system

Abstract

The system according to the embodiment aims to provide an optimal health management plan based on an individual's physique and genetic information. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit and generates a health management plan based on the individual's physique and genetic information. The provision unit provides the user with the health management plan generated by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately verify whether health management is actually being carried out numerically, nor does it provide flexible responses based on an individual's physique and genetic information, so there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal health management plan based on an individual's physique and genetic information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit and generates a health management plan based on the individual's physique and genetic information. The provision unit provides the user with the health management plan generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal health management plan based on an individual's physique and genetic information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention utilizes a generative AI to propose optimal health management for an individual. This health management system collects a user's health data, analyzes it using a generative AI, and generates an optimal health management plan based on the individual's physique and genetic information, providing the plan to the user. For example, detailed data such as the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood sugar level is collected. The user can use a smartphone or wearable device to record their daily exercise volume and diet, allowing for a detailed understanding of the user's health status. The generative AI then analyzes the collected data. Based on the collected data, the generative AI generates an optimal health management plan that takes into account the user's physique and genetic information. For example, the generative AI can analyze the user's genetic information to assess their risk of specific diseases. It can also propose appropriate exercise and meal plans based on the user's exercise habits and diet. The generated health management plan is provided to the user. The user can manage their daily health according to the plan proposed by the generative AI. For example, the user can exercise daily according to the exercise plan proposed by the generative AI. In addition, users can eat a balanced diet according to the meal plan suggested by the generative AI. This system allows users to optimally manage their health based on their individual physique and genetic information. This is expected to improve the effectiveness of health management and the user's health condition. This allows the health management system to efficiently collect, analyze, and provide users' health data.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects a user's health data. The user's health data may include, but is not limited to, weight, height, age, gender, genetic information, exercise habits, dietary habits, blood pressure, and blood glucose levels. The collection unit may record the user's daily exercise and dietary habits using, for example, a smartphone or a wearable device. For example, the collection unit may collect data entered by the user through a smartphone app. The collection unit may also automatically acquire data from a wearable device. For example, the collection unit may acquire data such as heart rate and step count from a smartwatch. The collection unit may also use a DNA analysis kit to acquire the user's genetic information. For example, the collection unit may analyze a saliva sample provided by the user to acquire the genetic information. The analysis unit analyzes the data collected by the collection unit and generates an optimal health management plan based on the user's physique and genetic information. The analysis unit may evaluate the user's health risks based on the collected data, for example, using a generation AI. For example, the analysis unit allows the generation AI to analyze the user's genetic information and evaluate the user's risk of a specific disease. The analysis unit can also suggest appropriate exercise plans and meal plans based on the user's exercise habits and dietary content. For example, the analysis unit allows the generation AI to analyze the user's exercise data and generate an optimal exercise plan. The provision unit provides the user with the health management plan generated by the analysis unit. The provision unit provides the user with the generated plan, for example, via a smartphone app or email. For example, the provision unit notifies the user of the exercise plan or meal plan via a smartphone app. The provision unit can also send the generated plan to the user via email. For example, the provision unit sends the health management plan to the user's email address. This allows the health management system according to the embodiment to efficiently collect, analyze, and provide the user's health data.

[0030] The collection unit can collect data on the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood glucose level. The collection unit collects, for example, data such as the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood glucose level. For example, the collection unit collects data entered by the user using a smartphone app. The collection unit can also automatically acquire data from a wearable device. For example, the collection unit acquires data such as heart rate and step count from a smartwatch. Furthermore, the collection unit can use a DNA analysis kit to acquire the user's genetic information. For example, the collection unit analyzes a saliva sample provided by the user to acquire genetic information. This allows for the collection of detailed health data, enabling the generation of a more accurate health management plan.

[0031] The collection unit can acquire data from a smartphone or a wearable device. The collection unit acquires data from, for example, a smartphone or a wearable device. For example, the collection unit collects data entered by a user through a smartphone app. The collection unit can also automatically acquire data from a wearable device. For example, the collection unit acquires data such as heart rate and step count from a smartwatch. In this way, by acquiring data from a smartphone or a wearable device, it is possible to collect user health data in real time.

