system

Abstract

The system according to the embodiment aims to provide an appropriate health program for each individual user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health-related information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a health program appropriate for each individual user based on the results of the analysis by the analysis unit. The provision unit provides the health program generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to provide an optimal health program for each individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an appropriate health program for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health-related information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a health program appropriate for each user based on the results of the analysis by the analysis unit. The provision unit provides the health program generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate health program for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A health program generation system according to an embodiment of the present invention collects, analyzes, generates, and provides health-related information. The health program generation system aggregates online health information and personal information from daily life and automatically generates a "100-Year Health Program" using a generation AI to improve lifestyles. For example, the health program generation system collects online health-related information, primarily from reliable medical institutions and research institutes, to obtain the latest health information. The health program generation system then collects personal information from social media, email, and smartphone sensors. For example, it collects data such as photos of meals and exercise records posted by users on social media, as well as step counts and sleep duration detected by smartphone sensors. The health program generation system then uses a generation AI to analyze the user's lifestyle and health status based on the collected information and provide specific advice. For example, it provides recommendations for improving diet, exercise, and sleep quality. This allows the health program generation system to provide a customized health program based on the user's lifestyle and health status. This allows the health program generation system to improve the user's lifestyle and achieve a healthier, longer, and happier lifestyle. For example, if a user wants to improve their diet, the AI ​​will analyze their food records and suggest a nutritionally balanced meal plan. If they want to get more exercise, the AI ​​will suggest an appropriate exercise program based on their exercise records. Furthermore, if they want to improve their sleep quality, the AI ​​will analyze their sleep data and suggest ways to improve their sleep environment.

[0029] A health program generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health-related information. For example, the collection unit collects health information from the web. The collection unit primarily collects information from reliable medical institutions and research institutes to obtain the latest health information. The collection unit can also collect personal information from social media, email, and smartphone sensors. For example, the collection unit collects data such as photos of meals and exercise records posted by users on social media, and data such as the number of steps and sleep time detected by smartphone sensors. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's lifestyle and health status based on the collected information using techniques such as data mining, statistical analysis, and machine learning. The generation unit generates an optimal health program for each individual user based on the results of the analysis by the analysis unit. The generation unit uses a generation AI to generate a customized health program based on the user's lifestyle and health status. For example, the generation AI analyzes the user's food records and proposes a nutritionally balanced meal plan. The generation AI can also propose an appropriate exercise program based on the user's exercise records. Furthermore, the generation AI can analyze the user's sleep data and suggest improvements to the sleep environment. The provision unit provides the health program generated by the generation unit. The provision unit provides specific advice based on the generated health program using means such as app notifications, emails, and websites. This allows the health program generation system according to the embodiment to provide the user with an optimal health program.

[0030] The collection unit can collect health information from the web. For example, the collection unit mainly collects information from reliable medical institutions and research institutions to obtain the latest health information. The collection unit can collect information from, for example, medical sites, health blogs, government health databases, etc. In this way, by collecting health information from the web, the latest health information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for collecting health information from the web.

[0031] The collection unit can collect personal information from social media, email, or a smartphone sensor. The collection unit collects, for example, data such as photos of meals and exercise records posted by the user on social media, and data such as the number of steps and sleep time detected by a smartphone sensor. The collection unit can collect, for example, the content of posts on social media, the content of emails, and smartphone sensor data. By collecting personal information, detailed information about the user's daily life can be obtained. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for collecting personal information from social media, email, or a smartphone sensor.

[0032] The analysis unit can analyze the user's lifestyle habits and health condition based on the collected information. The analysis unit can analyze the user's lifestyle habits and health condition based on the collected information using techniques such as data mining, statistical analysis, and machine learning. The analysis unit can analyze lifestyle habits such as eating patterns, exercise frequency, and sleep quality, as well as health conditions such as blood pressure, weight, and heart rate. By analyzing the user's lifestyle habits and health condition, it is possible to generate an optimal health program for each individual user. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform the analysis using an AI model for analyzing the user's lifestyle habits and health condition based on the collected information.

[0033] The generation unit can generate a customized health program based on the user's lifestyle and health condition. The generation unit uses a generation AI to generate a customized health program based on the user's lifestyle and health condition. The generation unit can, for example, analyze the user's food records and propose a nutritionally balanced meal plan. The generation unit can also, for example, propose an appropriate exercise program based on the user's exercise records. The generation unit can, for example, analyze the user's sleep data and propose improvements to the sleep environment. By generating a customized health program based on the user's lifestyle and health condition, optimal advice can be provided to each individual user. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can generate a program using a generation AI model for generating a customized health program based on the user's lifestyle and health condition.

[0034] The providing unit can provide specific advice based on the generated health program. The providing unit can provide specific advice based on the generated health program. The providing unit can provide specific advice based on the generated health program using means such as app notifications, emails, and websites. The providing unit can provide specific advice such as suggestions for improving diet, recommended exercise, and stress management methods. By providing specific advice based on the generated health program, it is possible to improve the user's lifestyle. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide advice using an AI model for providing specific advice based on the generated health program.

