system

Abstract

The system according to the embodiment aims to raise users' awareness of lifestyle-related diseases and promote health promotion activities. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects lifestyle and medical data of a user. The analysis unit analyzes the data collected by the collection unit and predicts the user's future health condition. The recommendation unit provides health recommendations to the user based on the analysis results obtained 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] With conventional techniques, there is a risk that users will not be aware of lifestyle-related diseases and will neglect to take measures against them.

[0005] The system according to the embodiment aims to raise users' awareness of lifestyle-related diseases and promote health promotion activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects lifestyle and medical data of a user. The analysis unit analyzes the data collected by the collection unit and predicts the user's future health condition. The recommendation unit provides health recommendations to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can raise the user's awareness of lifestyle-related diseases and promote health promotion activities. [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 recommendation system according to an embodiment of the present invention is a novel method for raising users' awareness of lifestyle-related diseases. It inputs the user's lifestyle habits and MRI and X-ray images generated during medical examinations into a large-scale language model (LLM) to visualize and recommend the user's future health status. This health recommendation system collects data related to the user's lifestyle habits (e.g., diet, exercise, sleep, etc.) and medical data such as MRI and X-ray images generated during medical examinations, and inputs the collected data into the LLM to predict the user's future health status. The LLM compares the data with past data to analyze how the user's lifestyle habits will affect their future health status. Based on the analysis results, the system provides specific health recommendations to the user. For example, the system indicates the risks of continuing the user's current lifestyle habits and areas for improvement. This allows the user to visualize their health status and raises their awareness of lifestyle-related diseases. This system not only raises the user's awareness of lifestyle-related diseases, but also promotes health promotion activities by suggesting specific improvement measures. For example, it provides specific action plans, such as recommendations for improving diet and exercise. This allows the user to proactively manage their health and prevent lifestyle-related diseases. This enables the health recommendation system to raise the user's awareness of lifestyle-related diseases and encourage health promotion activities.

[0029] A health recommendation system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects lifestyle and medical data of a user. The lifestyle of the user includes, but is not limited to, diet, exercise, and sleep. For example, the collection unit collects a diet record of the user. The collection unit can also collect an exercise record of the user. The collection unit can also collect a sleep pattern of the user. For example, the collection unit collects a diet record entered by the user using a smartphone app. The collection unit can also collect exercise data recorded by the user using a wearable device. The collection unit can also collect sleep data recorded by the user using a smartwatch. The analysis unit analyzes the data collected by the collection unit and predicts a future health condition. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, such examples. For example, the analysis unit performs statistical analysis on the collected data to predict a future health condition. The analysis unit can also analyze the data using a machine learning algorithm to predict a future health condition. The analysis unit can also compare the data with past data to analyze how the user's lifestyle habits will affect their future health condition. For example, the analysis unit can perform a risk assessment based on past data and predict their future health condition. The analysis unit can also calculate a health score and predict their future health condition. The recommendation unit provides health recommendations to the user based on the analysis results obtained by the analysis unit. Health recommendations include, but are not limited to, recommendations for improving diet and exercising. For example, the recommendation unit can recommend the user to improve their diet based on the analysis results. The recommendation unit can also recommend exercising based on the analysis results. The recommendation unit can also recommend changes to their lifestyle habits based on the analysis results. For example, the recommendation unit can provide the user with a specific action plan. This allows the health recommendation system according to the embodiment to raise users' awareness of lifestyle-related diseases and encourage them to engage in health promotion activities.

[0030] The collection unit can collect data related to the user's lifestyle habits. Examples of lifestyle-related data include, but are not limited to, meal records, exercise records, and sleep patterns. For example, the collection unit collects the user's meal records. For example, the collection unit collects meal records entered by the user using a smartphone app. The collection unit can also collect the user's exercise records. For example, the collection unit collects exercise data recorded by the user using a wearable device. The collection unit can also collect the user's sleep patterns. For example, the collection unit collects sleep data recorded by the user using a smartwatch. This allows efficient collection of data related to the user's lifestyle habits. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to the user's lifestyle habits into a generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect at least one medical data item, such as an MRI image or an X-ray image, created during a medical examination. Examples of medical data include, but are not limited to, MRI images, X-ray images, and blood test results. The collection unit collects, for example, MRI images created during a medical examination. For example, the collection unit digitally collects MRI images taken at a hospital. The collection unit can also collect X-ray images created during a medical examination. For example, the collection unit digitally collects X-ray images taken at a hospital. The collection unit can also collect medical data such as blood test results. For example, the collection unit digitally collects blood test results performed at a hospital. This allows for efficient collection of medical data created during a medical examination. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input MRI images or X-ray images created during a medical examination into a generation AI and have the generation AI analyze the image data.