[0032] The analysis unit can generate a health management plan that takes into account the user's physique and genetic information based on the collected data. For example, the analysis unit generates a health management plan that takes into account the user's physique and genetic information based on the collected data. For example, the analysis unit uses a generation AI to assess the user's health risk based on the collected data. For example, the analysis unit uses a generation AI to analyze the user's genetic information and assess the risk of a specific disease. The analysis unit can also suggest appropriate exercise plans and meal plans based on the user's exercise habits and dietary content. For example, the analysis unit uses a generation AI to analyze the user's exercise data and generate an optimal exercise plan. This enables personalized health management by generating a health management plan that takes into account the user's physique and genetic information.

[0033] The analysis unit can analyze the data using a machine learning algorithm to assess the user's health risk. The analysis unit can, for example, analyze the data using a machine learning algorithm to assess the user's health risk. For example, the analysis unit uses a generation AI to analyze the user's genetic information and assess the risk of a specific disease. The analysis unit can also suggest appropriate exercise plans and meal plans based on the user's exercise habits and dietary content. For example, the analysis unit uses a generation AI to analyze the user's exercise data and generate an optimal exercise plan. In this way, the use of a machine learning algorithm can assess the user's health risk with high accuracy.

[0034] The providing unit can provide the generated health management plan to the user via a smartphone app or email. The providing unit, for example, provides the generated health management plan to the user via a smartphone app or email. For example, the providing unit notifies the user of an exercise plan or a meal plan via a smartphone app. The providing unit can also send the generated plan to the user via email. For example, the providing unit sends the health management plan to the user's email address. In this way, the health management plan can be provided to the user via a smartphone app or email, allowing the user to easily access it.

[0035] The providing unit can provide an interface for the user to perform health management according to the generated plan. The providing unit provides, for example, an interface for the user to perform health management according to the generated plan. For example, the providing unit provides an interface for the user to check and execute an exercise plan or a meal plan through a smartphone app. The providing unit can also provide an interface for the user to perform health management according to the generated plan through a web application. For example, the providing unit provides an interface for the user to check and execute a health management plan through a web browser. This makes it easier for the user to perform health management by providing an interface for the user to perform health management according to the generated plan.

[0036] The collection unit can analyze the user's past health data and select a collection method. For example, the collection unit analyzes the user's past health data and selects the optimal collection method. For example, the collection unit can suggest the optimal collection method based on devices and apps the user has used in the past. The collection unit can also analyze the user's past data collection frequency and set the optimal collection interval. The collection unit can also select the most efficient collection method by referring to the user's past data collection method. For example, the collection unit can analyze data from fitness trackers and health management apps the user has used in the past and suggest the optimal devices and apps. In this way, the optimal collection method can be selected by analyzing past data, enabling efficient data collection.

[0037] The collection unit can filter the health data based on the user's current living situation and areas of interest when collecting the health data. For example, the collection unit filters the health data based on the user's current living situation and areas of interest when collecting the health data. For example, if the user is on a diet, the collection unit prioritizes collecting data related to dietary content. Furthermore, if the user is interested in exercise, the collection unit can also collect data related to the amount and type of exercise. Furthermore, if the user has a specific health problem, the collection unit can prioritize collecting data related to the problem. For example, if the user has diabetes, the collection unit prioritizes collecting data related to blood sugar levels. In this way, by filtering data based on the user's living situation and areas of interest, more relevant data can be collected.

[0038] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting health data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a high altitude, the collection unit collects health data related to altitude. Furthermore, when the user is in an urban area, the collection unit can also collect data related to air quality and noise levels. Furthermore, when the user is traveling, the collection unit can collect health data related to the environment of the travel destination. For example, the collection unit collects data related to the user's diet and exercise volume at the travel destination. In this way, by taking into account the user's geographical location information, appropriate data can be collected according to the environment.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting health data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting health data. For example, if the user posts about exercise on social media, the collection unit prioritizes collecting exercise data. Furthermore, if the user posts about diet, the collection unit can also collect data about diet content. Furthermore, if the user posts about stress, the collection unit can also collect stress-related data. For example, the collection unit analyzes the content of the user's posts on social media and collects related health data. In this way, data collection based on the user's interests becomes possible by analyzing social media activities.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit performs a detailed analysis of important health data. The analysis unit can also perform a concise analysis of general health data. The analysis unit can also perform a particularly detailed analysis of data that is of great interest to the user. In this way, adjusting the level of detail of the analysis based on the importance of the health data enables efficient data analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit applies an exercise pattern analysis algorithm to exercise data. The analysis unit can also apply a nutritional balance analysis algorithm to dietary data. The analysis unit can also apply a genetic risk analysis algorithm to genetic information. In this way, applying an analysis algorithm depending on the category of health data enables more accurate analysis.