[0035] The collection unit can analyze the user's past health data and select an appropriate collection method. The collection unit, for example, selects the most effective information collection means from the user's past health data. The collection unit, for example, can adjust the collection frequency based on the user's past health data. The collection unit, for example, can analyze the user's past health data and collect information with an emphasis on specific health indicators. This allows the optimal collection method to be selected by analyzing the user's past health data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can analyze the user's past health data and collect information using an AI model to select an appropriate collection method.

[0036] When collecting health information, the collection unit can perform filtering based on the user's current health condition and lifestyle habits. The collection unit, for example, collects only necessary information based on the user's current health condition. The collection unit can, for example, preferentially collect highly relevant information based on the user's lifestyle habits. The collection unit can, for example, adjust the type of information to be collected according to the user's health condition and lifestyle habits. This allows filtering based on the user's current health condition and lifestyle habits to collect only necessary information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for filtering based on the user's current health condition and lifestyle habits.

[0037] When collecting health information, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can prioritize collecting voice data. For example, when the user uses text input, the collection unit can prioritize collecting text data. For example, when the user uses image input, the collection unit can prioritize collecting image data. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for selecting the optimal collection means depending on the user's input method.

[0038] When collecting health information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting health information for the area where the user is currently located. For example, if the user is traveling, the collection unit can prioritize collecting health information for the travel destination. For example, the collection unit can collect area-specific health information based on the user's geographical location. This allows highly relevant information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for preferentially collecting highly relevant information by taking into account the user's geographical location information.

[0039] When collecting health information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects health information shared by the user on social media. For example, the collection unit can collect related health information from the user's social media activities. For example, the collection unit can refer to the activities of the user's friends on social media to collect related health information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's social media activities and collect information using an AI model for collecting related information.

[0040] When collecting health information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit can, for example, adjust the collection method based on feedback provided by the user in the past. The collection unit can, for example, select the type of information to collect based on the user's past feedback. The collection unit can, for example, adjust the collection frequency by reflecting the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can collect information using an AI model for customizing the collection method by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the health information during analysis. For example, the analysis unit can perform a detailed analysis of health information with high importance. For example, the analysis unit can perform a simplified analysis of health information with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the health information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health 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 perform the analysis using an AI model for adjusting the level of detail of the analysis based on the importance of the health information.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health information. For example, the analysis unit can apply a nutrition analysis algorithm to information about diet. For example, the analysis unit can apply an exercise analysis algorithm to information about exercise. For example, the analysis unit can apply a sleep analysis algorithm to information about sleep. By applying different analysis algorithms depending on the category of health information, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform analysis using an AI model for applying different analysis algorithms depending on the category of health information.

[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, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit, for example, can set parameters for improving the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, can determine the priority of the analysis by referring to the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for improving the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the collection date of the health information. For example, the analysis unit prioritizes analysis of the most recent health information. For example, the analysis unit can perform a simplified analysis of older health information. For example, the analysis unit can determine the priority of analysis according to the collection date of the health information. As a result, by determining the priority of analysis based on the collection date of the health information, the most recent information can be analyzed preferentially. 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 perform analysis using an AI model for determining the priority of analysis based on the collection date of the health information.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health information. For example, the analysis unit prioritizes analysis of highly relevant health information. For example, the analysis unit can postpone analysis of less relevant health information. For example, the analysis unit can determine the order of analysis according to the relevance of the health information. As a result, by adjusting the order of analysis based on the relevance of the health information, highly relevant information can be analyzed preferentially. 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 perform analysis using an AI model for adjusting the order of analysis based on the relevance of the health information.

[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 can use detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for adjusting the use of technical terminology in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. 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 perform the analysis using an AI model for adjusting the use of technical terminology in the analysis according to the user's level of expertise.

[0047] When generating a health program, the generation unit can adjust the level of detail of the program based on the user's health condition. For example, if the user's health condition is good, the generation unit can generate a detailed health program. For example, if the user's health condition is poor, the generation unit can generate a simplified health program. For example, the generation unit can adjust the level of detail of the program according to the user's health condition. This allows for adjusting the level of detail of the program based on the user's health condition, making it possible to provide a more appropriate health program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the level of detail of the program based on the user's health condition.

[0048] When generating a health program, the generation unit can apply different generation algorithms depending on the user's lifestyle habits. For example, the generation unit can apply an algorithm for generating a meal program based on the user's eating habits. For example, the generation unit can apply an algorithm for generating an exercise program based on the user's exercise habits. For example, the generation unit can apply an algorithm for generating a sleep program based on the user's sleeping habits. This allows for the application of different generation algorithms depending on the user's lifestyle habits, thereby providing a more appropriate health program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for applying different generation algorithms depending on the user's lifestyle habits.

[0049] When generating a health program, the generation unit can improve the accuracy of generation by referring to the user's past program results. The generation unit, for example, adjusts the generation algorithm based on the user's past program results. The generation unit, for example, can set parameters for improving the accuracy of generation based on the user's past program results. The generation unit, for example, can determine the priority of generation by referring to the user's past program results. This can improve the accuracy of generation by referring to the user's past program results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for improving the accuracy of generation by referring to the user's past program results.

