System

Abstract

An object of a system according to an embodiment is to propose optimal fitness and health habits based on genetic information and behavior information of an individual.SOLUTION: A system includes an acquisition unit, an analysis unit, a proposal unit, a collection unit, and a provision unit. The acquisition unit acquires genetic information. The analysis unit analyzes the genetic information acquired by the acquisition unit. The suggestion unit suggests training and nutritional guidance based on the information analyzed by the analysis unit. The collection unit collects behavior information obtained from smartphone data. The providing unit provides advice on health habits on the basis of the behavior information collected by the collecting 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 technologies do not adequately suggest optimal fitness and health habits based on an individual's genetic and behavioral information, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal fitness and health habits based on an individual's genetic information and behavioral information. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a suggestion unit, a collection unit, and a provision unit. The acquisition unit acquires genetic information. The analysis unit analyzes the genetic information acquired by the acquisition unit. The suggestion unit suggests training and nutritional guidance based on the information analyzed by the analysis unit. The collection unit collects behavioral information obtained from smartphone data. The provision unit provides advice on health habits based on the behavioral information collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal fitness and health habits based on an individual's genetic and behavioral information. [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 fitness service system according to an embodiment of the present invention provides optimal fitness services based on an individual's genetic information and behavioral information. The fitness service system acquires genetic information from a user using a familiar gene collection kit, and an analysis AI proposes optimal training and nutritional advice based on the genetic analysis results. It also provides health habit advice based on behavioral information obtained from smartphone data. For example, the fitness service system allows a user to collect samples such as saliva or blood and analyze the genetic information. This information is input into the analysis AI. The analysis AI then analyzes the input genetic information, identifies individual physical characteristics, and proposes optimal training and nutritional advice. For example, the genetic information can be used to analyze muscle development potential and metabolic characteristics, and a training plan can be created based on the analysis. Furthermore, the fitness service system provides health habit advice based on behavioral information obtained from smartphone data. For example, the smartphone's pedometer and location information can be used to understand daily exercise volume and activity patterns, and based on this, it can propose improvements to health habits. This allows the fitness service system to understand the physical characteristics, appropriate exercise methods, and nutritional balance derived from each individual's genetic information, thereby achieving personalized optimization. This allows the fitness service system to offer an innovative approach to scientifically optimizing an individual's health and fitness level. For example, users can live a healthy lifestyle by receiving optimal training and nutritional guidance based on their genetic information. Furthermore, by utilizing smartphone data, users can receive advice on health habits based on their daily behavior. This will enable personalized fitness services tailored to individual needs.

[0029] A fitness service system according to an embodiment includes an acquisition unit, an analysis unit, a suggestion unit, a collection unit, and a provision unit. The acquisition unit acquires genetic information using a gene collection kit familiar to the user. For example, the acquisition unit collects samples such as saliva or blood and analyzes the genetic information. The analysis unit analyzes the genetic information acquired by the acquisition unit using analytical AI. For example, the analysis unit identifies individual physical characteristics based on the genetic information and suggests optimal training and nutritional advice. The suggestion unit suggests training and nutritional advice based on the information analyzed by the analysis unit. For example, the suggestion unit analyzes muscle development potential and metabolic characteristics from the genetic information and creates a training plan based on the analysis. The collection unit collects behavioral information obtained from smartphone data. For example, the collection unit uses the smartphone's pedometer and location information to understand daily exercise volume and activity patterns. The provision unit provides health habit advice based on the behavioral information collected by the collection unit. For example, the provision unit suggests improvements to health habits based on the collected behavioral information. As a result, the fitness service system according to the embodiment can provide optimal fitness services based on an individual's genetic information and behavioral information.

[0030] The acquisition unit can acquire genetic information using a gene collection kit. Examples of gene collection kits include, but are not limited to, saliva collection kits and blood collection kits. The acquisition unit can, for example, collect a saliva sample using a saliva collection kit and acquire genetic information. The acquisition unit can also collect a blood sample using a blood collection kit and acquire genetic information. For example, the acquisition unit provides a gene collection kit that a user can easily use at home and acquires genetic information. This makes it possible to easily acquire genetic information by using the gene collection kit. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input sample data acquired by the gene collection kit into a generation AI and have the generation AI analyze the genetic information.

[0031] The analysis unit can grasp individual physical characteristics based on genetic information. The analysis unit, for example, grasps muscle development characteristics based on genetic information. For example, the analysis unit analyzes muscle fiber type and muscle growth rate. The analysis unit can also analyze basal metabolic rate and energy consumption to grasp metabolic characteristics. For example, the analysis unit analyzes metabolic characteristics from genetic information to grasp individual physical characteristics. The analysis unit can also analyze the absorption efficiency of specific nutrients based on genetic information. For example, the analysis unit analyzes the absorption efficiency of specific nutrients from genetic information and provides nutritional guidance based on the analysis. This enables individual optimization by grasping individual physical characteristics based on genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input genetic information into a generation AI and cause the generation AI to analyze individual physical characteristics.

[0032] The suggestion unit can analyze muscle development characteristics and metabolic characteristics from the genetic information and create a training plan based on the analysis. For example, the suggestion unit can analyze muscle development characteristics from the genetic information and create a strength training plan based on the analysis. For example, the suggestion unit can analyze muscle fiber type and muscle growth rate and propose an optimal training plan based on the analysis. The suggestion unit can also analyze metabolic characteristics from the genetic information and provide nutritional guidance based on the analysis. For example, the suggestion unit can analyze basal metabolic rate and energy consumption and propose a meal plan based on the analysis. The suggestion unit can also analyze the absorption efficiency of specific nutrients from the genetic information and provide nutritional guidance based on the analysis. For example, if the absorption efficiency of a specific nutrient is low, the suggestion unit can propose a meal plan to supplement the nutrient. This allows for an optimal training plan to be provided based on the genetic information. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input genetic information into a generation AI and cause the generation AI to create a training plan.

[0033] The collection unit can collect daily exercise volume and activity patterns using a smartphone's pedometer or location information. The collection unit, for example, collects step count data using a built-in sensor in the smartphone. For example, the collection unit can use a smartphone pedometer app to understand daily exercise volume. The collection unit can also collect activity patterns using the smartphone's location information. For example, the collection unit can use GPS data to understand the user's movement pattern. The collection unit can also use a smartphone location tracking app to collect the user's activity pattern. For example, the collection unit can understand the user's activity area and movement distance based on the location information. This makes it possible to understand daily exercise volume and activity patterns using smartphone data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the smartphone's step count data and location information to the generation AI and have the generation AI analyze the exercise volume and activity patterns.

[0034] The providing unit can suggest improvements to health habits based on the collected behavioral information. The providing unit can suggest improvements to exercise habits based on, for example, collected step count data. For example, if the amount of daily exercise is insufficient, the providing unit can provide specific advice to increase the amount of exercise. The providing unit can also suggest improvements to activity patterns based on collected location information. For example, if the travel distance is short, the providing unit can provide advice to encourage more walking. The providing unit can also suggest improvements to health habits based on collected heart rate data. For example, if the heart rate is high, the providing unit can provide specific advice to relax. This makes it possible to suggest improvements to health habits based on behavioral information. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the collected behavioral information to a generation AI and cause the generation AI to suggest improvements to health habits.

