system

Abstract

The system according to the embodiment aims to predict future health conditions and suggest appropriate lifestyle habits and treatments. [Solution] A system according to an embodiment includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects health checkup information or questionnaires and treatment history. The generation unit analyzes the information collected by the collection unit and creates a future avatar. The advice provision unit compares the future avatar generated by the generation unit with current health information and suggests what lifestyle habits and treatments would have been appropriate in the past.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to adequately predict future health conditions based on health checkups and treatment history, and suggest appropriate lifestyle habits and treatments.

[0005] The system according to the embodiment aims to predict future health conditions and suggest appropriate lifestyle habits and treatments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects health checkup information or questionnaires and treatment history. The generation unit analyzes the information collected by the collection unit and creates a future avatar. The advice provision unit compares the future avatar generated by the generation unit with current health information and suggests what lifestyle habits and treatments would have been appropriate in the past. [Effects of the Invention]

[0007] The system according to the embodiment can predict future health conditions and suggest appropriate lifestyle habits and treatments. [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) In an embodiment of the health prediction system of the present invention, a generation AI creates avatars of your future self and your family members based on family health checkup information, periodic questionnaires (lifestyle and thoughts), and treatment history. This health prediction system collects data such as family health checkup information, periodic questionnaires, and treatment history, and the generation AI analyzes this data to create a future avatar. This avatar includes both a "healthy future self" and a "future self in the event of an emergency." Next, the generation AI provides advice on "what lifestyle habits and treatments would have been better in the past" based on current health information. Specifically, the generation AI compares the future avatar with current health information and suggests what lifestyle habits and treatments would have been better in the past. This allows users to predict future health risks and improve their current lifestyle habits and treatments. For example, the generation AI creates an avatar of a "healthy future self" based on family health checkup information and lifestyle questionnaire results, and compares it with current health information to provide advice such as "You should have exercised more in the past." The system also creates an avatar of "your future self in the event of an emergency" and compares it with your current health information to provide advice such as "You should have eaten a more balanced diet in the past." This system allows users to predict future health risks and improve their current lifestyle habits and treatment methods. For example, the generating AI can provide specific advice such as "If you had exercised more in the past, your future health would have been better," encouraging users to review their current lifestyle habits and reduce future health risks. In this way, the health prediction system collects the user's health information, generates a future avatar, and suggests past lifestyle habits and treatment methods, thereby predicting future health risks and improving current lifestyle habits and treatment methods.

[0029] A health prediction system according to an embodiment includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects health checkup information, questionnaires, and treatment histories. The collection unit can collect the health checkup information, questionnaires, and treatment histories, for example, through a dedicated application. The collection unit efficiently collects information from a user using, for example, a smartphone app or a web application. The collection unit can also estimate a user's emotions and adjust the timing of collecting the health checkup information and questionnaires based on the estimated user emotions. For example, if a user is feeling stressed, the health checkup information and questionnaires can be collected during a time when the user is able to relax. The generation unit uses a generation AI to analyze the information collected by the collection unit and create a future avatar. The generation unit predicts a future health state from past data using, for example, a machine learning algorithm and creates the future avatar. The generation unit can predict a future health state with high accuracy using, for example, a machine learning algorithm such as a neural network or a decision tree. When generating the future avatar, the generation unit can also adjust the level of detail of the avatar based on the importance of health information. For example, a detailed avatar can be generated based on health information of high importance. The advice providing unit compares the future avatar generated by the generation unit with current health information and suggests what lifestyle habits and treatments would have been better in the past. The advice providing unit can compare the future avatar with current health information and specifically suggest what lifestyle habits and treatments would have been better in the past. For example, it can suggest dietary improvements, recommended exercise, changes in treatment methods, etc. As a result, the health prediction system according to the embodiment collects the user's health information, generates a future avatar, and suggests past lifestyle habits and treatments, thereby predicting future health risks and improving current lifestyle habits and treatments.