[0050] When generating a health program, the generation unit can determine the priority of the programs based on the user's lifestyle rhythm. For example, the generation unit can prioritize programs to be executed in the morning based on the user's lifestyle rhythm. For example, the generation unit can prioritize programs to be executed in the evening based on the user's lifestyle rhythm. For example, the generation unit can prioritize programs to be executed on weekends based on the user's lifestyle rhythm. This allows for providing more appropriate programs by determining the priority of programs based on the user's lifestyle rhythm. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for determining the priority of programs based on the user's lifestyle rhythm.

[0051] When generating a health program, the generation unit can adjust the order of the programs based on the user's health goals. For example, if the user's health goal is weight loss, the generation unit can prioritize generating an exercise program. For example, if the user's health goal is muscle strengthening, the generation unit can prioritize generating a strength training program. For example, if the user's health goal is stress reduction, the generation unit can prioritize generating a relaxation program. This allows for adjusting the order of the programs based on the user's health goals, thereby providing a more appropriate program. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the order of the programs based on the user's health goals.

[0052] When generating a health program, the generation unit can adjust the use of technical terminology in the program according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a health program using detailed technical terminology. For example, if the user does not have technical expertise, the generation unit can generate a health program explained in simple terms. For example, the generation unit can adjust the way the program is expressed according to the user's level of expertise. This allows for a more understandable program to be provided by adjusting the use of technical terminology in the program according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the use of technical terminology in the program according to the user's level of expertise.

[0053] When providing advice, the providing unit can adjust the level of detail of the advice based on the user's health condition. For example, when the user's health condition is good, the providing unit can provide detailed advice. For example, when the user's health condition is poor, the providing unit can provide simplified advice. For example, the providing unit can adjust the level of detail of the advice according to the user's health condition. As a result, more appropriate advice can be provided by adjusting the level of detail of the advice based on the user's health condition. 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 provide advice using an AI model for adjusting the level of detail of the advice based on the user's health condition.

[0054] The providing unit can apply different advice algorithms depending on the user's lifestyle habits when providing advice. The providing unit can apply, for example, an algorithm for providing dietary advice based on the user's eating habits. The providing unit can apply, for example, an algorithm for providing exercise advice based on the user's exercise habits. The providing unit can apply, for example, an algorithm for providing sleep advice based on the user's sleeping habits. In this way, by applying different advice algorithms depending on the user's lifestyle habits, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide advice using an AI model for applying different advice algorithms depending on the user's lifestyle habits.

[0055] When providing advice, the providing unit can improve the accuracy of the advice provided by referring to the user's past advice results. The providing unit, for example, adjusts an advice algorithm based on the user's past advice results. The providing unit, for example, can set parameters for improving the accuracy of the advice provided by referring to the user's past advice results. The providing unit, for example, can determine the priority of advice by referring to the user's past advice results. This can improve the accuracy of the advice provided by referring to the user's past advice results. 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 provide advice by using an AI model for improving the accuracy of the advice provided by referring to the user's past advice results.

[0056] When providing advice, the providing unit can determine the priority of advice based on the user's lifestyle rhythm. For example, the providing unit can prioritize advice to be performed in the morning based on the user's lifestyle rhythm. For example, the providing unit can prioritize advice to be performed in the evening based on the user's lifestyle rhythm. For example, the providing unit can prioritize advice to be performed on weekends according to the user's lifestyle rhythm. In this way, by determining the priority of advice based on the user's lifestyle rhythm, more appropriate advice can be provided. 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 provide advice using an AI model for determining the priority of advice based on the user's lifestyle rhythm.

[0057] When providing advice, the providing unit can adjust the order of advice based on the user's health goal. For example, if the user's health goal is weight loss, the providing unit can prioritize providing exercise advice. For example, if the user's health goal is muscle strength building, the providing unit can prioritize providing strength training advice. For example, if the user's health goal is stress reduction, the providing unit can prioritize providing relaxation advice. In this way, by adjusting the order of advice based on the user's health goal, more appropriate advice can be provided. 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 provide advice using an AI model for adjusting the order of advice based on the user's health goal.

[0058] When providing 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 can provide advice using detailed technical terms. For example, if the user does not have technical expertise, the providing unit can provide advice explained in simple language. For example, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. This makes it possible to provide advice that is easier to understand by adjusting 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, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model for adjusting the use of technical terms in the advice according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the user's past health data and select an appropriate collection method. For example, the collection unit can select the most effective information collection method from the user's past health data. The collection frequency can be adjusted based on the user's past health data. The collection unit can analyze the user's past health data and collect information with an emphasis on specific health indicators. This allows the optimal collection method to be selected by analyzing the user's past health data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the user's past health data and collect information using an AI model to select an appropriate collection method.

[0061] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health information. For example, a detailed analysis can be performed for health information with high importance. A simplified analysis can be performed for health information with low importance. The priority of the analysis can be determined according to the importance of the health information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health information. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for adjusting the level of detail of the analysis based on the importance of the health information.