[0035] The acquisition unit can analyze the user's past health data and select the optimal genetic information acquisition method. For example, the acquisition unit selects a method using a blood sample based on the user's past blood test results. For example, the acquisition unit analyzes the quality of the user's past saliva sample and selects a method using the saliva sample. The acquisition unit can also select the most reliable genetic information acquisition method from the user's past health data. For example, the acquisition unit analyzes the user's past health data and selects the optimal genetic information acquisition method. This allows the optimal genetic information acquisition method to be selected based on the user's past health data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past health data into the generation AI and cause the generation AI to select the optimal genetic information acquisition method.

[0036] When acquiring the genetic information, the acquisition unit can perform filtering based on the user's lifestyle and eating patterns. For example, if the user has a specific eating pattern, the acquisition unit filters the genetic information based on that pattern. For example, the acquisition unit filters the genetic information based on the user's lifestyle (e.g., whether the user is a night owl or a morning person). The acquisition unit can also analyze the user's dietary history and filter the genetic information taking into account the influence of specific nutrients. For example, the acquisition unit filters the genetic information based on the user's dietary history and taking into account the influence of specific nutrients. This makes it possible to filter the genetic information based on the user's lifestyle and eating patterns. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's lifestyle and eating patterns into the generation AI and cause the generation AI to filter the genetic information.

[0037] When acquiring genetic information, the acquisition unit can select an acquisition means according to the user's input method. For example, when the user provides a saliva sample, the acquisition unit acquires the genetic information using a saliva collection kit. For example, when the user provides a blood sample, the acquisition unit acquires the genetic information using a blood collection kit. Furthermore, when the user provides a hair sample, the acquisition unit can also acquire the genetic information using a hair collection kit. For example, when the user provides a hair sample, the acquisition unit acquires the genetic information using a hair collection kit. This makes it possible to select the optimal genetic information acquisition means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input sample data according to the user's input method into the generation AI and cause the generation AI to acquire the genetic information.

[0038] When acquiring genetic information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the acquisition unit prioritizes acquiring genetic information related to that area. For example, if the user is traveling, the acquisition unit prioritizes acquiring genetic information related to the environment of the travel destination. The acquisition unit can also prioritize acquiring genetic information related to region-specific health risks based on the user's geographical location information. For example, the acquisition unit prioritizes acquiring genetic information related to region-specific health risks based on the user's geographical location information. This allows highly relevant genetic information to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant genetic information.

[0039] When acquiring genetic information, the acquisition unit can analyze the user's social media activities and acquire related information. The acquisition unit acquires related genetic information based on, for example, health information shared by the user on social media. For example, the acquisition unit analyzes the user's social media activity patterns and acquires related genetic information. The acquisition unit can also acquire related genetic information by referring to health information of the user's friends on social media. For example, the acquisition unit acquires related genetic information by referring to health information of the user's friends on social media. This makes it possible to acquire related genetic information based on the user's social media activities. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related genetic information.

[0040] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring genetic information. The acquisition unit customizes the genetic information acquisition method based on, for example, feedback provided by the user in the past. For example, the acquisition unit selects the acquisition method that provides the highest satisfaction from the user's past feedback. The acquisition unit can also analyze the user's past feedback and reflect improvements to the acquisition method. For example, the acquisition unit analyzes the user's past feedback and reflects improvements to the acquisition method. This allows the genetic information acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information. For example, the analysis unit performs a detailed analysis of important genetic information. For example, the analysis unit performs a detailed analysis of genetic information of high importance based on the function of the gene or an associated disease. The analysis unit can also perform a brief analysis of genetic information of low importance. For example, the analysis unit performs a brief analysis of genetic information of low importance. The analysis unit can also set an analysis priority according to the importance of the genetic information. For example, the analysis unit prioritizes the analysis of genetic information of high importance. This allows the level of detail of the analysis to be adjusted based on the importance of the genetic information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input importance data of the genetic information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of genetic information. For example, the analysis unit applies a specific analysis algorithm to genetic information related to muscle development. For example, the analysis unit applies a specific analysis algorithm to analyze genetic information related to muscle development. The analysis unit can also apply a different analysis algorithm to genetic information related to metabolism. For example, the analysis unit applies a different analysis algorithm to analyze genetic information related to metabolism. The analysis unit can also apply yet another analysis algorithm to genetic information related to health risks. For example, the analysis unit applies yet another analysis algorithm to analyze genetic information related to health risks. This allows the application of an optimal analysis algorithm depending on the category of genetic information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the genetic information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. For example, the analysis unit extracts specific patterns from the user's past analysis results and reflects them in the analysis. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis algorithm. For example, the analysis unit analyzes the user's past analysis results and identifies areas for improvement in the analysis algorithm. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the acquisition time of the genetic information. For example, the analysis unit prioritizes analysis of recently acquired genetic information. For example, the analysis unit sets a low analysis priority for genetic information acquired recently. The analysis unit can also adjust the analysis schedule according to the acquisition time of the genetic information. For example, the analysis unit adjusts the analysis schedule according to the acquisition time of the genetic information. This makes it possible to determine the analysis priority based on the acquisition time of the genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the acquisition time of the genetic information to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the genetic information. For example, the analysis unit prioritizes analysis of highly relevant genetic information. For example, the analysis unit postpones the order of analysis of less relevant genetic information. The analysis unit can also adjust the analysis schedule according to the relevance of the genetic information. For example, the analysis unit adjusts the analysis schedule according to the relevance of the genetic information. This makes it possible to adjust the order of analysis based on the relevance of the genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the genetic information to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can use concise and easy-to-understand terminology. For example, if the user does not have technical expertise, the analysis unit uses concise and easy-to-understand terminology. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the analysis.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the training or nutritional guidance. For example, the suggestion unit makes detailed suggestions for important training or nutritional guidance. For example, the suggestion unit makes detailed suggestions for training or nutritional guidance that is highly important based on the health status or goal achievement level. The suggestion unit can also make brief suggestions for training or nutritional guidance that is less important. For example, the suggestion unit makes brief suggestions for training or nutritional guidance that is less important. The suggestion unit can also set priorities for the proposals based on the importance of the training or nutritional guidance. For example, the suggestion unit prioritizes suggestions for training or nutritional guidance that is highly important. This allows the level of detail of the proposal to be adjusted based on the importance of the training or nutritional guidance. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input importance data for training or nutritional guidance to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of training or nutritional guidance. For example, the suggestion unit applies a specific suggestion algorithm to suggestions related to strength training. For example, the suggestion unit applies a specific suggestion algorithm to make suggestions related to strength training. The suggestion unit can also apply a different suggestion algorithm to suggestions related to aerobic exercise. For example, the suggestion unit applies a different suggestion algorithm to make suggestions related to aerobic exercise. The suggestion unit can also apply yet another suggestion algorithm to suggestions related to nutritional guidance. For example, the suggestion unit applies yet another suggestion algorithm to make suggestions related to nutritional guidance. This makes it possible to apply an optimal suggestion algorithm depending on the category of training or nutritional guidance. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input category data of training or nutritional guidance to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the accuracy of the current proposal based on the user's past proposal results. For example, the suggestion unit extracts a specific pattern from the user's past proposal results and reflects it in the proposal. The suggestion unit can also analyze the user's past proposal results and identify areas for improvement in the proposal algorithm. For example, the suggestion unit analyzes the user's past proposal results and identifies areas for improvement in the proposal algorithm. This allows the accuracy of the proposal to be improved based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the training or nutritional guidance. For example, the suggestion unit prioritizes the most recently submitted training or nutritional guidance. For example, the suggestion unit sets a low priority for a proposal of a training or nutritional guidance that was submitted earlier. The suggestion unit can also adjust the schedule of the proposal based on the time of submission of the training or nutritional guidance. For example, the suggestion unit adjusts the schedule of the proposal based on the time of submission of the training or nutritional guidance. This makes it possible to determine the priority of the proposal based on the time of submission of the training or nutritional guidance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the time of submission of the training or nutritional guidance to the generation AI and cause the generation AI to determine the priority of the proposal.