[0030] The collection unit can collect health checkup information, questionnaires, and treatment histories through a dedicated application. Examples of dedicated applications include, but are not limited to, smartphone apps and web applications. The collection unit can collect health checkup information, questionnaires, and treatment histories from a user using a smartphone app. For example, a user inputs health checkup results through the smartphone app, and the collection unit stores the information in a database. The collection unit can also collect information from a user using a web application. For example, a user answers a questionnaire through the web application, and the collection unit analyzes the information. This allows health information to be collected efficiently through the dedicated application. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information entered by a user into AI, which then analyzes the information and stores it in a database.

[0031] The generation unit can use a machine learning algorithm to accurately predict future health conditions from past data and create a future avatar. Examples of machine learning algorithms include, but are not limited to, neural networks and decision trees. The generation unit can, for example, use a neural network to analyze past health checkup data and questionnaire results to predict future health conditions. For example, the generation unit inputs past data, and the neural network outputs the future health conditions. The generation unit can also use a decision tree to predict future health conditions from past data. For example, the generation unit inputs past data, and the decision tree outputs the future health conditions. Furthermore, the generation unit can create future avatars using a machine learning algorithm. For example, the generation unit creates a 3D model or a health condition simulation based on the future health conditions. Using a machine learning algorithm, the future health conditions can be predicted with high accuracy and an avatar can be created. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past data into the generation AI, which can then predict future health conditions and create an avatar.

[0032] The advice providing unit can compare the future avatar with current health information and suggest what lifestyle habits and treatments would have been better in the past. For example, the advice providing unit can compare the future avatar with current health information and specifically suggest what lifestyle habits and treatments would have been better in the past. For example, the advice providing unit can compare the future avatar with current health information and provide advice such as, "You should have exercised more in the past." The advice providing unit can also compare the future avatar with current health information and provide advice such as, "You should have eaten a more balanced diet in the past." Furthermore, the advice providing unit can provide specific advice to help the user improve their current lifestyle habits and treatment methods. For example, the advice providing unit can suggest dietary improvements, recommended exercise, changes in treatment methods, etc. As a result, by comparing the future avatar with current health information, specific suggestions can be made about past lifestyle habits and treatment methods. Some or all of the above-described processing by the advice providing unit may be performed using, or without, AI, for example. For example, the advice provider can input a future avatar and current health information into the AI, allowing the AI ​​to suggest what lifestyle habits and treatments would have been better in the past.

[0033] The generation unit can create avatars for both "a healthy future you" and "a future you in the event of an emergency." The generation unit can create avatars for both "a healthy future you" and "a future you in the event of an emergency," for example, using a machine learning algorithm. For example, the generation unit can analyze past data to create a healthy future avatar. The generation unit can also analyze past data to create a future avatar in the event of an emergency. This allows the creation of future avatars based on different scenarios, making it possible to provide the user with advice from multiple perspectives. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input past data into the generation AI, which can then create both a healthy future avatar and a future avatar in the event of an emergency.

[0034] The advice providing unit can provide specific advice to help the user improve their current lifestyle habits and treatment methods. For example, the advice providing unit can compare a future avatar with current health information and provide specific suggestions about what lifestyle habits and treatments the user should have performed in the past. For example, the advice providing unit can compare a future avatar with current health information and provide advice such as, "You should have exercised more in the past." The advice providing unit can also compare a future avatar with current health information and provide advice such as, "You should have eaten a more balanced diet in the past." Furthermore, the advice providing unit can provide specific advice to help the user improve their current lifestyle habits and treatment methods. For example, the advice providing unit can suggest dietary improvements, recommended exercise, changes in treatment methods, etc. By providing specific advice, the user can actually take specific actions to improve their lifestyle habits and treatment methods. Some or all of the above-described processing by the advice providing unit can be performed using, or without, AI. For example, the advice providing unit can input a future avatar and current health information into AI, which can then suggest what lifestyle habits and treatments the user should have performed in the past.