[0042] The analysis unit can determine the priority of analysis based on when the health data was collected during analysis. For example, the analysis unit determines the priority of analysis based on when the health data was collected during analysis. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also refer to past data while placing emphasis on the most recent data. The analysis unit can also focus on analyzing data collected during a specific period. In this way, by determining the priority of analysis based on when the health data was collected, it becomes possible to perform analysis that places emphasis on the most recent data.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the health data.

[0044] The providing unit can adjust the level of detail of the health management plan provided based on the importance of the health management plan at the time of provision. For example, the providing unit adjusts the level of detail of the health management plan provided based on the importance of the health management plan at the time of provision. For example, the providing unit provides a detailed explanation for an important health management plan. The providing unit can also provide a concise explanation for a general health management plan. The providing unit can also provide a particularly detailed explanation for a plan in which the user is highly interested. In this way, adjusting the level of detail of the health management plan based on the importance of the health management plan enables efficient plan provision.

[0045] The providing unit can apply different providing algorithms depending on the category of the health management plan when providing the plan. For example, the providing unit applies different providing algorithms depending on the category of the health management plan when providing the plan. For example, the providing unit applies a providing algorithm depending on the type and frequency of exercise to an exercise plan. The providing unit can also apply a providing algorithm depending on nutritional balance and calories to a meal plan. The providing unit can also apply a providing algorithm depending on the type and degree of risk to a genetic risk plan. In this way, by applying a providing algorithm depending on the category of the health management plan, it is possible to provide a more accurate plan.

[0046] The providing unit can determine the priority of provision based on the time of creation of the health management plan at the time of provision. For example, the providing unit determines the priority of provision based on the time of creation of the health management plan at the time of provision. For example, the providing unit prioritizes providing the most recently created plan. The providing unit can also refer to past plans while placing emphasis on the most recent plan. The providing unit can also provide plans created within a specific period with emphasis on providing. In this way, by determining the priority of provision based on the time of creation of the health management plan, it is possible to provide the most recent plan with emphasis.

[0047] The providing unit can adjust the order of provision based on the relevance of the health management plans at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of the health management plans at the time of provision. For example, the providing unit provides highly relevant plans preferentially. The providing unit can also postpone less relevant plans. The providing unit can also dynamically adjust the order of provision according to the relevance of the plans. This enables efficient plan provision by adjusting the order of provision based on the relevance of the health management plans.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The health management system can further collect and analyze the user's sleep data. For example, the collection unit can collect data on the user's sleep patterns and sleep quality through a smartwatch or smartphone app. The analysis unit can evaluate the user's sleep quality based on the collected sleep data and provide advice for improvement. The provision unit can propose an appropriate sleep plan to the user based on the analysis results. This allows the user to receive specific advice on how to get better sleep as part of their health management.

[0050] The health management system can further monitor and analyze the user's stress level. For example, the collection unit can collect data such as the user's heart rate variability and electrodermal activity through a smartwatch or stress monitoring device. The analysis unit can evaluate the user's stress level based on the collected data and provide advice for stress reduction. The provision unit can suggest relaxation methods and stress management plans to the user based on the analysis results. This allows the user to receive specific advice for effectively managing stress in their daily lives.

[0051] The health management system can further monitor and analyze the user's water intake. For example, the collection unit can collect data on the user's water intake through a smart watch or smart bottle. The analysis unit can evaluate the user's water intake status based on the collected data and suggest an appropriate amount of water intake. The provision unit can suggest a water intake plan to the user based on the analysis results. This allows the user to receive specific advice on maintaining appropriate water intake.