[0051] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the training and nutritional guidance. For example, the proposal unit prioritizes the proposal of highly relevant training and nutritional guidance. For example, the proposal unit postpones the order of proposals for less relevant training and nutritional guidance. The proposal unit can also adjust the schedule of proposals based on the relevance of the training and nutritional guidance. For example, the proposal unit adjusts the schedule of proposals based on the relevance of the training and nutritional guidance. This makes it possible to adjust the order of proposals based on the relevance of the training and nutritional guidance. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input relevance data of training and nutritional guidance to a generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses detailed technical terminology. For example, if the user has technical expertise, the suggestion unit uses detailed technical terminology. The suggestion unit can also use concise and easy-to-understand terminology if the user does not have technical expertise. For example, if the user does not have technical expertise, the suggestion unit uses concise and easy-to-understand terminology. The suggestion unit can also adjust the way the suggestion result is expressed according to the user's level of expertise. For example, the suggestion unit adjusts the way the suggestion result is expressed according to the user's level of expertise. This allows the use of technical terminology in the suggestion to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the suggestion.

[0053] The collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the collection unit selects a method for using a pedometer based on the user's past step count data. For example, the collection unit analyzes the user's past location information data and selects a method for using location information. The collection unit can also select the most reliable collection method from the user's past behavioral data. For example, the collection unit analyzes the user's past behavioral data and selects the optimal collection method. This allows the optimal collection method to be selected based on the user's past behavioral data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data to a generation AI and cause the generation AI to select the optimal collection method.

[0054] When collecting behavioral information, the collection unit can filter the behavioral information based on the user's lifestyle habits and activity patterns. For example, if the user has a specific activity pattern, the collection unit filters the behavioral information based on that pattern. For example, the collection unit filters the behavioral information based on the user's lifestyle habits (e.g., whether the user is a night owl or a morning person). The collection unit can also analyze the user's activity history and filter the behavioral information by taking into account the influence of specific activities. For example, the collection unit filters the behavioral information based on the user's activity history by taking into account the influence of specific activities. This makes it possible to filter the behavioral information based on the user's lifestyle habits and activity patterns. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's lifestyle habits and activity patterns to the generation AI and cause the generation AI to filter the behavioral information.

[0055] When collecting behavioral information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses a pedometer, the collection unit collects behavioral information using the pedometer. For example, if the user provides location information, the collection unit collects behavioral information using the location information. Furthermore, if the user provides heart rate data, the collection unit can also collect behavioral information using a heart rate meter. For example, if the user provides heart rate data, the collection unit collects behavioral information using the heart rate meter. This allows the optimal behavioral information collection means to be selected depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data depending on the user's input method to the generation AI and cause the generation AI to collect behavioral information.

[0056] When collecting behavioral information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting behavioral information related to that area. For example, if the user is traveling, the collection unit prioritizes collecting behavioral information related to the environment of the travel destination. The collection unit can also prioritize collecting behavioral information related to region-specific health risks based on the user's geographical location information. For example, the collection unit prioritizes collecting behavioral information related to region-specific health risks based on the user's geographical location information. This allows highly relevant behavioral information to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant behavioral information.

[0057] When collecting behavioral information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects related behavioral information based on health information shared by the user on social media. For example, the collection unit analyzes the user's social media activity patterns and collects related behavioral information. The collection unit can also collect related behavioral information by referring to health information of the user's friends on social media. For example, the collection unit collects related behavioral information by referring to health information of the user's friends on social media. This makes it possible to collect related behavioral information based on the user's social media activities. 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 may input the user's social media activity data into a generation AI and cause the generation AI to collect related behavioral information.

[0058] When collecting behavioral information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the behavioral information collection method, for example, based on feedback provided by the user in the past. For example, the collection unit selects the collection method that provides the highest satisfaction from the user's past feedback. The collection unit can also analyze the user's past feedback and reflect improvements to the collection method. For example, the collection unit analyzes the user's past feedback and reflects improvements to the collection method. This allows the behavioral information collection method to be customized based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0059] When providing health habit advice, the providing unit can adjust the level of detail of the advice based on the importance of the behavioral information. For example, the providing unit provides detailed advice for important behavioral information. For example, the providing unit provides detailed advice for behavioral information with high importance based on the health state and the degree of goal achievement. The providing unit can also provide concise advice for behavioral information with low importance. For example, the providing unit provides concise advice for behavioral information with low importance. The providing unit can also set a priority of advice according to the importance of the behavioral information. For example, the providing unit gives priority to advice for behavioral information with high importance. This makes it possible to adjust the level of detail of the advice based on the importance of the behavioral information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input importance data of the behavioral information to a generating AI and cause the generating AI to adjust the level of detail of the advice.

[0060] When providing health habit advice, the providing unit can apply different advice algorithms depending on the category of behavioral information. For example, the providing unit applies a specific advice algorithm to advice related to exercise habits. For example, the providing unit applies a specific advice algorithm to provide advice related to exercise habits. The providing unit can also apply a different advice algorithm to advice related to eating habits. For example, the providing unit applies a different advice algorithm to provide advice related to eating habits. The providing unit can also apply yet another advice algorithm to advice related to sleeping habits. For example, the providing unit applies yet another advice algorithm to provide advice related to sleeping habits. This makes it possible to apply an optimal advice algorithm depending on the category of behavioral information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the behavioral information to the generation AI and cause the generation AI to apply the advice algorithm.

[0061] When providing health habit advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, improves the accuracy of the current advice based on the user's past advice results. For example, the providing unit extracts a specific pattern from the user's past advice results and reflects it in the advice. The providing unit can also analyze the user's past advice results and identify areas for improvement in the advice algorithm. For example, the providing unit analyzes the user's past advice results and identifies areas for improvement in the advice algorithm. This allows the accuracy of the advice to be improved based on the user's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0062] When providing health habit advice, the providing unit can determine the priority of the advice based on the acquisition time of the behavioral information. For example, the providing unit preferentially reflects recently acquired behavioral information in the advice. For example, the providing unit sets a low priority for advice regarding behavioral information that was acquired recently. The providing unit can also adjust the advice schedule according to the acquisition time of the behavioral information. For example, the providing unit adjusts the advice schedule according to the acquisition time of the behavioral information. This makes it possible to determine the priority of advice based on the acquisition time of the behavioral information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the acquisition time of the behavioral information to the generating AI and cause the generating AI to determine the priority of advice.