[0032] The analysis unit can analyze the collected data and predict the future health state. Predictions of the future health state include, but are not limited to, risk assessments and health scores. For example, the analysis unit can perform statistical analysis on the collected data to predict the future health state. For example, the analysis unit can perform risk assessments on the collected data and predict the future health state. The analysis unit can also analyze the data using a machine learning algorithm to predict the future health state. For example, the analysis unit can calculate a health score on the collected data and predict the future health state. The analysis unit can also analyze how the user's lifestyle habits will affect the future health state by comparing it with past data. For example, the analysis unit can perform risk assessments on the past data and predict the future health state. This allows for accurate prediction of the future health state. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to predict the future health state.

[0033] The recommendation unit can provide specific health recommendations to the user based on the analysis results. Specific health recommendations include, but are not limited to, examples of dietary improvements, recommended exercise, and lifestyle changes. For example, the recommendation unit can recommend dietary improvements to the user based on the analysis results. For example, the recommendation unit can recommend a balanced diet to the user. The recommendation unit can also recommend exercise to the user based on the analysis results. For example, the recommendation unit can recommend regular exercise to the user. The recommendation unit can also recommend lifestyle changes to the user based on the analysis results. For example, the recommendation unit can suggest stress management methods to the user. This allows the user to receive specific health recommendations. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input the analysis results to a generation AI and cause the generation AI to generate specific health recommendations.

[0034] The recommendation unit can provide the user with at least one action plan of dietary improvement or exercise recommendation. Examples of action plans include, but are not limited to, dietary improvement, exercise recommendation, and lifestyle change. For example, the recommendation unit can suggest dietary improvement to the user. For example, the recommendation unit can recommend a balanced diet to the user. The recommendation unit can also recommend exercise to the user. For example, the recommendation unit can recommend regular exercise to the user. The recommendation unit can also suggest lifestyle changes to the user. For example, the recommendation unit can suggest stress management methods to the user. This allows the user to be provided with a specific action plan. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the analysis results into a generation AI and cause the generation AI to generate a specific action plan.

[0035] The collection unit can analyze the user's past lifestyle habit data and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method based on data previously input by the user. For example, the collection unit selects a collection method for a specific time period based on the user's past data. The collection unit can also analyze the user's past data and customize the collection method. For example, the collection unit adjusts the collection method to suit the user's preferences based on the user's past data. This makes it possible to select the optimal collection method based on the past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past lifestyle habit data into a generation AI and have the generation AI select the optimal collection method.

[0036] When collecting lifestyle habit data, the collection unit can filter the data based on the user's current health condition and areas of interest. The collection unit, for example, collects only necessary data, taking into account the user's current health condition. For example, the collection unit prioritizes collecting relevant data based on the user's areas of interest. The collection unit can also customize the data to be collected based on the user's health condition and areas of interest. For example, the collection unit prioritizes collecting specific data based on the user's health condition. This allows data to be filtered based on the user's health condition and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and areas of interest to a generation AI and have the generation AI perform filtering.