[0062] When generating a health program, the generation unit can adjust the level of detail of the program based on the user's health condition. For example, if the user's health condition is good, a detailed health program can be generated. If the user's health condition is poor, a simplified health program can be generated. The level of detail of the program can be adjusted according to the user's health condition. This allows for a more appropriate health program to be provided by adjusting the level of detail of the program based on the user's health condition. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the level of detail of the program based on the user's health condition.

[0063] When providing advice, the providing unit can adjust the level of detail of the advice based on the user's health condition. For example, if the user's health condition is good, detailed advice can be provided. If the user's health condition is poor, simplified advice can be provided. The level of detail of the advice can be adjusted according to the user's health condition. In this way, by adjusting the level of detail of the advice based on the user's health condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can provide advice using an AI model for adjusting the level of detail of the advice based on the user's health condition.

[0064] When providing advice, the providing unit can determine the priority of advice based on the user's lifestyle rhythm. For example, based on the user's lifestyle rhythm, advice to be performed in the morning hours can be provided preferentially. Based on the user's lifestyle rhythm, advice to be performed in the evening hours can be provided preferentially. Advice to be performed on weekends can be provided preferentially according to the user's lifestyle rhythm. In this way, by determining the priority of advice based on the user's lifestyle rhythm, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model for determining the priority of advice based on the user's lifestyle rhythm.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects health-related information. For example, the collection unit collects health information from the web. The collection unit mainly collects information from reliable medical institutions and research institutes, allowing it to obtain the latest health information. The collection unit can also collect personal information from social media, email, and smartphone sensors. For example, it collects data such as photos of meals and exercise records posted by users on social media, as well as data detected by smartphone sensors, such as the number of steps taken and sleep time. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's lifestyle and health condition based on the collected information using techniques such as data mining, statistical analysis, and machine learning. Step 3: The generation unit generates an optimal health program for each individual user based on the results of the analysis by the analysis unit. The generation unit uses the generation AI to generate a customized health program based on the user's lifestyle and health condition. For example, the generation AI can analyze the user's food records and suggest a nutritionally balanced meal plan. The generation AI can also suggest an appropriate exercise program based on the user's exercise records. Furthermore, the generation AI can analyze the user's sleep data and suggest improvements to the sleep environment. Step 4: The providing unit provides the health program generated by the generating unit. The providing unit provides specific advice based on the generated health program using means such as app notifications, emails, websites, etc.

[0067] (Example 2) A health program generation system according to an embodiment of the present invention collects, analyzes, generates, and provides health-related information. The health program generation system aggregates online health information and personal information from daily life and automatically generates a "100-Year Health Program" using a generation AI to improve lifestyles. For example, the health program generation system collects online health-related information, primarily from reliable medical institutions and research institutes, to obtain the latest health information. The health program generation system then collects personal information from social media, email, and smartphone sensors. For example, it collects data such as photos of meals and exercise records posted by users on social media, as well as step counts and sleep duration detected by smartphone sensors. The health program generation system then uses a generation AI to analyze the user's lifestyle and health status based on the collected information and provide specific advice. For example, it provides recommendations for improving diet, exercise, and sleep quality. This allows the health program generation system to provide a customized health program based on the user's lifestyle and health status. This allows the health program generation system to improve the user's lifestyle and achieve a healthier, longer, and happier lifestyle. For example, if a user wants to improve their diet, the AI ​​will analyze their food records and suggest a nutritionally balanced meal plan. If they want to get more exercise, the AI ​​will suggest an appropriate exercise program based on their exercise records. Furthermore, if they want to improve their sleep quality, the AI ​​will analyze their sleep data and suggest ways to improve their sleep environment.

[0068] A health program generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health-related information. For example, the collection unit collects health information from the web. The collection unit primarily collects information from reliable medical institutions and research institutes to obtain the latest health information. The collection unit can also collect personal information from social media, email, and smartphone sensors. For example, the collection unit collects data such as photos of meals and exercise records posted by users on social media, and data such as the number of steps and sleep time detected by smartphone sensors. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's lifestyle and health status based on the collected information using techniques such as data mining, statistical analysis, and machine learning. The generation unit generates an optimal health program for each individual user based on the results of the analysis by the analysis unit. The generation unit uses a generation AI to generate a customized health program based on the user's lifestyle and health status. For example, the generation AI analyzes the user's food records and proposes a nutritionally balanced meal plan. The generation AI can also propose an appropriate exercise program based on the user's exercise records. Furthermore, the generation AI can analyze the user's sleep data and suggest improvements to the sleep environment. The provision unit provides the health program generated by the generation unit. The provision unit provides specific advice based on the generated health program using means such as app notifications, emails, and websites. This allows the health program generation system according to the embodiment to provide the user with an optimal health program.

[0069] The collection unit can collect health information from the web. For example, the collection unit mainly collects information from reliable medical institutions and research institutions to obtain the latest health information. The collection unit can collect information from, for example, medical sites, health blogs, government health databases, etc. In this way, by collecting health information from the web, the latest health information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for collecting health information from the web.