[0063] When providing health habit advice, the providing unit can adjust the order of advice based on the relevance of behavioral information. For example, the providing unit preferentially reflects highly relevant behavioral information in the advice. For example, the providing unit postpones the order of advice for less relevant behavioral information. The providing unit can also adjust the schedule of advice based on the relevance of behavioral information. For example, the providing unit adjusts the schedule of advice based on the relevance of behavioral information. This makes it possible to adjust the order of advice based on the relevance of behavioral information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of behavioral information to a generating AI and cause the generating AI to adjust the order of advice.

[0064] When providing health habit advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, the providing unit uses detailed technical terms. For example, if the user has technical expertise, the providing unit uses detailed technical terms. Furthermore, if the user does not have technical expertise, the providing unit can use concise and easy-to-understand terms. For example, if the user does not have technical expertise, the providing unit uses concise and easy-to-understand terms. Furthermore, the providing unit can adjust the way in which the advice result is expressed according to the user's level of expertise. For example, the providing unit adjusts the way in which the advice result is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terms in the advice.

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

[0066] The fitness service system can further acquire the user's sleep data and analyze it with an analysis unit. For example, the acquisition unit can use sensors on the user's smartwatch or smartphone to record the quality and duration of sleep. The analysis unit can analyze the user's sleep patterns based on this data and provide advice for improving sleep quality. The suggestion unit can propose a sleep improvement plan suitable for the user based on the analysis results. For example, specific advice can be provided, such as going to bed at a specific time or avoiding certain foods. This allows the fitness service system to more comprehensively support the user's overall health condition.

[0067] The fitness service system can further acquire the user's dietary data and analyze it with the analysis unit. For example, the acquisition unit collects dietary data using an app that allows the user to record their dietary habits. The analysis unit can analyze the user's nutritional balance based on this data and provide advice for improving their diet. The suggestion unit can propose a meal plan suitable for the user based on the analysis results. For example, it can suggest ingredients and recipes for increasing specific nutrients. This allows the fitness service system to provide support for making the user's diet healthier.

[0068] The fitness service system can further acquire the user's social activity data and analyze it with the analysis unit. For example, the acquisition unit collects the user's social media activity and event participation history. The analysis unit can analyze the user's social activity pattern based on this data and provide advice for balancing social activities. The suggestion unit can propose a social activity plan suitable for the user based on the analysis results. For example, it can recommend participating in a specific event or taking up a new hobby. In this way, the fitness service system can also support the user's social health.

[0069] The fitness service system can further acquire the user's exercise history data and analyze it with the analysis unit. For example, the acquisition unit collects data on the user's past training. Based on this data, the analysis unit analyzes the user's exercise history and understands the user's training progress. Based on the analysis results, the suggestion unit proposes a training plan suitable for the user. For example, based on past training data, it can identify areas in need of improvement for the user and propose training accordingly. This allows the fitness service system to provide a personalized training plan based on the user's exercise history.

[0070] The fitness service system can further acquire the user's geographical location information and analyze it with the analysis unit. For example, the acquisition unit can use GPS data from the user's smartphone to collect the user's location information. Based on this data, the analysis unit analyzes the user's activity area and movement patterns to identify health risks specific to the area. The suggestion unit provides the user with advice on health habits appropriate for the user based on the analysis results. For example, it can suggest exercise and dietary precautions for a specific area. This allows the fitness service system to provide personalized health advice based on the user's geographical location information.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The acquisition unit acquires genetic information using a gene collection kit familiar to the user. For example, it collects a sample of saliva or blood and analyzes the genetic information. Step 2: The analysis unit uses analytical AI to analyze the genetic information acquired by the acquisition unit. For example, it identifies individual physical characteristics based on the genetic information and proposes optimal training and nutritional guidance. Step 3: The suggestion unit proposes training and nutritional advice based on the information analyzed by the analysis unit. For example, it analyzes the likelihood of muscle development and metabolic characteristics from genetic information and creates a training plan based on that. Step 4: The collection unit collects behavioral information obtained from smartphone data. For example, it uses the smartphone's pedometer and location information to understand daily exercise volume and activity patterns. Step 5: The providing unit provides advice on health habits based on the behavioral information collected by the collecting unit. For example, the providing unit suggests improvements to health habits based on the collected behavioral information.

[0073] (Example 2) A fitness service system according to an embodiment of the present invention provides optimal fitness services based on an individual's genetic information and behavioral information. The fitness service system acquires genetic information from a user using a familiar gene collection kit, and an analysis AI proposes optimal training and nutritional advice based on the genetic analysis results. It also provides health habit advice based on behavioral information obtained from smartphone data. For example, the fitness service system allows a user to collect samples such as saliva or blood and analyze the genetic information. This information is input into the analysis AI. The analysis AI then analyzes the input genetic information, identifies individual physical characteristics, and proposes optimal training and nutritional advice. For example, the genetic information can be used to analyze muscle development potential and metabolic characteristics, and a training plan can be created based on the analysis. Furthermore, the fitness service system provides health habit advice based on behavioral information obtained from smartphone data. For example, the smartphone's pedometer and location information can be used to understand daily exercise volume and activity patterns, and based on this, it can propose improvements to health habits. This allows the fitness service system to understand the physical characteristics, appropriate exercise methods, and nutritional balance derived from each individual's genetic information, thereby achieving personalized optimization. This allows the fitness service system to offer an innovative approach to scientifically optimizing an individual's health and fitness level. For example, users can live a healthy lifestyle by receiving optimal training and nutritional guidance based on their genetic information. Furthermore, by utilizing smartphone data, users can receive advice on health habits based on their daily behavior. This will enable personalized fitness services tailored to individual needs.

[0074] A fitness service system according to an embodiment includes an acquisition unit, an analysis unit, a suggestion unit, a collection unit, and a provision unit. The acquisition unit acquires genetic information using a gene collection kit familiar to the user. For example, the acquisition unit collects samples such as saliva or blood and analyzes the genetic information. The analysis unit analyzes the genetic information acquired by the acquisition unit using analytical AI. For example, the analysis unit identifies individual physical characteristics based on the genetic information and suggests optimal training and nutritional advice. The suggestion unit suggests training and nutritional advice based on the information analyzed by the analysis unit. For example, the suggestion unit analyzes muscle development potential and metabolic characteristics from the genetic information and creates a training plan based on the analysis. The collection unit collects behavioral information obtained from smartphone data. For example, the collection unit uses the smartphone's pedometer and location information to understand daily exercise volume and activity patterns. The provision unit provides health habit advice based on the behavioral information collected by the collection unit. For example, the provision unit suggests improvements to health habits based on the collected behavioral information. As a result, the fitness service system according to the embodiment can provide optimal fitness services based on an individual's genetic information and behavioral information.

[0075] The acquisition unit can acquire genetic information using a gene collection kit. Examples of gene collection kits include, but are not limited to, saliva collection kits and blood collection kits. The acquisition unit can, for example, collect a saliva sample using a saliva collection kit and acquire genetic information. The acquisition unit can also collect a blood sample using a blood collection kit and acquire genetic information. For example, the acquisition unit provides a gene collection kit that a user can easily use at home and acquires genetic information. This makes it possible to easily acquire genetic information by using the gene collection kit. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input sample data acquired by the gene collection kit into a generation AI and have the generation AI analyze the genetic information.