[0035] The collection unit can analyze the user's past health checkup history and select the optimal collection method. The collection unit can, for example, analyze the user's past health checkup history and select the optimal collection method. For example, the collection unit prioritizes collection of health checkup items that the user has frequently undergone in the past. The collection unit can also focus on collecting items that pose a specific high health risk from the user's past health checkup history. Furthermore, the collection unit can suggest the optimal collection frequency based on the user's past health checkup history. In this way, by analyzing the past health checkup history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the user's past health checkup history into AI, which can select the optimal collection method.

[0036] The collection unit can filter the health checkup information and questionnaires based on the user's current living situation and areas of interest when collecting the information. For example, the collection unit can filter the health checkup information and questionnaires based on the user's current living situation and areas of interest when collecting the information. For example, the collection unit prioritizes collecting health checkup items that are highly relevant to the user's current living situation. The collection unit can also collect specific health information based on the user's areas of interest. Furthermore, the collection unit can provide customized questionnaires based on the user's living situation and areas of interest. This allows more relevant information to be collected by filtering the information based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI, which can then perform the filtering.

[0037] The collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting health checkup information or questionnaires. For example, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting health checkup information or questionnaires. For example, the collection unit collects relevant health information based on health risks in the area where the user lives. The collection unit can also collect information on health issues specific to the area based on the user's geographical location information. Furthermore, the collection unit can suggest optimal health checkup items taking the user's geographical location information into consideration. This makes it possible to address health risks specific to the area by collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant information.

[0038] The collection unit can analyze the user's social media activities and collect related information when collecting health checkup information or a questionnaire. For example, the collection unit can analyze the user's social media activities and collect related information when collecting health checkup information or a questionnaire. For example, the collection unit can identify health concerns from the user's social media activities and collect related information. The collection unit can also prioritize collection of items with high health risks based on the user's social media activities. Furthermore, the collection unit can analyze the user's social media activities and suggest optimal health checkup items. In this way, health concerns can be identified and related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related information.

[0039] The generation unit can adjust the level of detail of the avatar based on the importance of the health information when generating a future avatar. For example, the generation unit can adjust the level of detail of the avatar based on the importance of the health information when generating a future avatar. For example, the generation unit can generate a detailed avatar based on health information with a high level of importance. The generation unit can also generate a simplified avatar based on health information with a low level of importance. Furthermore, the generation unit can adjust the representation method of the avatar based on the importance of the health information. By adjusting the level of detail of the avatar based on the importance of the health information, an avatar that reflects more important information can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input health information importance data into the generation AI, which can then adjust the level of detail of the avatar.

[0040] The generation unit can apply different generation algorithms depending on the category of health information when generating a future avatar. For example, the generation unit can apply different generation algorithms depending on the category of health information when generating a future avatar. For example, the generation unit can generate a detailed avatar based on physical health information. The generation unit can also generate an avatar that emphasizes facial expressions and posture based on mental health information. Furthermore, the generation unit can generate an avatar that recreates a lifestyle scene based on lifestyle information. In this way, by applying different generation algorithms depending on the category of health information, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input health information category data into the generation AI, and the generation AI can generate an avatar by applying different generation algorithms.

[0041] The generation unit can determine the priority of avatars based on the time of submission of health information when generating future avatars. For example, the generation unit can determine the priority of avatars based on the time of submission of health information when generating future avatars. For example, the generation unit can prioritize avatars based on the most recently submitted health information. The generation unit can also lower the priority of avatars based on health information that was submitted the oldest. Furthermore, the generation unit can adjust the order in which avatars are generated based on the time of submission of health information. By determining the priority of avatars based on the time of submission of health information, avatars that reflect the most recent information can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the time of submission of health information into the generation AI, which can then determine the priority of avatars.