[0070] The collection unit can collect personal information from social media, email, or a smartphone sensor. The collection unit collects, for example, data such as photos of meals and exercise records posted by the user on social media, and data such as the number of steps and sleep time detected by a smartphone sensor. The collection unit can collect, for example, the content of posts on social media, the content of emails, and smartphone sensor data. By collecting personal information, detailed information about the user's daily life can be obtained. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for collecting personal information from social media, email, or a smartphone sensor.

[0071] The analysis unit can analyze the user's lifestyle habits and health condition based on the collected information. The analysis unit can analyze the user's lifestyle habits and health condition based on the collected information using techniques such as data mining, statistical analysis, and machine learning. The analysis unit can analyze lifestyle habits such as eating patterns, exercise frequency, and sleep quality, as well as health conditions such as blood pressure, weight, and heart rate. By analyzing the user's lifestyle habits and health condition, it is possible to generate an optimal health program for each individual user. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform the analysis using an AI model for analyzing the user's lifestyle habits and health condition based on the collected information.

[0072] The generation unit can generate a customized health program based on the user's lifestyle and health condition. The generation unit uses a generation AI to generate a customized health program based on the user's lifestyle and health condition. The generation unit can, for example, analyze the user's food records and propose a nutritionally balanced meal plan. The generation unit can also, for example, propose an appropriate exercise program based on the user's exercise records. The generation unit can, for example, analyze the user's sleep data and propose improvements to the sleep environment. By generating a customized health program based on the user's lifestyle and health condition, optimal advice can be provided to each individual user. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can generate a program using a generation AI model for generating a customized health program based on the user's lifestyle and health condition.

[0073] The providing unit can provide specific advice based on the generated health program. The providing unit can provide specific advice based on the generated health program. The providing unit can provide specific advice based on the generated health program using means such as app notifications, emails, and websites. The providing unit can provide specific advice such as suggestions for improving diet, recommended exercise, and stress management methods. By providing specific advice based on the generated health program, it is possible to improve the user's lifestyle. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide advice using an AI model for providing specific advice based on the generated health program.

[0074] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect information during times when the user is relaxed. For example, if the user is relaxed, the collection unit can collect information frequently to collect detailed data. For example, if the user is in a hurry, the collection unit can temporarily stop information collection and resume it later. This allows information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for estimating the user's emotions and adjusting the timing of information collection based on the estimated user emotions.

[0075] The collection unit can analyze the user's past health data and select an appropriate collection method. The collection unit, for example, selects the most effective information collection means from the user's past health data. The collection unit, for example, can adjust the collection frequency based on the user's past health data. The collection unit, for example, can analyze the user's past health data and collect information with an emphasis on specific health indicators. This allows the optimal collection method to be selected by analyzing the user's past health data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can analyze the user's past health data and collect information using an AI model to select an appropriate collection method.

[0076] When collecting health information, the collection unit can perform filtering based on the user's current health condition and lifestyle habits. The collection unit, for example, collects only necessary information based on the user's current health condition. The collection unit can, for example, preferentially collect highly relevant information based on the user's lifestyle habits. The collection unit can, for example, adjust the type of information to be collected according to the user's health condition and lifestyle habits. This allows filtering based on the user's current health condition and lifestyle habits to collect only necessary information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for filtering based on the user's current health condition and lifestyle habits.

[0077] When collecting health information, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can prioritize collecting voice data. For example, when the user uses text input, the collection unit can prioritize collecting text data. For example, when the user uses image input, the collection unit can prioritize collecting image data. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for selecting the optimal collection means depending on the user's input method.

[0078] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting information related to stress reduction. For example, when the user is relaxed, the collection unit can prioritize collecting information related to maintaining health. For example, when the user is in a hurry, the collection unit can prioritize collecting information that can be obtained in a short time. This allows more appropriate information to be collected by determining the priority of information to be collected based on 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for estimating the user's emotions and determining the priority of information to be collected based on the estimated user emotions.

[0079] When collecting health information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting health information for the area where the user is currently located. For example, if the user is traveling, the collection unit can prioritize collecting health information for the travel destination. For example, the collection unit can collect area-specific health information based on the user's geographical location. This allows highly relevant information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model for preferentially collecting highly relevant information by taking into account the user's geographical location information.

[0080] When collecting health information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects health information shared by the user on social media. For example, the collection unit can collect related health information from the user's social media activities. For example, the collection unit can refer to the activities of the user's friends on social media to collect related health information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's social media activities and collect information using an AI model for collecting related information.

[0081] When collecting health information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit can, for example, adjust the collection method based on feedback provided by the user in the past. The collection unit can, for example, select the type of information to collect based on the user's past feedback. The collection unit can, for example, adjust the collection frequency by reflecting the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can collect information using an AI model for customizing the collection method by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the health information during analysis. For example, the analysis unit can perform a detailed analysis of health information with high importance. For example, the analysis unit can perform a simplified analysis of health information with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the health information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health 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 perform the analysis using an AI model for adjusting the level of detail of the analysis based on the importance of the health information.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health information. For example, the analysis unit can apply a nutrition analysis algorithm to information about diet. For example, the analysis unit can apply an exercise analysis algorithm to information about exercise. For example, the analysis unit can apply a sleep analysis algorithm to information about sleep. By applying different analysis algorithms depending on the category of health information, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform analysis using an AI model for applying different analysis algorithms depending on the category of health information.