[0052] The health management system can further collect and analyze the user's mental health data. For example, the collection unit can collect data on the user's mental health through a smartphone app or an online questionnaire. The analysis unit can evaluate the user's mental health state based on the collected data and provide advice for improvement. The provision unit can propose a mental health care plan to the user based on the analysis results. This allows the user to receive specific advice for maintaining and improving their mental health.

[0053] The health management system can further analyze the nutritional balance of the user's diet and make suggestions for improvement. For example, the collection unit can record the details of the meals the user has eaten through a smartphone app. The analysis unit can evaluate the user's nutritional balance based on the collected dietary data and suggest the amount of nutrients they need to consume. The provision unit can suggest a balanced meal plan to the user based on the analysis results. This allows the user to receive specific advice on maintaining a healthy diet.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects the user's health data. The user's health data includes weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, blood sugar levels, etc. The collection unit uses a smartphone or wearable device to record the amount of exercise and dietary information the user has daily. For example, the collection unit collects data entered by the user through a smartphone app and automatically obtains data such as heart rate and number of steps from the wearable device. The collection unit can also obtain the user's genetic information using a DNA analysis kit. Step 2: The analysis unit analyzes the data collected by the collection unit and generates an optimal health management plan based on the individual's physique and genetic information. The analysis unit uses the generation AI to assess the user's health risks based on the collected data and evaluates the risk of specific diseases. It also suggests appropriate exercise and meal plans based on the user's exercise habits and dietary habits. Step 3: The provision unit provides the user with the health management plan generated by the analysis unit. The provision unit provides the user with the generated plan via a smartphone app or email. For example, the provision unit notifies the user of an exercise plan or meal plan via a smartphone app and sends the user the health management plan via email.

[0056] (Example 2) A health management system according to an embodiment of the present invention utilizes a generative AI to propose optimal health management for an individual. This health management system collects a user's health data, analyzes it using a generative AI, and generates an optimal health management plan based on the individual's physique and genetic information, providing the plan to the user. For example, detailed data such as the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood sugar level is collected. The user can use a smartphone or wearable device to record their daily exercise volume and diet, allowing for a detailed understanding of the user's health status. The generative AI then analyzes the collected data. Based on the collected data, the generative AI generates an optimal health management plan that takes into account the user's physique and genetic information. For example, the generative AI can analyze the user's genetic information to assess their risk of specific diseases. It can also propose appropriate exercise and meal plans based on the user's exercise habits and diet. The generated health management plan is provided to the user. The user can manage their daily health according to the plan proposed by the generative AI. For example, the user can exercise daily according to the exercise plan proposed by the generative AI. In addition, users can eat a balanced diet according to the meal plan suggested by the generative AI. This system allows users to optimally manage their health based on their individual physique and genetic information. This is expected to improve the effectiveness of health management and the user's health condition. This allows the health management system to efficiently collect, analyze, and provide users' health data.

[0057] A health management system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects a user's health data. The user's health data may include, but is not limited to, weight, height, age, gender, genetic information, exercise habits, dietary habits, blood pressure, and blood glucose levels. The collection unit may record the user's daily exercise and dietary habits using, for example, a smartphone or a wearable device. For example, the collection unit may collect data entered by the user through a smartphone app. The collection unit may also automatically acquire data from a wearable device. For example, the collection unit may acquire data such as heart rate and step count from a smartwatch. The collection unit may also use a DNA analysis kit to acquire the user's genetic information. For example, the collection unit may analyze a saliva sample provided by the user to acquire the genetic information. The analysis unit analyzes the data collected by the collection unit and generates an optimal health management plan based on the user's physique and genetic information. The analysis unit may evaluate the user's health risks based on the collected data, for example, using a generation AI. For example, the analysis unit allows the generation AI to analyze the user's genetic information and evaluate the user's risk of a specific disease. The analysis unit can also suggest appropriate exercise plans and meal plans based on the user's exercise habits and dietary content. For example, the analysis unit allows the generation AI to analyze the user's exercise data and generate an optimal exercise plan. The provision unit provides the user with the health management plan generated by the analysis unit. The provision unit provides the user with the generated plan, for example, via a smartphone app or email. For example, the provision unit notifies the user of the exercise plan or meal plan via a smartphone app. The provision unit can also send the generated plan to the user via email. For example, the provision unit sends the health management plan to the user's email address. This allows the health management system according to the embodiment to efficiently collect, analyze, and provide the user's health data.