[0037] When collecting lifestyle habit data, the collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, if the user lives in a specific area, the collection unit collects data related to health risks in that area. For example, if the user is traveling, the collection unit collects data related to health risks at the travel destination. The collection unit can also collect region-specific health data based on the user's geographical location information. For example, the collection unit evaluates region-specific health risks based on the user's geographical location information. This allows collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] When collecting lifestyle habit data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media posts and collects health-related data. For example, the collection unit identifies health topics of interest from the user's social media activities and collects related data. The collection unit can also collect lifestyle habit data based on the user's social media activities. For example, the collection unit analyzes the user's social media activities and collects health-related data. This makes it possible to collect related data based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specialized analysis algorithm to medical data. For example, the analysis unit applies a general analysis algorithm to lifestyle habit data. The analysis unit can also select the optimal analysis algorithm depending on the data category. For example, the analysis unit applies a specialized analysis algorithm to medical data. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit performs a simplified analysis on older data. The analysis unit can also dynamically adjust the analysis priority according to the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. This makes it possible to determine the analysis priority according to the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit performs a simplified analysis on less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis according to the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the analysis result. For example, the recommendation unit makes a detailed recommendation for an analysis result with a high importance. For example, the recommendation unit makes a simplified recommendation for an analysis result with a low importance. The recommendation unit can also dynamically adjust the level of detail of the recommendation according to the importance of the analysis result. For example, the recommendation unit adjusts the level of detail of the recommendation based on the importance of the analysis result. This makes it possible to adjust the level of detail of the recommendation according to the importance of the analysis result. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0044] The recommendation unit can apply different recommendation algorithms depending on the category of the analysis results when making recommendations. For example, the recommendation unit applies a specialized recommendation algorithm to recommendations based on medical data. For example, the recommendation unit applies a general recommendation algorithm to recommendations based on lifestyle data. The recommendation unit can also select the optimal recommendation algorithm depending on the category of the analysis results. For example, the recommendation unit applies a specialized recommendation algorithm to recommendations based on medical data. This allows the optimal recommendation algorithm to be applied depending on the category of the analysis results. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the category of the analysis results into a generating AI and have the generating AI execute the application of the optimal recommendation algorithm.

[0045] The recommendation unit can determine the priority of recommendations based on the timing of analysis result collection when issuing recommendations. For example, the recommendation unit may prioritize recommendations based on the most recent analysis results. For example, it may provide simplified recommendations for older analysis results. The recommendation unit can also dynamically adjust the priority of recommendations according to the timing of analysis result collection. For example, the recommendation unit may determine the priority of recommendations based on the timing of analysis result collection. This allows the recommendation unit to determine the priority of recommendations according to the timing of analysis result collection. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input the timing of analysis result collection into a generating AI and have the generating AI determine the priority of recommendations.

[0046] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the relevance of the analysis results. For example, the recommendation unit prioritizes recommendations based on highly relevant analysis results. For example, the recommendation unit makes a simplified recommendation for less relevant analysis results. The recommendation unit can also dynamically adjust the order of recommendations according to the relevance of the analysis results. For example, the recommendation unit adjusts the order of recommendations based on the relevance of the analysis results. This makes it possible to adjust the order of recommendations according to the relevance of the analysis results. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of recommendations.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The health recommendation system can further collect the user's social network data and use it for analysis. For example, the collection unit can collect data such as the user's social media posts, friendships, and number of followers. This allows the system to understand the user's social activity and stress level and provide more accurate health predictions. The analysis unit can also evaluate the user's social support and degree of isolation based on the collected social network data and analyze the impact this has on health. Furthermore, the recommendation unit can recommend to the user to increase social activity and interact with friends based on the analysis results. This allows the system to provide comprehensive health recommendations that also take the user's social health into consideration.

[0049] The health recommendation system can further collect the user's genetic information and use it for analysis. For example, the collection unit can collect the user's genetic test results. This allows the user's genetic risk factors to be identified and more personalized health predictions to be made. The analysis unit can analyze the interaction between the user's genetic risk factors and lifestyle habits based on the collected genetic information. Furthermore, the recommendation unit can suggest specific lifestyle improvement measures to the user to reduce genetic risks based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take into account the user's genetic health risks.

[0050] The health recommendation system can also collect the user's purchase history data and use it for analysis. For example, the collection unit can collect the user's purchase history of ingredients and supplements. This allows the user's eating habits and nutritional intake status to be understood, enabling more accurate health predictions to be made. The analysis unit can analyze the user's eating habits and nutritional balance based on the collected purchase history data. Furthermore, the recommendation unit can recommend to the user improving their nutritional balance or consuming specific nutrients based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take into account the user's purchase history.