[0085] 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, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit, for example, can set parameters for improving the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, can determine the priority of the analysis by referring to the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for improving the accuracy of the analysis by referring to the user's past analysis results.

[0086] 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 stressed, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform analysis using an AI model for estimating the user's emotions and adjusting the length of the analysis based on the estimated user emotions.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the collection date of the health information. For example, the analysis unit prioritizes analysis of the most recent health information. For example, the analysis unit can perform a simplified analysis of older health information. For example, the analysis unit can determine the priority of analysis according to the collection date of the health information. As a result, by determining the priority of analysis based on the collection date of the health information, the most recent information can be analyzed preferentially. 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 perform analysis using an AI model for determining the priority of analysis based on the collection date of the health information.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health information. For example, the analysis unit prioritizes analysis of highly relevant health information. For example, the analysis unit can postpone analysis of less relevant health information. For example, the analysis unit can determine the order of analysis according to the relevance of the health information. As a result, by adjusting the order of analysis based on the relevance of the health information, highly relevant information can be analyzed preferentially. 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 perform analysis using an AI model for adjusting the order of analysis based on the relevance of the health information.

[0089] 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 can use detailed technical terminology. For example, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows for adjusting the use of technical terminology in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. 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 perform the analysis using an AI model for adjusting the use of technical terminology in the analysis according to the user's level of expertise.

[0090] The generation unit can estimate the user's emotions and adjust the presentation method of the generated health program based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a simple, highly visible health program. For example, if the user is relaxed, the generation unit can generate a detailed health program. For example, if the user is in a hurry, the generation unit can generate a health program that focuses on the main points. This allows for adjusting the presentation method of the health program based on the user's emotions, thereby providing a more appropriate program. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model that estimates the user's emotions and adjusts the presentation method of the generated health program based on the estimated user emotions.

[0091] When generating a health program, the generation unit can adjust the level of detail of the program based on the user's health condition. For example, if the user's health condition is good, the generation unit can generate a detailed health program. For example, if the user's health condition is poor, the generation unit can generate a simplified health program. For example, the generation unit can adjust the level of detail of the program according to the user's health condition. This allows for adjusting the level of detail of the program based on the user's health condition, making it possible to provide a more appropriate health program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the level of detail of the program based on the user's health condition.

[0092] When generating a health program, the generation unit can apply different generation algorithms depending on the user's lifestyle habits. For example, the generation unit can apply an algorithm for generating a meal program based on the user's eating habits. For example, the generation unit can apply an algorithm for generating an exercise program based on the user's exercise habits. For example, the generation unit can apply an algorithm for generating a sleep program based on the user's sleeping habits. This allows for the application of different generation algorithms depending on the user's lifestyle habits, thereby providing a more appropriate health program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for applying different generation algorithms depending on the user's lifestyle habits.

[0093] When generating a health program, the generation unit can improve the accuracy of generation by referring to the user's past program results. The generation unit, for example, adjusts the generation algorithm based on the user's past program results. The generation unit, for example, can set parameters for improving the accuracy of generation based on the user's past program results. The generation unit, for example, can determine the priority of generation by referring to the user's past program results. This can improve the accuracy of generation by referring to the user's past program results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for improving the accuracy of generation by referring to the user's past program results.

[0094] The generation unit can estimate the user's emotions and adjust the length of the generated health program based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a short and concise health program. For example, if the user is relaxed, the generation unit can generate a longer health program with detailed explanations. For example, if the user is in a hurry, the generation unit can generate a concise and quickly executable health program. This allows for providing a more appropriate program by adjusting the length of the health program based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can generate a program using an AI model for estimating the user's emotions and adjusting the length of the health program based on the estimated user emotions.

[0095] When generating a health program, the generation unit can determine the priority of the programs based on the user's lifestyle rhythm. For example, the generation unit can prioritize programs to be executed in the morning based on the user's lifestyle rhythm. For example, the generation unit can prioritize programs to be executed in the evening based on the user's lifestyle rhythm. For example, the generation unit can prioritize programs to be executed on weekends based on the user's lifestyle rhythm. This allows for providing more appropriate programs by determining the priority of programs based on the user's lifestyle rhythm. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for determining the priority of programs based on the user's lifestyle rhythm.

[0096] When generating a health program, the generation unit can adjust the order of the programs based on the user's health goals. For example, if the user's health goal is weight loss, the generation unit can prioritize generating an exercise program. For example, if the user's health goal is muscle strengthening, the generation unit can prioritize generating a strength training program. For example, if the user's health goal is stress reduction, the generation unit can prioritize generating a relaxation program. This allows for adjusting the order of the programs based on the user's health goals, thereby providing a more appropriate program. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the order of the programs based on the user's health goals.