[0042] The generation unit can adjust the order of avatars based on the relevance of health information when generating future avatars. For example, the generation unit can adjust the order of avatars based on the relevance of health information when generating future avatars. For example, the generation unit can prioritize generating avatars based on highly relevant health information. The generation unit can also lower the order of avatar generation based on less relevant health information. Furthermore, the generation unit can adjust the order of avatar generation according to the relevance of health information. In this way, by adjusting the order of avatars based on the relevance of health information, more relevant avatars can be prioritized for generation. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input health information relevance data into the generation AI, and the generation AI can adjust the order of avatars.

[0043] The advice providing unit can adjust the level of detail of the advice based on the importance of the health information when providing the advice. The advice providing unit can, for example, adjust the level of detail of the advice based on the importance of the health information when providing the advice. For example, the advice providing unit can provide detailed advice based on health information of high importance. The advice providing unit can also provide simplified advice based on health information of low importance. Furthermore, the advice providing unit can adjust the level of detail of the advice according to the importance of the health information. In this way, by adjusting the level of detail of the advice according to the importance of the health information, it is possible to provide advice based on more important information. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input health information importance data to AI, and the AI ​​can adjust the level of detail of the advice.

[0044] The advice providing unit can apply different advice algorithms depending on the category of health information when providing advice. The advice providing unit can apply different advice algorithms depending on the category of health information when providing advice. For example, the advice providing unit can provide advice regarding exercise and diet based on physical health information. The advice providing unit can also provide advice regarding stress management and relaxation based on mental health information. Furthermore, the advice providing unit can provide advice regarding improving daily life based on lifestyle habit information. In this way, by applying different advice algorithms depending on the category of health information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice providing unit can input health information category data into AI, and the AI ​​can provide advice by applying different advice algorithms.

[0045] The advice providing unit can determine the priority of advice based on the time of submission of the health information when providing the advice. The advice providing unit can determine the priority of advice based on the time of submission of the health information when providing the advice, for example. For example, the advice providing unit can preferentially provide advice based on the most recently submitted health information. The advice providing unit can also lower the priority of advice based on the health information that was submitted the oldest. Furthermore, the advice providing unit can adjust the order in which advice is provided depending on the time of submission of the health information. In this way, by determining the priority of advice based on the time of submission of the health information, advice based on the most recent information can be provided. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input data on the time of submission of health information into AI, and the AI ​​can determine the priority of advice.

[0046] The advice providing unit can adjust the order of advice based on the relevance of the health information when providing advice. The advice providing unit can, for example, adjust the order of advice based on the relevance of the health information when providing advice. For example, the advice providing unit can prioritize providing advice based on highly relevant health information. The advice providing unit can also lower the order of providing advice based on less relevant health information. Furthermore, the advice providing unit can adjust the order of providing advice according to the relevance of the health information. In this way, by adjusting the order of advice based on the relevance of the health information, more relevant advice can be provided preferentially. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI, for example. For example, the advice providing unit can input health information relevance data into AI, and the AI ​​can adjust the order of advice.

[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 prediction system may further include a sleep analysis unit that monitors the user's sleep patterns. The sleep analysis unit collects the user's sleep data and analyzes the quality and patterns of sleep. For example, the sleep analysis unit may record the user's sleep time, the ratio of deep sleep to light sleep, the number of awakenings during the night, etc., and evaluate the quality of sleep based on this data. Furthermore, the sleep analysis unit may suggest ways to improve sleep based on the user's sleep data. For example, it may provide advice on how to relax before bed or on creating an appropriate sleeping environment. This allows the user to improve their sleep quality and their overall health.

[0049] The health prediction system may further include a dietary recorder that collects the user's dietary data. The dietary recorder records the user's daily dietary intake and analyzes the nutritional balance. For example, the dietary recorder may record the calories, proteins, fats, carbohydrates, vitamins, minerals, and other nutrients ingested by the user, and evaluate the nutritional balance based on this data. Furthermore, the dietary recorder may suggest ways to improve the nutritional balance based on the user's dietary data. For example, if a specific nutrient is lacking, the dietary recorder may advise the user to consume foods containing that nutrient. This allows the user to review their dietary intake and achieve a healthier diet.