[0058] The collection unit can collect data on the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood glucose level. The collection unit collects, for example, data such as the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood glucose level. For example, the collection unit collects data entered by the user using a smartphone app. The collection unit can also automatically acquire data from a wearable device. For example, the collection unit acquires data such as heart rate and step count from a smartwatch. Furthermore, the collection unit can use a DNA analysis kit to acquire the user's genetic information. For example, the collection unit analyzes a saliva sample provided by the user to acquire genetic information. This allows for the collection of detailed health data, enabling the generation of a more accurate health management plan.

[0059] The collection unit can acquire data from a smartphone or a wearable device. The collection unit acquires data from, for example, a smartphone or a wearable device. For example, the collection unit collects data entered by a user through a smartphone app. The collection unit can also automatically acquire data from a wearable device. For example, the collection unit acquires data such as heart rate and step count from a smartwatch. In this way, by acquiring data from a smartphone or a wearable device, it is possible to collect user health data in real time.

[0060] The analysis unit can generate a health management plan that takes into account the user's physique and genetic information based on the collected data. For example, the analysis unit generates a health management plan that takes into account the user's physique and genetic information based on the collected data. For example, the analysis unit uses a generation AI to assess the user's health risk based on the collected data. For example, the analysis unit uses a generation AI to analyze the user's genetic information and assess the risk of a specific disease. The analysis unit can also suggest appropriate exercise plans and meal plans based on the user's exercise habits and dietary content. For example, the analysis unit uses a generation AI to analyze the user's exercise data and generate an optimal exercise plan. This enables personalized health management by generating a health management plan that takes into account the user's physique and genetic information.

[0061] The analysis unit can analyze the data using a machine learning algorithm to assess the user's health risk. The analysis unit can, for example, analyze the data using a machine learning algorithm to assess the user's health risk. For example, the analysis unit uses a generation AI to analyze the user's genetic information and assess the risk of a specific disease. The analysis unit can also suggest appropriate exercise plans and meal plans based on the user's exercise habits and dietary content. For example, the analysis unit uses a generation AI to analyze the user's exercise data and generate an optimal exercise plan. In this way, the use of a machine learning algorithm can assess the user's health risk with high accuracy.

[0062] The providing unit can provide the generated health management plan to the user via a smartphone app or email. The providing unit, for example, provides the generated health management plan to the user via a smartphone app or email. For example, the providing unit notifies the user of an exercise plan or a meal plan via a smartphone app. The providing unit can also send the generated plan to the user via email. For example, the providing unit sends the health management plan to the user's email address. In this way, the health management plan can be provided to the user via a smartphone app or email, allowing the user to easily access it.

[0063] The providing unit can provide an interface for the user to perform health management according to the generated plan. The providing unit provides, for example, an interface for the user to perform health management according to the generated plan. For example, the providing unit provides an interface for the user to check and execute an exercise plan or a meal plan through a smartphone app. The providing unit can also provide an interface for the user to perform health management according to the generated plan through a web application. For example, the providing unit provides an interface for the user to check and execute a health management plan through a web browser. This makes it easier for the user to perform health management by providing an interface for the user to perform health management according to the generated plan.

[0064] The collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection timing to reduce the burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing to quickly acquire data. This reduces the burden on the user by adjusting the collection timing according to the user's emotions and enables more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The collection unit can analyze the user's past health data and select a collection method. For example, the collection unit analyzes the user's past health data and selects the optimal collection method. For example, the collection unit can suggest the optimal collection method based on devices and apps the user has used in the past. The collection unit can also analyze the user's past data collection frequency and set the optimal collection interval. The collection unit can also select the most efficient collection method by referring to the user's past data collection method. For example, the collection unit can analyze data from fitness trackers and health management apps the user has used in the past and suggest the optimal devices and apps. In this way, the optimal collection method can be selected by analyzing past data, enabling efficient data collection.

