sleeping environment. This can improve the user's sleep quality and is expected to improve their overall health.

[0111] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, when the user is relaxed, the collection unit can collect detailed data to obtain more information. For example, when the user is in a hurry, the collection unit can collect simplified data to quickly obtain necessary information. In this way, by adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time.

[0112] The analysis unit can predict future health risks based on the user's past health data. For example, it can analyze past dietary habits, exercise habits, and health status data to identify potential future health risks. The analysis unit can also suggest preventive measures based on the predicted health risks. For example, it can suggest increasing intake of certain nutrients or increasing specific exercise. This allows the user to understand future health risks in advance and take appropriate measures.

[0113] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the suggestion unit can provide simple and visually easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions to encourage deeper understanding. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. This allows more appropriate suggestions to be provided by adjusting the way suggestions are expressed based on the user's emotions.

[0114] The monitoring unit can select an optimal monitoring method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, when the user is in a specific area, the monitoring unit can prioritize monitoring health data related to that area. Furthermore, when the user is traveling, the monitoring unit can prioritize monitoring data related to the user's eating habits and exercise environment at the travel destination. For example, when the user is at home, the monitoring unit prioritizes monitoring data related to daily life. This allows the optimal monitoring method to be selected by taking into account the user's geographical location information.

[0115] The health management system may further include a social analysis unit that analyzes a user's social media activity. The social analysis unit analyzes the user's social media posts, the number of likes, comments, and so on to gain insights into the user's health condition and lifestyle. For example, the social analysis unit can collect the diet and exercise records shared by the user on social media and use them to help manage their health. The social analysis unit can also provide health advice based on the user's social media activity. For example, if a user posts about a healthy diet, the social analysis unit can send a message praising the user's behavior and encouraging them to continue. This allows for more personalized health management by analyzing the user's social media activity.

[0116] 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. Data priority includes, but is not limited to, for example, the importance of data and the timing of collection. For example, when the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data related to the user's health condition. For example, when the user is in a hurry, the collection unit prioritizes collecting the minimum amount of data necessary. In this way, by determining the priority of data to be collected based on the user's emotions, more important data can be collected preferentially.

[0117] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and experience. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0118] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, but are not limited to, past successes and failures. The suggestion unit can improve the accuracy of current suggestions, for example, based on the user's past suggestion results. The suggestion unit can also find specific patterns from the user's past suggestion results and reflect them in current suggestions. For example, the suggestion unit can optimize the suggestion algorithm by referring to the user's past suggestion results. In this way, the accuracy of current suggestions can be improved by referring to the user's past suggestion results.

[0119] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the monitoring unit can reduce the frequency of monitoring and perform monitoring when the user is relaxed. Furthermore, if the user is relaxed, the monitoring unit can perform detailed monitoring to obtain more information. For example, if the user is in a hurry, the monitoring unit can perform simplified monitoring to quickly obtain necessary information. This allows more appropriate monitoring to be performed by adjusting the monitoring method based on the user's emotions.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects data about the user's lifestyle, eating habits, and health status. The user's lifestyle includes daily activities, type of work, hobbies, etc. The collection unit includes a method in which the user inputs data using a smartphone app and a method in which data is collected in real time using a wearable device. For example, the user inputs the details of their meals into the app, and the wearable device measures their exercise volume and heart rate. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and understand the user's characteristics. The analysis is performed using machine learning algorithms. For example, the generation AI analyzes the user's eating habits and exercise habits to identify factors that affect their health. Step 3: The suggestion unit uses the generation AI to propose a personalized diet plan based on the user's characteristics understood by the analysis unit. The proposed plan includes meal content, exercise type, and frequency. For example, the suggestion unit suggests a balanced meal menu based on the user's eating habits and provides an appropriate exercise plan based on the user's exercise habits. Step 4: The monitoring unit monitors the results of the implementation of the plan proposed by the proposal unit. Monitoring is performed by collecting data in real time using a wearable device and providing feedback periodically through an app. For example, the monitoring unit may monitor the user's exercise volume and dietary habits and adjust the plan as necessary. Some or all of the processing in the monitoring unit may be performed using AI.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 data about a user's lifestyle, eating habits, and health status; an analysis unit that analyzes the data collected by the collection unit and understands user characteristics; a suggestion unit that proposes a personalized diet plan based on the user's characteristics understood by the analysis unit; a monitoring unit that monitors the execution results of the plan proposed by the proposal unit. A system characterized by:

2. The collecting unit The user enters data using a smartphone app The system of claim 1 .

3. The collecting unit Use wearable devices to collect data in real time The system of claim 1 .

4. The analysis unit Uses machine learning algorithms to analyze user data and find patterns The system of claim 1 .

5. The proposal unit Suggesting balanced meal menus based on the user's eating habits The system of claim 1 .

6. The proposal unit Providing appropriate exercise plans based on the user's exercise habits The system of claim 1 .

7. The monitoring unit Use wearable devices to collect data in real time and provide feedback through an app on a regular basis The system of claim 1 .

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

9. The collecting unit Analyze the user's past data entry history and select the optimal collection method The system of claim 1 .

10. The collecting unit When collecting data, filtering is performed based on the user's current health status and lifestyle. The system of claim 1 .

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