[0051] The health recommendation system can also collect environmental data about the user and use it for analysis. For example, the collection unit can collect data about the user's living environment and work environment. This allows the system to understand the impact of the user's living environment on health and make more accurate health predictions. The analysis unit can analyze the interaction between the user's living environment and health status based on the collected environmental data. Furthermore, the recommendation unit can suggest to the user ways to improve their living environment based on the analysis results. This makes it possible to provide comprehensive health recommendations that take the user's environment into consideration.

[0052] The health recommendation system can further collect data on the user's hobbies and interests and use it for analysis. For example, the collection unit can collect survey results and online activity data on the user's hobbies and interests. This allows the system to understand the impact of the user's hobbies and interests on health and make more accurate health predictions. The analysis unit can analyze the interaction between the user's lifestyle habits and health status based on the collected data on hobbies and interests. Furthermore, the recommendation unit can suggest health promotion measures to the user that utilize the user's hobbies and interests based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take the user's hobbies and interests into consideration.

[0053] The health recommendation system can further collect the user's occupational data and use it for analysis. For example, the collection unit can collect data on the user's occupation, working hours, and workplace stress level. This allows the impact of the user's occupation on health to be understood and more accurate health predictions to be made. The analysis unit can also analyze the interaction between the user's occupation and health status based on the collected occupational data. Furthermore, the recommendation unit can suggest to the user ways to improve the work environment and stress management methods based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take the user's occupation into consideration.

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

[0055] Step 1: The collection unit collects lifestyle and medical data of the user. Specifically, it collects data on the user's diet, exercise, sleep, etc. For example, the collection unit collects food records entered by the user using a smartphone app, exercise data recorded using a wearable device, and sleep data recorded using a smartwatch. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts future health status. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit performs statistical analysis based on the collected data to predict future health status. It can also analyze the data using machine learning algorithms, compare it with past data to perform risk assessment, and calculate a health score. Step 3: The recommendation unit provides health recommendations to the user based on the analysis results obtained by the analysis unit. These recommendations may include suggestions for dietary improvements, exercise, and lifestyle changes. For example, the recommendation unit provides the user with a specific action plan based on the analysis results to raise awareness of lifestyle-related diseases and encourage health promotion activities.

[0056] (Example 2) A health recommendation system according to an embodiment of the present invention is a novel method for raising users' awareness of lifestyle-related diseases. It inputs the user's lifestyle habits and MRI and X-ray images generated during medical examinations into a large-scale language model (LLM) to visualize and recommend the user's future health status. This health recommendation system collects data related to the user's lifestyle habits (e.g., diet, exercise, sleep, etc.) and medical data such as MRI and X-ray images generated during medical examinations, and inputs the collected data into the LLM to predict the user's future health status. The LLM compares the data with past data to analyze how the user's lifestyle habits will affect their future health status. Based on the analysis results, the system provides specific health recommendations to the user. For example, the system indicates the risks of continuing the user's current lifestyle habits and areas for improvement. This allows the user to visualize their health status and raises their awareness of lifestyle-related diseases. This system not only raises the user's awareness of lifestyle-related diseases, but also promotes health promotion activities by suggesting specific improvement measures. For example, it provides specific action plans, such as recommendations for improving diet and exercise. This allows the user to proactively manage their health and prevent lifestyle-related diseases. This enables the health recommendation system to raise the user's awareness of lifestyle-related diseases and encourage health promotion activities.

[0057] A health recommendation system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects lifestyle and medical data of a user. The lifestyle of the user includes, but is not limited to, diet, exercise, and sleep. For example, the collection unit collects a diet record of the user. The collection unit can also collect an exercise record of the user. The collection unit can also collect a sleep pattern of the user. For example, the collection unit collects a diet record entered by the user using a smartphone app. The collection unit can also collect exercise data recorded by the user using a wearable device. The collection unit can also collect sleep data recorded by the user using a smartwatch. The analysis unit analyzes the data collected by the collection unit and predicts a future health condition. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, such examples. For example, the analysis unit performs statistical analysis on the collected data to predict a future health condition. The analysis unit can also analyze the data using a machine learning algorithm to predict a future health condition. The analysis unit can also compare the data with past data to analyze how the user's lifestyle habits will affect their future health condition. For example, the analysis unit can perform a risk assessment based on past data and predict their future health condition. The analysis unit can also calculate a health score and predict their future health condition. The recommendation unit provides health recommendations to the user based on the analysis results obtained by the analysis unit. Health recommendations include, but are not limited to, recommendations for improving diet and exercising. For example, the recommendation unit can recommend the user to improve their diet based on the analysis results. The recommendation unit can also recommend exercising based on the analysis results. The recommendation unit can also recommend changes to their lifestyle habits based on the analysis results. For example, the recommendation unit can provide the user with a specific action plan. This allows the health recommendation system according to the embodiment to raise users' awareness of lifestyle-related diseases and encourage them to engage in health promotion activities.