[0076] The analysis unit can grasp individual physical characteristics based on genetic information. The analysis unit, for example, grasps muscle development characteristics based on genetic information. For example, the analysis unit analyzes muscle fiber type and muscle growth rate. The analysis unit can also analyze basal metabolic rate and energy consumption to grasp metabolic characteristics. For example, the analysis unit analyzes metabolic characteristics from genetic information to grasp individual physical characteristics. The analysis unit can also analyze the absorption efficiency of specific nutrients based on genetic information. For example, the analysis unit analyzes the absorption efficiency of specific nutrients from genetic information and provides nutritional guidance based on the analysis. This enables individual optimization by grasping individual physical characteristics based on genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input genetic information into a generation AI and cause the generation AI to analyze individual physical characteristics.

[0077] The suggestion unit can analyze muscle development characteristics and metabolic characteristics from the genetic information and create a training plan based on the analysis. For example, the suggestion unit can analyze muscle development characteristics from the genetic information and create a strength training plan based on the analysis. For example, the suggestion unit can analyze muscle fiber type and muscle growth rate and propose an optimal training plan based on the analysis. The suggestion unit can also analyze metabolic characteristics from the genetic information and provide nutritional guidance based on the analysis. For example, the suggestion unit can analyze basal metabolic rate and energy consumption and propose a meal plan based on the analysis. The suggestion unit can also analyze the absorption efficiency of specific nutrients from the genetic information and provide nutritional guidance based on the analysis. For example, if the absorption efficiency of a specific nutrient is low, the suggestion unit can propose a meal plan to supplement the nutrient. This allows for an optimal training plan to be provided based on the genetic information. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input genetic information into a generation AI and cause the generation AI to create a training plan.

[0078] The collection unit can collect daily exercise volume and activity patterns using a smartphone's pedometer or location information. The collection unit, for example, collects step count data using a built-in sensor in the smartphone. For example, the collection unit can use a smartphone pedometer app to understand daily exercise volume. The collection unit can also collect activity patterns using the smartphone's location information. For example, the collection unit can use GPS data to understand the user's movement pattern. The collection unit can also use a smartphone location tracking app to collect the user's activity pattern. For example, the collection unit can understand the user's activity area and movement distance based on the location information. This makes it possible to understand daily exercise volume and activity patterns using smartphone data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the smartphone's step count data and location information to the generation AI and have the generation AI analyze the exercise volume and activity patterns.

[0079] The providing unit can suggest improvements to health habits based on the collected behavioral information. The providing unit can suggest improvements to exercise habits based on, for example, collected step count data. For example, if the amount of daily exercise is insufficient, the providing unit can provide specific advice to increase the amount of exercise. The providing unit can also suggest improvements to activity patterns based on collected location information. For example, if the travel distance is short, the providing unit can provide advice to encourage more walking. The providing unit can also suggest improvements to health habits based on collected heart rate data. For example, if the heart rate is high, the providing unit can provide specific advice to relax. This makes it possible to suggest improvements to health habits based on behavioral information. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the collected behavioral information to a generation AI and cause the generation AI to suggest improvements to health habits.

[0080] The acquisition unit can estimate the user's emotions and adjust the timing of genetic information acquisition based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit adjusts the acquisition of genetic information to occur immediately. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is feeling stressed, the acquisition of genetic information can be postponed until the user is able to relax. For example, the acquisition unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, the acquisition unit can provide a simplified procedure for quickly acquiring genetic information when the user is in a hurry. For example, the acquisition unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. This allows the timing of genetic information acquisition to be adjusted according to the user's emotions. Emotion estimation is realized 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 acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0081] The acquisition unit can analyze the user's past health data and select the optimal genetic information acquisition method. For example, the acquisition unit selects a method using a blood sample based on the user's past blood test results. For example, the acquisition unit analyzes the quality of the user's past saliva sample and selects a method using the saliva sample. The acquisition unit can also select the most reliable genetic information acquisition method from the user's past health data. For example, the acquisition unit analyzes the user's past health data and selects the optimal genetic information acquisition method. This allows the optimal genetic information acquisition method to be selected based on the user's past health data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past health data into the generation AI and cause the generation AI to select the optimal genetic information acquisition method.

[0082] When acquiring the genetic information, the acquisition unit can perform filtering based on the user's lifestyle and eating patterns. For example, if the user has a specific eating pattern, the acquisition unit filters the genetic information based on that pattern. For example, the acquisition unit filters the genetic information based on the user's lifestyle (e.g., whether the user is a night owl or a morning person). The acquisition unit can also analyze the user's dietary history and filter the genetic information taking into account the influence of specific nutrients. For example, the acquisition unit filters the genetic information based on the user's dietary history and taking into account the influence of specific nutrients. This makes it possible to filter the genetic information based on the user's lifestyle and eating patterns. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's lifestyle and eating patterns into the generation AI and cause the generation AI to filter the genetic information.

[0083] When acquiring genetic information, the acquisition unit can select an acquisition means according to the user's input method. For example, when the user provides a saliva sample, the acquisition unit acquires the genetic information using a saliva collection kit. For example, when the user provides a blood sample, the acquisition unit acquires the genetic information using a blood collection kit. Furthermore, when the user provides a hair sample, the acquisition unit can also acquire the genetic information using a hair collection kit. For example, when the user provides a hair sample, the acquisition unit acquires the genetic information using a hair collection kit. This makes it possible to select the optimal genetic information acquisition means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input sample data according to the user's input method into the generation AI and cause the generation AI to acquire the genetic information.

[0084] The acquisition unit can estimate the user's emotions and determine the priority of genetic information to be acquired based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit prioritizes acquiring the most important genetic information. For example, when the user is stressed, the acquisition unit sets a low priority for genetic information to be acquired. Furthermore, when the user is in a hurry, the acquisition unit can prioritize genetic information that can be acquired quickly. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows the priority of genetic information to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When acquiring genetic information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the acquisition unit prioritizes acquiring genetic information related to that area. For example, if the user is traveling, the acquisition unit prioritizes acquiring genetic information related to the environment of the travel destination. The acquisition unit can also prioritize acquiring genetic information related to region-specific health risks based on the user's geographical location information. For example, the acquisition unit prioritizes acquiring genetic information related to region-specific health risks based on the user's geographical location information. This allows highly relevant genetic information to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant genetic information.

[0086] When acquiring genetic information, the acquisition unit can analyze the user's social media activities and acquire related information. The acquisition unit acquires related genetic information based on, for example, health information shared by the user on social media. For example, the acquisition unit analyzes the user's social media activity patterns and acquires related genetic information. The acquisition unit can also acquire related genetic information by referring to health information of the user's friends on social media. For example, the acquisition unit acquires related genetic information by referring to health information of the user's friends on social media. This makes it possible to acquire related genetic information based on the user's social media activities. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related genetic information.