[0050] The health prediction system may further include an exercise recording unit that collects the user's exercise data. The exercise recording unit records the type, duration, intensity, etc. of exercise performed by the user and analyzes the effects of the exercise. For example, the exercise recording unit can collect exercise data such as walking, running, and strength training performed by the user and evaluate the effects of the exercise based on this data. Furthermore, the exercise recording unit can also suggest ways to improve the user's exercise based on the user's exercise data. For example, if a specific exercise is lacking, the exercise recording unit can advise the user to add that exercise. This allows the user to review their exercise habits and live a healthier life.

[0051] The health prediction system may further include a stress analysis unit that monitors the user's stress level. The stress analysis unit collects biometric data such as the user's heart rate, respiratory rate, and electrodermal activity, and analyzes the stress level. For example, the stress analysis unit may record fluctuations in the user's heart rate and respiratory rate, and evaluate the stress level based on this data. Furthermore, the stress analysis unit may suggest stress reduction methods based on the user's stress data. For example, the stress analysis unit may advise on relaxation techniques and stress management techniques. This allows the user to manage their own stress level and maintain their physical and mental health.

[0052] The health prediction system can also prioritize the collection of highly relevant information based on the user's geographical location information. For example, it can collect relevant health information based on the health risks in the area where the user lives. It can also collect information on health issues specific to the area based on the user's geographical location information. It can also suggest optimal health checkup items taking the user's geographical location information into consideration. This allows the system to address health risks specific to the area by collecting highly relevant information based on the user's geographical location information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects health checkup information or questionnaires and treatment history. The collection unit can collect the health checkup information, questionnaires, and treatment history, for example, through a dedicated application. The collection unit can efficiently collect information from the user, for example, using a smartphone app or web application. The collection unit can also estimate the user's emotions and adjust the timing of collecting the health checkup information and questionnaires based on the estimated user emotions. For example, if the user is feeling stressed, the health checkup information and questionnaires can be collected during a time when the user is able to relax. Step 2: The generation unit uses a generation AI to analyze the information collected by the collection unit and create a future avatar. The generation unit uses, for example, a machine learning algorithm to predict future health conditions from past data and create the future avatar. The generation unit can predict future health conditions with high accuracy using, for example, machine learning algorithms such as neural networks and decision trees. When generating the future avatar, the generation unit can also adjust the level of detail of the avatar based on the importance of the health information. For example, a detailed avatar can be generated based on health information with high importance. Step 3: The advice providing unit compares the future avatar generated by the generation unit with the current health information and suggests what lifestyle habits and treatments would have been better in the past. For example, the advice providing unit can compare the future avatar with the current health information and make specific suggestions about what lifestyle habits and treatments would have been better in the past. For example, it can suggest improvements to diet, recommended exercise, changes to treatment methods, etc.

[0055] (Example 2) In an embodiment of the health prediction system of the present invention, a generation AI creates avatars of your future self and your family members based on family health checkup information, periodic questionnaires (lifestyle and thoughts), and treatment history. This health prediction system collects data such as family health checkup information, periodic questionnaires, and treatment history, and the generation AI analyzes this data to create a future avatar. This avatar includes both a "healthy future self" and a "future self in the event of an emergency." Next, the generation AI provides advice on "what lifestyle habits and treatments would have been better in the past" based on current health information. Specifically, the generation AI compares the future avatar with current health information and suggests what lifestyle habits and treatments would have been better in the past. This allows users to predict future health risks and improve their current lifestyle habits and treatments. For example, the generation AI creates an avatar of a "healthy future self" based on family health checkup information and lifestyle questionnaire results, and compares it with current health information to provide advice such as "You should have exercised more in the past." The system also creates an avatar of "your future self in the event of an emergency" and compares it with your current health information to provide advice such as "You should have eaten a more balanced diet in the past." This system allows users to predict future health risks and improve their current lifestyle habits and treatment methods. For example, the generating AI can provide specific advice such as "If you had exercised more in the past, your future health would have been better," encouraging users to review their current lifestyle habits and reduce future health risks. In this way, the health prediction system collects the user's health information, generates a future avatar, and suggests past lifestyle habits and treatment methods, thereby predicting future health risks and improving current lifestyle habits and treatment methods.