[0058] The collection unit can collect data related to the user's lifestyle habits. Examples of lifestyle-related data include, but are not limited to, meal records, exercise records, and sleep patterns. For example, the collection unit collects the user's meal records. For example, the collection unit collects meal records entered by the user using a smartphone app. The collection unit can also collect the user's exercise records. For example, the collection unit collects exercise data recorded by the user using a wearable device. The collection unit can also collect the user's sleep patterns. For example, the collection unit collects sleep data recorded by the user using a smartwatch. This allows efficient collection of data related to the user's lifestyle habits. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to the user's lifestyle habits into a generation AI and have the generation AI analyze the data.

[0059] The collection unit can collect at least one medical data item, such as an MRI image or an X-ray image, created during a medical examination. Examples of medical data include, but are not limited to, MRI images, X-ray images, and blood test results. The collection unit collects, for example, MRI images created during a medical examination. For example, the collection unit digitally collects MRI images taken at a hospital. The collection unit can also collect X-ray images created during a medical examination. For example, the collection unit digitally collects X-ray images taken at a hospital. The collection unit can also collect medical data such as blood test results. For example, the collection unit digitally collects blood test results performed at a hospital. This allows for efficient collection of medical data created during a medical examination. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input MRI images or X-ray images created during a medical examination into a generation AI and have the generation AI analyze the image data.

[0060] The analysis unit can analyze the collected data and predict the future health state. Predictions of the future health state include, but are not limited to, risk assessments and health scores. For example, the analysis unit can perform statistical analysis on the collected data to predict the future health state. For example, the analysis unit can perform risk assessments on the collected data and predict the future health state. The analysis unit can also analyze the data using a machine learning algorithm to predict the future health state. For example, the analysis unit can calculate a health score on the collected data and predict the future health state. The analysis unit can also analyze how the user's lifestyle habits will affect the future health state by comparing it with past data. For example, the analysis unit can perform risk assessments on the past data and predict the future health state. This allows for accurate prediction of the future health state. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to predict the future health state.

[0061] The recommendation unit can provide specific health recommendations to the user based on the analysis results. Specific health recommendations include, but are not limited to, examples of dietary improvements, recommended exercise, and lifestyle changes. For example, the recommendation unit can recommend dietary improvements to the user based on the analysis results. For example, the recommendation unit can recommend a balanced diet to the user. The recommendation unit can also recommend exercise to the user based on the analysis results. For example, the recommendation unit can recommend regular exercise to the user. The recommendation unit can also recommend lifestyle changes to the user based on the analysis results. For example, the recommendation unit can suggest stress management methods to the user. This allows the user to receive specific health recommendations. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input the analysis results to a generation AI and cause the generation AI to generate specific health recommendations.

[0062] The recommendation unit can provide the user with at least one action plan of dietary improvement or exercise recommendation. Examples of action plans include, but are not limited to, dietary improvement, exercise recommendation, and lifestyle change. For example, the recommendation unit can suggest dietary improvement to the user. For example, the recommendation unit can recommend a balanced diet to the user. The recommendation unit can also recommend exercise to the user. For example, the recommendation unit can recommend regular exercise to the user. The recommendation unit can also suggest lifestyle changes to the user. For example, the recommendation unit can suggest stress management methods to the user. This allows the user to be provided with a specific action plan. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the analysis results into a generation AI and cause the generation AI to generate a specific action plan.