[0066] The collection unit can filter the health data based on the user's current living situation and areas of interest when collecting the health data. For example, the collection unit filters the health data based on the user's current living situation and areas of interest when collecting the health data. For example, if the user is on a diet, the collection unit prioritizes collecting data related to dietary content. Furthermore, if the user is interested in exercise, the collection unit can also collect data related to the amount and type of exercise. Furthermore, if the user has a specific health problem, the collection unit can prioritize collecting data related to the problem. For example, if the user has diabetes, the collection unit prioritizes collecting data related to blood sugar levels. In this way, by filtering data based on the user's living situation and areas of interest, more relevant data can be collected.

[0067] The collection unit can estimate the user's emotions and determine the priority of the health data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the health data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. Also, if the user is relaxed, the collection unit can collect overall health data in a balanced manner. Also, if the user is in a hurry, the collection unit can prioritize collecting only important data. In this way, by determining the priority of data according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting health data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a high altitude, the collection unit collects health data related to altitude. Furthermore, when the user is in an urban area, the collection unit can also collect data related to air quality and noise levels. Furthermore, when the user is traveling, the collection unit can collect health data related to the environment of the travel destination. For example, the collection unit collects data related to the user's diet and exercise volume at the travel destination. In this way, by taking into account the user's geographical location information, appropriate data can be collected according to the environment.

[0069] The collection unit can analyze the user's social media activities and collect related data when collecting health data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting health data. For example, if the user posts about exercise on social media, the collection unit prioritizes collecting exercise data. Furthermore, if the user posts about diet, the collection unit can also collect data about diet content. Furthermore, if the user posts about stress, the collection unit can also collect stress-related data. For example, the collection unit analyzes the content of the user's posts on social media and collects related health data. In this way, data collection based on the user's interests becomes possible by analyzing social media activities.

[0070] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. The analysis unit can also provide analysis results in a format that is quickly understandable when the user is in a hurry. In this way, by adjusting the way the analysis is presented according to the user's emotion, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the health data during analysis. For example, the analysis unit performs a detailed analysis of important health data. The analysis unit can also perform a concise analysis of general health data. The analysis unit can also perform a particularly detailed analysis of data that is of great interest to the user. In this way, adjusting the level of detail of the analysis based on the importance of the health data enables efficient data analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit applies an exercise pattern analysis algorithm to exercise data. The analysis unit can also apply a nutritional balance analysis algorithm to dietary data. The analysis unit can also apply a genetic risk analysis algorithm to genetic information. In this way, applying an analysis algorithm depending on the category of health data enables more accurate analysis.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. The analysis unit can also provide analysis results in a format that is quickly understandable when the user is in a hurry. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing appropriate analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The analysis unit can determine the priority of analysis based on when the health data was collected during analysis. For example, the analysis unit determines the priority of analysis based on when the health data was collected during analysis. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also refer to past data while placing emphasis on the most recent data. The analysis unit can also focus on analyzing data collected during a specific period. In this way, by determining the priority of analysis based on when the health data was collected, it becomes possible to perform analysis that places emphasis on the most recent data.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the health data.

[0076] The providing unit can estimate the user's emotions and adjust the presentation method of the plan to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the presentation method of the plan to be provided based on the estimated user emotions. For example, the providing unit can provide a detailed plan when the user is relaxed. The providing unit can also provide a concise and to-the-point plan when the user is stressed. The providing unit can also provide a plan in a format that can be quickly understood when the user is in a hurry. In this way, by adjusting the presentation method of the plan according to the user's emotions, it is possible to provide a plan that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function using, 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.

[0077] The providing unit can adjust the level of detail of the health management plan provided based on the importance of the health management plan at the time of provision. For example, the providing unit adjusts the level of detail of the health management plan provided based on the importance of the health management plan at the time of provision. For example, the providing unit provides a detailed explanation for an important health management plan. The providing unit can also provide a concise explanation for a general health management plan. The providing unit can also provide a particularly detailed explanation for a plan in which the user is highly interested. In this way, adjusting the level of detail of the health management plan based on the importance of the health management plan enables efficient plan provision.