[0056] A health prediction system according to an embodiment includes a collection unit, a generation unit, and an advice provision unit. The collection unit collects health checkup information, questionnaires, and treatment histories. The collection unit can collect the health checkup information, questionnaires, and treatment histories, for example, through a dedicated application. The collection unit efficiently collects information from a user using, for example, a smartphone app or a web application. The collection unit can also estimate a user's emotions and adjust the timing of collecting the health checkup information and questionnaires based on the estimated user emotions. For example, if a user is feeling stressed, the health checkup information and questionnaires can be collected during a time when the user is able to relax. The generation unit uses a generation AI to analyze the information collected by the collection unit and create a future avatar. The generation unit predicts a future health state from past data using, for example, a machine learning algorithm and creates the future avatar. The generation unit can predict a future health state with high accuracy using, for example, a machine learning algorithm such as a neural network or a decision tree. When generating the future avatar, the generation unit can also adjust the level of detail of the avatar based on the importance of health information. For example, a detailed avatar can be generated based on health information of high importance. The advice providing unit compares the future avatar generated by the generation unit with current health information and suggests what lifestyle habits and treatments would have been better in the past. The advice providing unit can compare the future avatar with current health information and specifically suggest what lifestyle habits and treatments would have been better in the past. For example, it can suggest dietary improvements, recommended exercise, changes in treatment methods, etc. As a result, the health prediction system according to the embodiment collects the user's health information, generates a future avatar, and suggests past lifestyle habits and treatments, thereby predicting future health risks and improving current lifestyle habits and treatments.

[0057] The collection unit can collect health checkup information, questionnaires, and treatment histories through a dedicated application. Examples of dedicated applications include, but are not limited to, smartphone apps and web applications. The collection unit can collect health checkup information, questionnaires, and treatment histories from a user using a smartphone app. For example, a user inputs health checkup results through the smartphone app, and the collection unit stores the information in a database. The collection unit can also collect information from a user using a web application. For example, a user answers a questionnaire through the web application, and the collection unit analyzes the information. This allows health information to be collected efficiently through the dedicated application. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information entered by a user into AI, which then analyzes the information and stores it in a database.

[0058] The generation unit can use a machine learning algorithm to accurately predict future health conditions from past data and create a future avatar. Examples of machine learning algorithms include, but are not limited to, neural networks and decision trees. The generation unit can, for example, use a neural network to analyze past health checkup data and questionnaire results to predict future health conditions. For example, the generation unit inputs past data, and the neural network outputs the future health conditions. The generation unit can also use a decision tree to predict future health conditions from past data. For example, the generation unit inputs past data, and the decision tree outputs the future health conditions. Furthermore, the generation unit can create future avatars using a machine learning algorithm. For example, the generation unit creates a 3D model or a health condition simulation based on the future health conditions. Using a machine learning algorithm, the future health conditions can be predicted with high accuracy and an avatar can be created. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past data into the generation AI, which can then predict future health conditions and create an avatar.