[0063] The data collection unit can estimate the user's emotions and adjust the timing of collecting lifestyle data based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can collect data immediately to obtain accurate information. Also, if the user is busy, the data collection unit can adjust the collection timing to match the user's schedule. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0064] The collection unit can analyze the user's past lifestyle habit data and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method based on data previously input by the user. For example, the collection unit selects a collection method for a specific time period based on the user's past data. The collection unit can also analyze the user's past data and customize the collection method. For example, the collection unit adjusts the collection method to suit the user's preferences based on the user's past data. This makes it possible to select the optimal collection method based on the past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past lifestyle habit data into a generation AI and have the generation AI select the optimal collection method.

[0065] When collecting lifestyle habit data, the collection unit can filter the data based on the user's current health condition and areas of interest. The collection unit, for example, collects only necessary data, taking into account the user's current health condition. For example, the collection unit prioritizes collecting relevant data based on the user's areas of interest. The collection unit can also customize the data to be collected based on the user's health condition and areas of interest. For example, the collection unit prioritizes collecting specific data based on the user's health condition. This allows data to be filtered based on the user's health condition and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and areas of interest to a generation AI and have the generation AI perform filtering.

[0066] The collection unit can estimate the user's emotions and determine the priority of 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. For example, if the user is relaxed, the collection unit can collect overall health data. Furthermore, if the user is interested in a particular health issue, the collection unit can prioritize collecting data related to that issue. This allows data prioritization to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data prioritization.

[0067] When collecting lifestyle habit data, the collection unit can prioritize collection of highly relevant data based on the user's geographical location information. For example, if the user lives in a specific area, the collection unit collects data related to health risks in that area. For example, if the user is traveling, the collection unit collects data related to health risks at the travel destination. The collection unit can also collect region-specific health data based on the user's geographical location information. For example, the collection unit evaluates region-specific health risks based on the user's geographical location information. This allows collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0068] When collecting lifestyle habit data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media posts and collects health-related data. For example, the collection unit identifies health topics of interest from the user's social media activities and collects related data. The collection unit can also collect lifestyle habit data based on the user's social media activities. For example, the collection unit analyzes the user's social media activities and collects health-related data. This makes it possible to collect related data based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0069] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can also provide visually appealing analysis results. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specialized analysis algorithm to medical data. For example, the analysis unit applies a general analysis algorithm to lifestyle habit data. The analysis unit can also select the optimal analysis algorithm depending on the data category. For example, the analysis unit applies a specialized analysis algorithm to medical data. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually appealing analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0073] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. For example, the analysis unit performs a simplified analysis on older data. The analysis unit can also dynamically adjust the analysis priority according to the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. This makes it possible to determine the analysis priority according to the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit performs a simplified analysis on less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis according to the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0075] The recommendation unit can estimate the user's emotions and adjust the way the recommendation is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the recommendation unit provides a simple and easy-to-understand recommendation. For example, if the user is relaxed, the recommendation unit can provide a detailed recommendation. Furthermore, if the user is excited, the recommendation unit can also provide a visually appealing recommendation. This allows the way the recommendation is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the recommendation is presented.

[0076] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the analysis result. For example, the recommendation unit makes a detailed recommendation for an analysis result with a high importance. For example, the recommendation unit makes a simplified recommendation for an analysis result with a low importance. The recommendation unit can also dynamically adjust the level of detail of the recommendation according to the importance of the analysis result. For example, the recommendation unit adjusts the level of detail of the recommendation based on the importance of the analysis result. This makes it possible to adjust the level of detail of the recommendation according to the importance of the analysis result. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0077] The recommendation unit can apply different recommendation algorithms depending on the category of the analysis results when making recommendations. For example, the recommendation unit applies a specialized recommendation algorithm to recommendations based on medical data. For example, the recommendation unit applies a general recommendation algorithm to recommendations based on lifestyle data. The recommendation unit can also select the optimal recommendation algorithm depending on the category of the analysis results. For example, the recommendation unit applies a specialized recommendation algorithm to recommendations based on medical data. This allows the optimal recommendation algorithm to be applied depending on the category of the analysis results. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the category of the analysis results into a generating AI and have the generating AI execute the application of the optimal recommendation algorithm.