[0087] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring genetic information. The acquisition unit customizes the genetic information acquisition method based on, for example, feedback provided by the user in the past. For example, the acquisition unit selects the acquisition method that provides the highest satisfaction from the user's past feedback. The acquisition unit can also analyze the user's past feedback and reflect improvements to the acquisition method. For example, the acquisition unit analyzes the user's past feedback and reflects improvements to the acquisition method. This allows the genetic information acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Furthermore, if the user is feeling stressed, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide the analysis results in a format that is easy to understand. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotions using an emotion estimation algorithm. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information. For example, the analysis unit performs a detailed analysis of important genetic information. For example, the analysis unit performs a detailed analysis of genetic information of high importance based on the function of the gene or an associated disease. The analysis unit can also perform a brief analysis of genetic information of low importance. For example, the analysis unit performs a brief analysis of genetic information of low importance. The analysis unit can also set an analysis priority according to the importance of the genetic information. For example, the analysis unit prioritizes the analysis of genetic information of high importance. This allows the level of detail of the analysis to be adjusted based on the importance of the genetic information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input importance data of the genetic information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of genetic information. For example, the analysis unit applies a specific analysis algorithm to genetic information related to muscle development. For example, the analysis unit applies a specific analysis algorithm to analyze genetic information related to muscle development. The analysis unit can also apply a different analysis algorithm to genetic information related to metabolism. For example, the analysis unit applies a different analysis algorithm to analyze genetic information related to metabolism. The analysis unit can also apply yet another analysis algorithm to genetic information related to health risks. For example, the analysis unit applies yet another analysis algorithm to analyze genetic information related to health risks. This allows the application of an optimal analysis algorithm depending on the category of genetic information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the genetic information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. For example, the analysis unit extracts specific patterns from the user's past analysis results and reflects them in the analysis. The analysis unit can also analyze the user's past analysis results and identify areas for improvement in the analysis algorithm. For example, the analysis unit analyzes the user's past analysis results and identifies areas for improvement in the analysis algorithm. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is stressed, the analysis unit can provide concise and concise analysis results. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that is easy to understand. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0093] During analysis, the analysis unit can determine the analysis priority based on the acquisition time of the genetic information. For example, the analysis unit prioritizes analysis of recently acquired genetic information. For example, the analysis unit sets a low analysis priority for genetic information acquired recently. The analysis unit can also adjust the analysis schedule according to the acquisition time of the genetic information. For example, the analysis unit adjusts the analysis schedule according to the acquisition time of the genetic information. This makes it possible to determine the analysis priority based on the acquisition time of the genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the acquisition time of the genetic information to the generation AI and have the generation AI determine the analysis priority.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the genetic information. For example, the analysis unit prioritizes analysis of highly relevant genetic information. For example, the analysis unit postpones the order of analysis of less relevant genetic information. The analysis unit can also adjust the analysis schedule according to the relevance of the genetic information. For example, the analysis unit adjusts the analysis schedule according to the relevance of the genetic information. This makes it possible to adjust the order of analysis based on the relevance of the genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the genetic information to the generation AI and cause the generation AI to adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can use concise and easy-to-understand terminology. For example, if the user does not have technical expertise, the analysis unit uses concise and easy-to-understand terminology. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the analysis.

[0096] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is feeling stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, the suggestion unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions in a format that is easy to understand. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the way suggestions are presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0097] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the training or nutritional guidance. For example, the suggestion unit makes detailed suggestions for important training or nutritional guidance. For example, the suggestion unit makes detailed suggestions for training or nutritional guidance that is highly important based on the health status or goal achievement level. The suggestion unit can also make brief suggestions for training or nutritional guidance that is less important. For example, the suggestion unit makes brief suggestions for training or nutritional guidance that is less important. The suggestion unit can also set priorities for the proposals based on the importance of the training or nutritional guidance. For example, the suggestion unit prioritizes suggestions for training or nutritional guidance that is highly important. This allows the level of detail of the proposal to be adjusted based on the importance of the training or nutritional guidance. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input importance data for training or nutritional guidance to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0098] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of training or nutritional guidance. For example, the suggestion unit applies a specific suggestion algorithm to suggestions related to strength training. For example, the suggestion unit applies a specific suggestion algorithm to make suggestions related to strength training. The suggestion unit can also apply a different suggestion algorithm to suggestions related to aerobic exercise. For example, the suggestion unit applies a different suggestion algorithm to make suggestions related to aerobic exercise. The suggestion unit can also apply yet another suggestion algorithm to suggestions related to nutritional guidance. For example, the suggestion unit applies yet another suggestion algorithm to make suggestions related to nutritional guidance. This makes it possible to apply an optimal suggestion algorithm depending on the category of training or nutritional guidance. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input category data of training or nutritional guidance to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0099] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the accuracy of the current proposal based on the user's past proposal results. For example, the suggestion unit extracts a specific pattern from the user's past proposal results and reflects it in the proposal. The suggestion unit can also analyze the user's past proposal results and identify areas for improvement in the proposal algorithm. For example, the suggestion unit analyzes the user's past proposal results and identifies areas for improvement in the proposal algorithm. This allows the accuracy of the proposal to be improved based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0100] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user emotion. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions in a format that is easy to understand. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the length of the suggestion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0101] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the training or nutritional guidance. For example, the suggestion unit prioritizes the most recently submitted training or nutritional guidance. For example, the suggestion unit sets a low priority for a proposal of a training or nutritional guidance that was submitted earlier. The suggestion unit can also adjust the schedule of the proposal based on the time of submission of the training or nutritional guidance. For example, the suggestion unit adjusts the schedule of the proposal based on the time of submission of the training or nutritional guidance. This makes it possible to determine the priority of the proposal based on the time of submission of the training or nutritional guidance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the time of submission of the training or nutritional guidance to the generation AI and cause the generation AI to determine the priority of the proposal.

[0102] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the training and nutritional guidance. For example, the proposal unit prioritizes the proposal of highly relevant training and nutritional guidance. For example, the proposal unit postpones the order of proposals for less relevant training and nutritional guidance. The proposal unit can also adjust the schedule of proposals based on the relevance of the training and nutritional guidance. For example, the proposal unit adjusts the schedule of proposals based on the relevance of the training and nutritional guidance. This makes it possible to adjust the order of proposals based on the relevance of the training and nutritional guidance. Some or all of the above-described processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input relevance data of training and nutritional guidance to a generation AI and cause the generation AI to adjust the order of proposals.

[0103] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses detailed technical terminology. For example, if the user has technical expertise, the suggestion unit uses detailed technical terminology. The suggestion unit can also use concise and easy-to-understand terminology if the user does not have technical expertise. For example, if the user does not have technical expertise, the suggestion unit uses concise and easy-to-understand terminology. The suggestion unit can also adjust the way the suggestion result is expressed according to the user's level of expertise. For example, the suggestion unit adjusts the way the suggestion result is expressed according to the user's level of expertise. This allows the use of technical terminology in the suggestion to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the suggestion.

[0104] The collection unit can estimate the user's emotions and adjust the timing of behavioral information collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit adjusts the collection of behavioral information to be performed immediately. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is feeling stressed, the collection unit can postpone the collection of behavioral information and wait for a time when the user can relax. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, the collection unit can provide a simplified procedure for quickly collecting behavioral information when the user is in a hurry. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the timing of behavioral information collection to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0105] The collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the collection unit selects a method for using a pedometer based on the user's past step count data. For example, the collection unit analyzes the user's past location information data and selects a method for using location information. The collection unit can also select the most reliable collection method from the user's past behavioral data. For example, the collection unit analyzes the user's past behavioral data and selects the optimal collection method. This allows the optimal collection method to be selected based on the user's past behavioral data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data to a generation AI and cause the generation AI to select the optimal collection method.