[0097] When generating a health program, the generation unit can adjust the use of technical terminology in the program according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a health program using detailed technical terminology. For example, if the user does not have technical expertise, the generation unit can generate a health program explained in simple terms. For example, the generation unit can adjust the way the program is expressed according to the user's level of expertise. This allows for a more understandable program to be provided by adjusting the use of technical terminology in the program according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the use of technical terminology in the program according to the user's level of expertise.

[0098] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide simple, highly visible advice. For example, when the user is relaxed, the providing unit can provide detailed advice. For example, when the user is in a hurry, the providing unit can provide advice that focuses on the main points. This allows for adjusting the way in which advice is presented based on the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can provide advice using an AI model for estimating the user's emotions and adjusting the way in which advice is presented based on the estimated user's emotions.

[0099] When providing advice, the providing unit can adjust the level of detail of the advice based on the user's health condition. For example, when the user's health condition is good, the providing unit can provide detailed advice. For example, when the user's health condition is poor, the providing unit can provide simplified advice. For example, the providing unit can adjust the level of detail of the advice according to the user's health condition. As a result, more appropriate advice can be provided by adjusting the level of detail of the advice based on the user's health condition. 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 provide advice using an AI model for adjusting the level of detail of the advice based on the user's health condition.

[0100] The providing unit can apply different advice algorithms depending on the user's lifestyle habits when providing advice. The providing unit can apply, for example, an algorithm for providing dietary advice based on the user's eating habits. The providing unit can apply, for example, an algorithm for providing exercise advice based on the user's exercise habits. The providing unit can apply, for example, an algorithm for providing sleep advice based on the user's sleeping habits. In this way, by applying different advice algorithms depending on the user's lifestyle habits, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide advice using an AI model for applying different advice algorithms depending on the user's lifestyle habits.

[0101] When providing advice, the providing unit can improve the accuracy of the advice provided by referring to the user's past advice results. The providing unit, for example, adjusts an advice algorithm based on the user's past advice results. The providing unit, for example, can set parameters for improving the accuracy of the advice provided by referring to the user's past advice results. The providing unit, for example, can determine the priority of advice by referring to the user's past advice results. This can improve the accuracy of the advice provided by referring to the user's past advice results. 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 provide advice by using an AI model for improving the accuracy of the advice provided by referring to the user's past advice results.

[0102] The providing unit can estimate the user's emotions and adjust the length of the advice to be provided based on the estimated user's emotions. For example, when the user is stressed, the providing unit can provide short, to-the-point advice. For example, when the user is relaxed, the providing unit can provide longer advice with detailed explanations. For example, when the user is in a hurry, the providing unit can provide concise, quickly actionable advice. This allows for more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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, an AI, or can be performed without using an AI. For example, the providing unit can provide advice using an AI model for estimating the user's emotions and adjusting the length of the advice to be provided based on the estimated user's emotions.

[0103] When providing advice, the providing unit can determine the priority of advice based on the user's lifestyle rhythm. For example, the providing unit can prioritize advice to be performed in the morning based on the user's lifestyle rhythm. For example, the providing unit can prioritize advice to be performed in the evening based on the user's lifestyle rhythm. For example, the providing unit can prioritize advice to be performed on weekends according to the user's lifestyle rhythm. In this way, by determining the priority of advice based on the user's lifestyle rhythm, more appropriate advice can be provided. 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 provide advice using an AI model for determining the priority of advice based on the user's lifestyle rhythm.

[0104] When providing advice, the providing unit can adjust the order of advice based on the user's health goal. For example, if the user's health goal is weight loss, the providing unit can prioritize providing exercise advice. For example, if the user's health goal is muscle strength building, the providing unit can prioritize providing strength training advice. For example, if the user's health goal is stress reduction, the providing unit can prioritize providing relaxation advice. In this way, by adjusting the order of advice based on the user's health goal, more appropriate advice can be provided. 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 provide advice using an AI model for adjusting the order of advice based on the user's health goal.

[0105] When providing 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 can provide advice using detailed technical terms. For example, if the user does not have technical expertise, the providing unit can provide advice explained in simple language. For example, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. This makes it possible to provide advice that is easier to understand by adjusting 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, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model for adjusting the use of technical terms in the advice according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation 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 collection unit collects health information and personal information on the Web using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a health program using a generation AI. The provision unit provides the generated health program to a user, for example, by the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation 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 collection unit collects health information and personal information on the Web using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a health program using a generation AI. The provision unit provides the generated health program to the user, for example, by the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation 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 collection unit collects health information and personal information on the Web using the camera 42 and communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a health program using a generation AI. The provision unit provides the generated health program to the user, for example, by using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation 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 collection unit collects health information and personal information on the Web using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a health program using a generation AI. The provision unit provides the generated health program to the user, for example, by the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, information related to stress reduction can be prioritized. If the user is relaxed, information related to maintaining health can be prioritized. If the user is in a hurry, information that can be obtained in a short time can be prioritized. This allows more appropriate information to be collected by prioritizing the information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI or without AI. For example, the collection unit can collect information using an AI model for estimating the user's emotions and prioritizing the information to be collected based on the estimated user emotions.