[0078] The providing unit can apply different providing algorithms depending on the category of the health management plan when providing the plan. For example, the providing unit applies different providing algorithms depending on the category of the health management plan when providing the plan. For example, the providing unit applies a providing algorithm depending on the type and frequency of exercise to an exercise plan. The providing unit can also apply a providing algorithm depending on nutritional balance and calories to a meal plan. The providing unit can also apply a providing algorithm depending on the type and degree of risk to a genetic risk plan. In this way, by applying a providing algorithm depending on the category of the health management plan, it is possible to provide a more accurate plan.

[0079] The providing unit can estimate the user's emotions and adjust the length of the plan to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the length of the plan to be provided based on the estimated user emotions. For example, the providing unit can provide a detailed plan when the user is relaxed. The providing unit can also provide a concise and to-the-point plan when the user is stressed. The providing unit can also provide a plan in a format that can be quickly understood when the user is in a hurry. In this way, by adjusting the length of the plan according to the user's emotions, it is possible to provide an appropriate plan for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The providing unit can determine the priority of provision based on the time of creation of the health management plan at the time of provision. For example, the providing unit determines the priority of provision based on the time of creation of the health management plan at the time of provision. For example, the providing unit prioritizes providing the most recently created plan. The providing unit can also refer to past plans while placing emphasis on the most recent plan. The providing unit can also provide plans created within a specific period with emphasis on providing. In this way, by determining the priority of provision based on the time of creation of the health management plan, it is possible to provide the most recent plan with emphasis.

[0081] The providing unit can adjust the order of provision based on the relevance of the health management plans at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of the health management plans at the time of provision. For example, the providing unit provides highly relevant plans preferentially. The providing unit can also postpone less relevant plans. The providing unit can also dynamically adjust the order of provision according to the relevance of the plans. This enables efficient plan provision by adjusting the order of provision based on the relevance of the health management plans. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis 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 the user's health data using the camera 42 and microphone 38B of the smart device 14 and records the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generation AI, and generates an optimal health management plan. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated health management plan to the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and provision unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis 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 the user's health data using the camera 42 and microphone 238 of the smart glasses 214 and records the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generation AI, and generates an optimal health management plan. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated health management plan to the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and provision unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis 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 the user's health data using the camera 42 and microphone 238 of the headset-type terminal 314 and records the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI to generate an optimal health management plan. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated health management plan to the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and provision unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis 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 data of the user using the camera 42 and microphone 238 of the robot 414 and records the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generation AI, and generates an optimal health management plan. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated health management plan to the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and provision unit may be realized, for example, by the control unit 46A of the robot 414.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The health management system can further collect and analyze the user's sleep data. For example, the collection unit can collect data on the user's sleep patterns and sleep quality through a smartwatch or smartphone app. The analysis unit can evaluate the user's sleep quality based on the collected sleep data and provide advice for improvement. The provision unit can propose an appropriate sleep plan to the user based on the analysis results. This allows the user to receive specific advice on how to get better sleep as part of their health management.

[0084] The health management system can further monitor and analyze the user's stress level. For example, the collection unit can collect data such as the user's heart rate variability and electrodermal activity through a smartwatch or stress monitoring device. The analysis unit can evaluate the user's stress level based on the collected data and provide advice for stress reduction. The provision unit can suggest relaxation methods and stress management plans to the user based on the analysis results. This allows the user to receive specific advice for effectively managing stress in their daily lives.

[0085] The health management system can further monitor and analyze the user's water intake. For example, the collection unit can collect data on the user's water intake through a smart watch or smart bottle. The analysis unit can evaluate the user's water intake status based on the collected data and suggest an appropriate amount of water intake. The provision unit can suggest a water intake plan to the user based on the analysis results. This allows the user to receive specific advice on maintaining appropriate water intake.

[0086] The health management system can further collect and analyze the user's mental health data. For example, the collection unit can collect data on the user's mental health through a smartphone app or an online questionnaire. The analysis unit can evaluate the user's mental health state based on the collected data and provide advice for improvement. The provision unit can propose a mental health care plan to the user based on the analysis results. This allows the user to receive specific advice for maintaining and improving their mental health.