[0106] When collecting behavioral information, the collection unit can filter the behavioral information based on the user's lifestyle habits and activity patterns. For example, if the user has a specific activity pattern, the collection unit filters the behavioral information based on that pattern. For example, the collection unit filters the behavioral information based on the user's lifestyle habits (e.g., whether the user is a night owl or a morning person). The collection unit can also analyze the user's activity history and filter the behavioral information by taking into account the influence of specific activities. For example, the collection unit filters the behavioral information based on the user's activity history by taking into account the influence of specific activities. This makes it possible to filter the behavioral information based on the user's lifestyle habits and activity patterns. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's lifestyle habits and activity patterns to the generation AI and cause the generation AI to filter the behavioral information.

[0107] When collecting behavioral information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses a pedometer, the collection unit collects behavioral information using the pedometer. For example, if the user provides location information, the collection unit collects behavioral information using the location information. Furthermore, if the user provides heart rate data, the collection unit can also collect behavioral information using a heart rate meter. For example, if the user provides heart rate data, the collection unit collects behavioral information using the heart rate meter. This allows the optimal behavioral information collection means to be selected depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data depending on the user's input method to the generation AI and cause the generation AI to collect behavioral information.

[0108] The collection unit can estimate the user's emotions and determine the priority of the behavioral information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting the most important behavioral information. For example, when the user is stressed, the collection unit sets a low priority for the behavioral information to be collected. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting behavioral information that can be collected quickly. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows the priority of the behavioral information to be determined according to the user's emotions. Emotion estimation is realized 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, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0109] When collecting behavioral information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting behavioral information related to that area. For example, if the user is traveling, the collection unit prioritizes collecting behavioral information related to the environment of the travel destination. The collection unit can also prioritize collecting behavioral information related to region-specific health risks based on the user's geographical location information. For example, the collection unit prioritizes collecting behavioral information related to region-specific health risks based on the user's geographical location information. This allows highly relevant behavioral information to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant behavioral information.

[0110] When collecting behavioral information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects related behavioral information based on health information shared by the user on social media. For example, the collection unit analyzes the user's social media activity patterns and collects related behavioral information. The collection unit can also collect related behavioral information by referring to health information of the user's friends on social media. For example, the collection unit collects related behavioral information by referring to health information of the user's friends on social media. This makes it possible to collect related behavioral information based on the user's social media activities. 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 may input the user's social media activity data into a generation AI and cause the generation AI to collect related behavioral information.

[0111] When collecting behavioral information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the behavioral information collection method, for example, based on feedback provided by the user in the past. For example, the collection unit selects the collection method that provides the highest satisfaction from the user's past feedback. The collection unit can also analyze the user's past feedback and reflect improvements to the collection method. For example, the collection unit analyzes the user's past feedback and reflects improvements to the collection method. This allows the behavioral information collection method to be customized based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0112] The providing unit can estimate the user's emotions and adjust the way in which health habit advice is presented based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide detailed advice. For example, the providing unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is feeling stressed, the providing unit can provide concise, to-the-point advice. For example, the providing unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide advice in a format that is easy to understand. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the way in which health habit advice is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0113] When providing health habit advice, the providing unit can adjust the level of detail of the advice based on the importance of the behavioral information. For example, the providing unit provides detailed advice for important behavioral information. For example, the providing unit provides detailed advice for behavioral information with high importance based on the health state and the degree of goal achievement. The providing unit can also provide concise advice for behavioral information with low importance. For example, the providing unit provides concise advice for behavioral information with low importance. The providing unit can also set a priority of advice according to the importance of the behavioral information. For example, the providing unit gives priority to advice for behavioral information with high importance. This makes it possible to adjust the level of detail of the advice based on the importance of the behavioral information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input importance data of the behavioral information to a generating AI and cause the generating AI to adjust the level of detail of the advice.

[0114] When providing health habit advice, the providing unit can apply different advice algorithms depending on the category of behavioral information. For example, the providing unit applies a specific advice algorithm to advice related to exercise habits. For example, the providing unit applies a specific advice algorithm to provide advice related to exercise habits. The providing unit can also apply a different advice algorithm to advice related to eating habits. For example, the providing unit applies a different advice algorithm to provide advice related to eating habits. The providing unit can also apply yet another advice algorithm to advice related to sleeping habits. For example, the providing unit applies yet another advice algorithm to provide advice related to sleeping habits. This makes it possible to apply an optimal advice algorithm depending on the category of behavioral information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the behavioral information to the generation AI and cause the generation AI to apply the advice algorithm.

[0115] When providing health habit advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, improves the accuracy of the current advice based on the user's past advice results. For example, the providing unit extracts a specific pattern from the user's past advice results and reflects it in the advice. The providing unit can also analyze the user's past advice results and identify areas for improvement in the advice algorithm. For example, the providing unit analyzes the user's past advice results and identifies areas for improvement in the advice algorithm. This allows the accuracy of the advice to be improved based on the user's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0116] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, when the user is relaxed, the providing unit provides detailed advice. For example, the providing unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, when the user is feeling stressed, the providing unit can provide concise, to-the-point advice. For example, the providing unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, when the user is in a hurry, the providing unit can provide advice in a format that is quickly understandable. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the length of advice to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate emotions.

[0117] When providing health habit advice, the providing unit can determine the priority of the advice based on the acquisition time of the behavioral information. For example, the providing unit preferentially reflects recently acquired behavioral information in the advice. For example, the providing unit sets a low priority for advice regarding behavioral information that was acquired recently. The providing unit can also adjust the advice schedule according to the acquisition time of the behavioral information. For example, the providing unit adjusts the advice schedule according to the acquisition time of the behavioral information. This makes it possible to determine the priority of advice based on the acquisition time of the behavioral information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the acquisition time of the behavioral information to the generating AI and cause the generating AI to determine the priority of advice.

[0118] When providing health habit advice, the providing unit can adjust the order of advice based on the relevance of behavioral information. For example, the providing unit preferentially reflects highly relevant behavioral information in the advice. For example, the providing unit postpones the order of advice for less relevant behavioral information. The providing unit can also adjust the schedule of advice based on the relevance of behavioral information. For example, the providing unit adjusts the schedule of advice based on the relevance of behavioral information. This makes it possible to adjust the order of advice based on the relevance of behavioral information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of behavioral information to a generating AI and cause the generating AI to adjust the order of advice.

[0119] When providing health habit advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, the providing unit uses detailed technical terms. For example, if the user has technical expertise, the providing unit uses detailed technical terms. Furthermore, if the user does not have technical expertise, the providing unit can use concise and easy-to-understand terms. For example, if the user does not have technical expertise, the providing unit uses concise and easy-to-understand terms. Furthermore, the providing unit can adjust the way in which the advice result is expressed according to the user's level of expertise. For example, the providing unit adjusts the way in which the advice result is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terms in the advice. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, suggestion unit, collection unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire the user's genetic information using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired genetic information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests training and nutritional advice based on the analysis results. The collection unit collects smartphone data via the communication I / F 44 of the smart device 14 and understands daily exercise volume and activity patterns. The provision unit is realized by the control unit 46A of the smart device 14 and provides health habit advice based on the collected behavioral information. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, suggestion unit, collection unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire the user's genetic information using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired genetic information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests training and nutritional advice based on the analysis results. The collection unit collects smartphone data via the communication I / F 44 of the smart glasses 214 and understands daily exercise volume and activity patterns. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides health habit advice based on the collected behavioral information. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, analysis unit, suggestion unit, collection unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit can acquire the user's genetic information using the camera 42 or microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired genetic information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests training and nutritional advice based on the analysis results. The collection unit collects smartphone data via the communication I / F 44 of the headset-type terminal 314 and understands the user's daily exercise volume and activity patterns. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides health habit advice based on the collected behavioral information. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, analysis unit, suggestion unit, collection unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire the user's genetic information using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired genetic information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests training and nutritional advice based on the analysis results. The collection unit collects smartphone data via the communication I / F 44 of the robot 414 and understands the amount of daily exercise and activity patterns. The provision unit is realized by the control unit 46A of the robot 414 and provides health habit advice based on the collected behavioral information.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The fitness service system can further acquire the user's sleep data and analyze it with an analysis unit. For example, the acquisition unit can use sensors on the user's smartwatch or smartphone to record the quality and duration of sleep. The analysis unit can analyze the user's sleep patterns based on this data and provide advice for improving sleep quality. The suggestion unit can propose a sleep improvement plan suitable for the user based on the analysis results. For example, specific advice can be provided, such as going to bed at a specific time or avoiding certain foods. This allows the fitness service system to more comprehensively support the user's overall health condition.