[0078] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user's emotions. For example, if the user is in a hurry, the recommendation unit provides a short and to-the-point recommendation. For example, if the user is relaxed, the recommendation unit can provide a detailed recommendation. Furthermore, if the user is excited, the recommendation unit can also provide a visually appealing recommendation. This allows the length of the recommendation to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit may be performed using AI, or may be performed without using AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the recommendation.

[0079] The recommendation unit can determine the priority of recommendations based on the timing of analysis result collection when issuing recommendations. For example, the recommendation unit may prioritize recommendations based on the most recent analysis results. For example, it may provide simplified recommendations for older analysis results. The recommendation unit can also dynamically adjust the priority of recommendations according to the timing of analysis result collection. For example, the recommendation unit may determine the priority of recommendations based on the timing of analysis result collection. This allows the recommendation unit to determine the priority of recommendations according to the timing of analysis result collection. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input the timing of analysis result collection into a generating AI and have the generating AI determine the priority of recommendations.

[0080] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the relevance of the analysis results. For example, the recommendation unit prioritizes recommendations based on highly relevant analysis results. For example, the recommendation unit makes a simplified recommendation for less relevant analysis results. The recommendation unit can also dynamically adjust the order of recommendations according to the relevance of the analysis results. For example, the recommendation unit adjusts the order of recommendations based on the relevance of the analysis results. This makes it possible to adjust the order of recommendations according to the relevance of the analysis results. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of recommendations. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user lifestyle data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future health status based on the collected data. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides specific health recommendations to the user based on the analysis results. Some or all of the data collection unit, analysis unit, and recommendation unit may be implemented in the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user lifestyle data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts future health status based on the collected data. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides specific health recommendations to the user based on the analysis results. Some or all of the data collection unit, analysis unit, and recommendation unit may be implemented, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user lifestyle data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future health status based on the collected data. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides specific health recommendations to the user based on the analysis results. Some or all of the data collection unit, analysis unit, and recommendation unit may be implemented, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and recommendation 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 lifestyle habit data of the user using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts a future health condition based on the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides specific health recommendations to the user based on the analysis results. Some or all of the collection unit, analysis unit, and recommendation unit may be realized, for example, by the control unit 46A of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The health recommendation system can further collect the user's social network data and use it for analysis. For example, the collection unit can collect data such as the user's social media posts, friendships, and number of followers. This allows the system to understand the user's social activity and stress level and provide more accurate health predictions. The analysis unit can also evaluate the user's social support and degree of isolation based on the collected social network data and analyze the impact this has on health. Furthermore, the recommendation unit can recommend to the user to increase social activity and interact with friends based on the analysis results. This allows the system to provide comprehensive health recommendations that also take the user's social health into consideration.

[0083] The health recommendation system can further collect the user's genetic information and use it for analysis. For example, the collection unit can collect the user's genetic test results. This allows the user's genetic risk factors to be identified and more personalized health predictions to be made. The analysis unit can analyze the interaction between the user's genetic risk factors and lifestyle habits based on the collected genetic information. Furthermore, the recommendation unit can suggest specific lifestyle improvement measures to the user to reduce genetic risks based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take into account the user's genetic health risks.

[0084] The health recommendation system can further estimate the user's emotions and adjust the timing of the recommendation based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can delay the timing of the recommendation and make the recommendation when the user is in a relaxed state. The analysis unit can analyze the optimal timing of the recommendation based on the user's emotion data. Furthermore, if the user is relaxed, the recommendation unit can make the recommendation immediately and provide an effective action plan. This allows health recommendations to be made at the optimal timing according to the user's emotions.

[0085] The health recommendation system can also collect the user's purchase history data and use it for analysis. For example, the collection unit can collect the user's purchase history of ingredients and supplements. This allows the user's eating habits and nutritional intake status to be understood, enabling more accurate health predictions to be made. The analysis unit can analyze the user's eating habits and nutritional balance based on the collected purchase history data. Furthermore, the recommendation unit can recommend to the user improving their nutritional balance or consuming specific nutrients based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take into account the user's purchase history.