[0087] The health management system can further analyze the nutritional balance of the user's diet and make suggestions for improvement. For example, the collection unit can record the details of the meals the user has eaten through a smartphone app. The analysis unit can evaluate the user's nutritional balance based on the collected dietary data and suggest the amount of nutrients they need to consume. The provision unit can suggest a balanced meal plan to the user based on the analysis results. This allows the user to receive specific advice on maintaining a healthy diet.

[0088] The health management system can estimate the user's emotions and adjust the exercise plan based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can suggest an exercise that has a relaxing effect. Also, if the user is relaxed, the analysis unit can suggest a more intense exercise. The provision unit can provide the user with an appropriate exercise plan based on the analysis results. This allows the user to carry out the optimal exercise plan according to their emotional state at that time.

[0089] The health management system can estimate the user's emotions and adjust the meal plan based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can suggest a meal that includes ingredients that have a relaxing effect. Also, if the user is relaxed, the analysis unit can suggest a nutritionally balanced meal. The provision unit can provide the user with an appropriate meal plan based on the analysis results. This allows the user to implement the optimal meal plan according to their emotional state at that time.

[0090] The health management system can estimate the user's emotions and adjust the timing of notification of the health management plan based on the estimated emotions. For example, the providing unit can reduce notifications when the user is feeling stressed and proactively notify the user when the user is relaxed. Also, if the user is in a hurry, the providing unit can prioritize only important notifications. This allows the health management plan to be notified at an appropriate time according to the user's emotional state.

[0091] The health management system can estimate the user's emotions and adjust the content of the health management plan based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can suggest a plan specialized in stress reduction. Alternatively, if the user is relaxed, the analysis unit can suggest a plan focused on maintaining overall health. The provision unit can provide the user with an appropriate health management plan based on the analysis results. This allows the user to implement the optimal health management plan according to their emotional state at that time.

[0092] The health management system can estimate the user's emotions and adjust the feedback method for the health management plan based on the estimated emotions. For example, the providing unit can provide concise and positive feedback when the user is feeling stressed, or provide detailed feedback when the user is relaxed. This makes it possible to provide appropriate feedback according to the user's emotional state.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The collection unit collects the user's health data. The user's health data includes weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, blood sugar levels, etc. The collection unit uses a smartphone or wearable device to record the amount of exercise and dietary information the user has daily. For example, the collection unit collects data entered by the user through a smartphone app and automatically obtains data such as heart rate and number of steps from the wearable device. The collection unit can also obtain the user's genetic information using a DNA analysis kit. Step 2: The analysis unit analyzes the data collected by the collection unit and generates an optimal health management plan based on the individual's physique and genetic information. The analysis unit uses the generation AI to assess the user's health risks based on the collected data and evaluates the risk of specific diseases. It also suggests appropriate exercise and meal plans based on the user's exercise habits and dietary habits. Step 3: The provision unit provides the user with the health management plan generated by the analysis unit. The provision unit provides the user with the generated plan via a smartphone app or email. For example, the provision unit notifies the user of an exercise plan or meal plan via a smartphone app and sends the user the health management plan via email.

[0095] 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.

[0096] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit and generates a health management plan based on the individual's physique and genetic information; a providing unit that provides a user with the health management plan generated by the analysis unit. A system characterized by:

2. The collecting unit Collects data on the user's weight, height, age, gender, genetic information, exercise habits, diet, blood pressure, and blood sugar levels 2. The system of claim 1.

3. The collecting unit Acquire data from smartphones and wearable devices 2. The system of claim 1.

4. The analysis unit Based on the collected data, a health management plan is generated that takes into account the user's physique and genetic information.

2. The system of claim 1.

5. The analysis unit Uses machine learning algorithms to analyze data and assess user health risks 2. The system of claim 1.

6. The providing unit The generated health management plan is provided to the user via a smartphone app or email.

2. The system of claim 1.

7. The providing unit Provides an interface for users to manage their health according to the generated plan 2. The system of claim 1.

8. The collecting unit The system estimates the user's emotions and adjusts the timing of health data collection based on the estimated user emotions.

2. The system of claim 1.

Images