[0122] The fitness service system can further monitor the user's stress level and analyze it with the analysis unit. For example, the acquisition unit can collect the user's heart rate and electrodermal activity with a sensor and estimate the stress level. The analysis unit can analyze the user's stress pattern based on this data and provide advice for stress management. The suggestion unit can propose a stress reduction plan suitable for the user based on the analysis results. For example, it can recommend relaxation exercises or meditation. In this way, the fitness service system can also support the user's mental health.

[0123] The fitness service system can further acquire the user's dietary data and analyze it with the analysis unit. For example, the acquisition unit collects dietary data using an app that allows the user to record their dietary habits. The analysis unit can analyze the user's nutritional balance based on this data and provide advice for improving their diet. The suggestion unit can propose a meal plan suitable for the user based on the analysis results. For example, it can suggest ingredients and recipes for increasing specific nutrients. This allows the fitness service system to provide support for making the user's diet healthier.

[0124] The fitness service system can further estimate the user's emotions and adjust the difficulty of the training based on the estimated emotions. For example, the acquisition unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit uses this data to understand the user's current emotional state and adjust the difficulty of the training. The suggestion unit suggests a training plan suitable for the user based on the analysis results. For example, if the user is feeling stressed, it can suggest light exercise that will help them relax, and if the user is relaxed, it can suggest a more challenging training plan. This allows the fitness service system to provide flexible training plans that correspond to the user's emotional state.

[0125] The fitness service system can further acquire the user's social activity data and analyze it with the analysis unit. For example, the acquisition unit collects the user's social media activity and event participation history. The analysis unit can analyze the user's social activity pattern based on this data and provide advice for balancing social activities. The suggestion unit can propose a social activity plan suitable for the user based on the analysis results. For example, it can recommend participating in a specific event or taking up a new hobby. In this way, the fitness service system can also support the user's social health.

[0126] The fitness service system can further estimate the user's emotions and adjust the content of the nutritional guidance based on the estimated emotions. For example, the acquisition unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit understands the user's current emotional state based on this data and adjusts the content of the nutritional guidance. The suggestion unit suggests nutritional guidance suitable for the user based on the analysis results. For example, if the user is feeling stressed, it can suggest ingredients and recipes that are effective in reducing stress, and if the user is relaxed, it can suggest a meal plan that emphasizes nutritional balance. This allows the fitness service system to provide flexible nutritional guidance according to the user's emotional state.

[0127] The fitness service system can further acquire the user's exercise history data and analyze it with the analysis unit. For example, the acquisition unit collects data on the user's past training. Based on this data, the analysis unit analyzes the user's exercise history and understands the user's training progress. Based on the analysis results, the suggestion unit proposes a training plan suitable for the user. For example, based on past training data, it can identify areas in need of improvement for the user and propose training accordingly. This allows the fitness service system to provide a personalized training plan based on the user's exercise history.

[0128] The fitness service system can further estimate the user's emotions and adjust the timing of health habit advice based on the estimated emotions. For example, the acquisition unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit uses this data to understand the user's current emotional state and adjust the timing of the advice. The provision unit provides health habit advice at a timing appropriate for the user based on the analysis results. For example, if the user is relaxed, detailed advice can be provided, and if the user is feeling stressed, concise advice that focuses on the main points can be provided. This allows the fitness service system to provide flexible advice according to the user's emotional state.

[0129] The fitness service system can further acquire the user's geographical location information and analyze it with the analysis unit. For example, the acquisition unit can use GPS data from the user's smartphone to collect the user's location information. Based on this data, the analysis unit analyzes the user's activity area and movement patterns to identify health risks specific to the area. The suggestion unit provides the user with advice on health habits appropriate for the user based on the analysis results. For example, it can suggest exercise and dietary precautions for a specific area. This allows the fitness service system to provide personalized health advice based on the user's geographical location information.

[0130] The fitness service system can further estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, the acquisition unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit understands the user's current emotional state based on this data and adjusts the content of the feedback. The provision unit provides feedback appropriate to the user based on the analysis results. For example, if the user is relaxed, detailed feedback can be provided, and if the user is feeling stressed, brief feedback that focuses on the main points can be provided. This allows the fitness service system to provide flexible feedback according to the user's emotional state.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The acquisition unit acquires genetic information using a gene collection kit familiar to the user. For example, it collects a sample of saliva or blood and analyzes the genetic information. Step 2: The analysis unit uses analytical AI to analyze the genetic information acquired by the acquisition unit. For example, it identifies individual physical characteristics based on the genetic information and proposes optimal training and nutritional guidance. Step 3: The suggestion unit proposes training and nutritional advice based on the information analyzed by the analysis unit. For example, it analyzes the likelihood of muscle development and metabolic characteristics from genetic information and creates a training plan based on that. Step 4: The collection unit collects behavioral information obtained from smartphone data. For example, it uses the smartphone's pedometer and location information to understand daily exercise volume and activity patterns. Step 5: The providing unit provides advice on health habits based on the behavioral information collected by the collecting unit. For example, the providing unit suggests improvements to health habits based on the collected behavioral information.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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 AI 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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, in order to avoid confusion and to 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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. an acquisition unit that acquires genetic information; an analysis unit that analyzes the genetic information acquired by the acquisition unit; a suggestion unit that suggests training and nutritional advice based on the information analyzed by the analysis unit; A collection unit that collects behavioral information obtained from smartphone data; a providing unit that provides advice on health habits based on the behavioral information collected by the collecting unit; Equipped with A system characterized by:

2. The acquisition unit Obtaining genetic information using a gene collection kit 2. The system of claim 1.

3. The analysis unit Understanding individual physical characteristics based on genetic information 2. The system of claim 1.

4. The proposal unit Analyzing muscle development and metabolic characteristics from genetic information and creating training plans based on that.

2. The system of claim 1.

5. The collecting unit Use your smartphone's pedometer or location information to collect information on your daily exercise and activity patterns.

2. The system of claim 1.

6. The providing unit Recommend improvements to health habits based on collected behavioral information 2. The system of claim 1.

7. The acquisition unit The system estimates the user's emotions and adjusts the timing of genetic information acquisition based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past health data and select the optimal method for obtaining genetic information 2. The system of claim 1.

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