[0108] 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 stressed, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a summary analysis result can be provided. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can perform analysis using an AI model for estimating the user's emotions and adjusting the presentation method of the analysis based on the estimated user emotions.

[0109] The generation unit can estimate the user's emotions and adjust the presentation of the generated health program based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible health program can be generated. If the user is relaxed, a detailed health program can be generated. If the user is in a hurry, a health program that focuses on the key points can be generated. This allows for adjusting the presentation of the health program based on the user's emotions, thereby providing a more appropriate program. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can generate a program using an AI model for estimating the user's emotions and adjusting the presentation of the generated health program based on the estimated user emotions.

[0110] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, simple, highly visible advice can be provided. If the user is relaxed, detailed advice can be provided. If the user is in a hurry, advice that focuses on the main points can be provided. This allows more appropriate advice to be provided by adjusting the way in which advice is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model for estimating the user's emotions and adjusting the way in which advice is presented based on the estimated user's emotions.

[0111] The providing unit can estimate the user's emotions and adjust the length of the advice to be provided based on the estimated user emotions. For example, if the user is feeling stressed, short, to-the-point advice can be provided. If the user is relaxed, longer advice with detailed explanations can be provided. If the user is in a hurry, concise, quickly actionable advice can be provided. This allows for more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function 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 providing unit can be performed using AI or without AI. For example, the providing unit can provide advice using an AI model for estimating the user's emotions and adjusting the length of the advice to be provided based on the estimated user emotions.

[0112] The collection unit can analyze the user's past health data and select an appropriate collection method. For example, the collection unit can select the most effective information collection method from the user's past health data. The collection frequency can be adjusted based on the user's past health data. The collection unit can analyze the user's past health data and collect information with an emphasis on specific health indicators. This allows the optimal collection method to be selected by analyzing the user's past health data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the user's past health data and collect information using an AI model to select an appropriate collection method.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health information. For example, a detailed analysis can be performed for health information with high importance. A simplified analysis can be performed for health information with low importance. The priority of the analysis can be determined according to the importance of the health information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health information. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for adjusting the level of detail of the analysis based on the importance of the health information.

[0114] When generating a health program, the generation unit can adjust the level of detail of the program based on the user's health condition. For example, if the user's health condition is good, a detailed health program can be generated. If the user's health condition is poor, a simplified health program can be generated. The level of detail of the program can be adjusted according to the user's health condition. This allows for a more appropriate health program to be provided by adjusting the level of detail of the program based on the user's health condition. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can generate a program using an AI model for adjusting the level of detail of the program based on the user's health condition.

[0115] When providing advice, the providing unit can adjust the level of detail of the advice based on the user's health condition. For example, if the user's health condition is good, detailed advice can be provided. If the user's health condition is poor, simplified advice can be provided. The level of detail of the advice can be adjusted according to the user's health condition. In this way, by adjusting the level of detail of the advice based on the user's health condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can provide advice using an AI model for adjusting the level of detail of the advice based on the user's health condition.

[0116] When providing advice, the providing unit can determine the priority of advice based on the user's lifestyle rhythm. For example, based on the user's lifestyle rhythm, advice to be performed in the morning hours can be provided preferentially. Based on the user's lifestyle rhythm, advice to be performed in the evening hours can be provided preferentially. Advice to be performed on weekends can be provided preferentially according to the user's lifestyle rhythm. In this way, by determining the priority of advice based on the user's lifestyle rhythm, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model for determining the priority of advice based on the user's lifestyle rhythm.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects health-related information. For example, the collection unit collects health information from the web. The collection unit mainly collects information from reliable medical institutions and research institutes, allowing it to obtain the latest health information. The collection unit can also collect personal information from social media, email, and smartphone sensors. For example, it collects data such as photos of meals and exercise records posted by users on social media, as well as data detected by smartphone sensors, such as the number of steps taken and sleep time. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the user's lifestyle and health condition based on the collected information using techniques such as data mining, statistical analysis, and machine learning. Step 3: The generation unit generates an optimal health program for each individual user based on the results of the analysis by the analysis unit. The generation unit uses the generation AI to generate a customized health program based on the user's lifestyle and health condition. For example, the generation AI can analyze the user's food records and suggest a nutritionally balanced meal plan. The generation AI can also suggest an appropriate exercise program based on the user's exercise records. Furthermore, the generation AI can analyze the user's sleep data and suggest improvements to the sleep environment. Step 4: The providing unit provides the health program generated by the generating unit. The providing unit provides specific advice based on the generated health program using means such as app notifications, emails, websites, etc.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects health-related information; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a health program appropriate for each individual user based on the results of the analysis by the analysis unit; a providing unit that provides the health program generated by the generating unit. A system characterized by:

2. The collecting unit Collecting health information on the web 2. The system of claim 1.

3. The collecting unit Collecting personal information from social media, email, or smartphone sensors 2. The system of claim 1.

4. The analysis unit Analyze the user's lifestyle and health status based on the collected information 2. The system of claim 1.

5. The generation unit Generate a customized health program based on the user's lifestyle and health status 2. The system of claim 1.

6. The providing unit Providing specific advice based on the generated health program 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

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

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