[0086] The health advisory system can further estimate the user's emotions and customize the advisory content based on those emotions. For example, if the data collection unit is feeling stressed, it can recommend exercises or relaxation methods that are effective in reducing stress. The analysis unit can analyze the user's emotional data to determine the most appropriate advisory content. Furthermore, if the user is relaxed, the advisory unit can provide an action plan that is effective in maintaining or improving health. This allows the system to provide optimal health advisories tailored to the user's emotions.

[0087] The health recommendation system can also collect environmental data about the user and use it for analysis. For example, the collection unit can collect data about the user's living environment and work environment. This allows the system to understand the impact of the user's living environment on health and make more accurate health predictions. The analysis unit can analyze the interaction between the user's living environment and health status based on the collected environmental data. Furthermore, the recommendation unit can suggest to the user ways to improve their living environment based on the analysis results. This makes it possible to provide comprehensive health recommendations that take the user's environment into consideration.

[0088] The health advisory system can further estimate the user's emotions and adjust the format of the advisories based on those emotions. For example, the data collection unit can provide advisories in a simple and easy-to-understand format if the user is feeling stressed. The analysis unit can analyze the user's emotion data to determine the optimal advisory format. Furthermore, the advisory unit can provide advisories in a format that includes detailed information if the user is relaxed. This allows the system to provide health advisories in the most appropriate format according to the user's emotions.

[0089] The health recommendation system can further collect data on the user's hobbies and interests and use it for analysis. For example, the collection unit can collect survey results and online activity data on the user's hobbies and interests. This allows the system to understand the impact of the user's hobbies and interests on health and make more accurate health predictions. The analysis unit can analyze the interaction between the user's lifestyle habits and health status based on the collected data on hobbies and interests. Furthermore, the recommendation unit can suggest health promotion measures to the user that utilize the user's hobbies and interests based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take the user's hobbies and interests into consideration.

[0090] The health recommendation system can further estimate the user's emotions and adjust the frequency of recommendations based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of recommendations to avoid placing a burden on the user. The analysis unit can analyze the optimal recommendation frequency based on the user's emotion data. Furthermore, if the user is relaxed, the recommendation unit can increase the frequency of recommendations to promote more proactive health promotion. This allows health recommendations to be made at an optimal frequency depending on the user's emotions.

[0091] The health recommendation system can further collect the user's occupational data and use it for analysis. For example, the collection unit can collect data on the user's occupation, working hours, and workplace stress level. This allows the impact of the user's occupation on health to be understood and more accurate health predictions to be made. The analysis unit can also analyze the interaction between the user's occupation and health status based on the collected occupational data. Furthermore, the recommendation unit can suggest to the user ways to improve the work environment and stress management methods based on the analysis results. This makes it possible to provide comprehensive health recommendations that also take the user's occupation into consideration.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects lifestyle and medical data of the user. Specifically, it collects data on the user's diet, exercise, sleep, etc. For example, the collection unit collects food records entered by the user using a smartphone app, exercise data recorded using a wearable device, and sleep data recorded using a smartwatch. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts future health status. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit performs statistical analysis based on the collected data to predict future health status. It can also analyze the data using machine learning algorithms, compare it with past data to perform risk assessment, and calculate a health score. Step 3: The recommendation unit provides health recommendations to the user based on the analysis results obtained by the analysis unit. These recommendations may include suggestions for dietary improvements, exercise, and lifestyle changes. For example, the recommendation unit provides the user with a specific action plan based on the analysis results to raise awareness of lifestyle-related diseases and encourage health promotion activities.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 lifestyle and medical data of a user; an analysis unit that analyzes the data collected by the collection unit and predicts a future health state; a recommendation unit that provides health advice to the user based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collecting data about users' lifestyle habits 2. The system of claim 1.

3. The collecting unit Collect at least one medical data item from MRI or X-ray images taken during the consultation.

2. The system of claim 1.

4. The analysis unit Analyzing collected data and predicting future health conditions 2. The system of claim 1.

5. The recommendation unit Provide specific health recommendations to users based on the analysis results 2. The system of claim 1.

6. The recommendation unit Providing the user with an action plan for at least one of improving their diet or recommending exercise 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting lifestyle data based on the estimated user emotions.

2. The system of claim 1.

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

Images