[0059] The advice providing unit can compare the future avatar with current health information and suggest what lifestyle habits and treatments would have been better in the past. For example, the advice providing unit can compare the future avatar with current health information and specifically suggest what lifestyle habits and treatments would have been better in the past. For example, the advice providing unit can compare the future avatar with current health information and provide advice such as, "You should have exercised more in the past." The advice providing unit can also compare the future avatar with current health information and provide advice such as, "You should have eaten a more balanced diet in the past." Furthermore, the advice providing unit can provide specific advice to help the user improve their current lifestyle habits and treatment methods. For example, the advice providing unit can suggest dietary improvements, recommended exercise, changes in treatment methods, etc. As a result, by comparing the future avatar with current health information, specific suggestions can be made about past lifestyle habits and treatment methods. Some or all of the above-described processing by the advice providing unit may be performed using, or without, AI, for example. For example, the advice provider can input a future avatar and current health information into the AI, allowing the AI ​​to suggest what lifestyle habits and treatments would have been better in the past.

[0060] The generation unit can create avatars for both "a healthy future you" and "a future you in the event of an emergency." The generation unit can create avatars for both "a healthy future you" and "a future you in the event of an emergency," for example, using a machine learning algorithm. For example, the generation unit can analyze past data to create a healthy future avatar. The generation unit can also analyze past data to create a future avatar in the event of an emergency. This allows the creation of future avatars based on different scenarios, making it possible to provide the user with advice from multiple perspectives. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input past data into the generation AI, which can then create both a healthy future avatar and a future avatar in the event of an emergency.

[0061] The advice providing unit can provide specific advice to help the user improve their current lifestyle habits and treatment methods. For example, the advice providing unit can compare a future avatar with current health information and provide specific suggestions about what lifestyle habits and treatments the user should have performed in the past. For example, the advice providing unit can compare a future avatar with current health information and provide advice such as, "You should have exercised more in the past." The advice providing unit can also compare a future avatar with current health information and provide advice such as, "You should have eaten a more balanced diet in the past." Furthermore, the advice providing unit can provide specific advice to help the user improve their current lifestyle habits and treatment methods. For example, the advice providing unit can suggest dietary improvements, recommended exercise, changes in treatment methods, etc. By providing specific advice, the user can actually take specific actions to improve their lifestyle habits and treatment methods. Some or all of the above-described processing by the advice providing unit can be performed using, or without, AI. For example, the advice providing unit can input a future avatar and current health information into AI, which can then suggest what lifestyle habits and treatments the user should have performed in the past.

[0062] The collection unit can estimate the user's emotions and adjust the timing of collecting health checkup information and questionnaires based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting health checkup information and questionnaires based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects health checkup information and questionnaires during a time when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can collect detailed questionnaires to obtain more information. Furthermore, if the user is busy, the collection unit can provide a simple questionnaire that can be answered in a short time. This allows information to be collected at a more appropriate time by adjusting the collection timing 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and adjust the collection timing based on the results.

[0063] The collection unit can analyze the user's past health checkup history and select the optimal collection method. The collection unit can, for example, analyze the user's past health checkup history and select the optimal collection method. For example, the collection unit prioritizes collection of health checkup items that the user has frequently undergone in the past. The collection unit can also focus on collecting items that pose a specific high health risk from the user's past health checkup history. Furthermore, the collection unit can suggest the optimal collection frequency based on the user's past health checkup history. In this way, by analyzing the past health checkup history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input the user's past health checkup history into AI, which can select the optimal collection method.

[0064] The collection unit can filter the health checkup information and questionnaires based on the user's current living situation and areas of interest when collecting the information. For example, the collection unit can filter the health checkup information and questionnaires based on the user's current living situation and areas of interest when collecting the information. For example, the collection unit prioritizes collecting health checkup items that are highly relevant to the user's current living situation. The collection unit can also collect specific health information based on the user's areas of interest. Furthermore, the collection unit can provide customized questionnaires based on the user's living situation and areas of interest. This allows more relevant information to be collected by filtering the information based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI, which can then perform the filtering.

[0065] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related health information. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting detailed health information. Furthermore, if the user is busy, the collection unit can prioritize collecting health information of high importance. By determining the priority of information according to the user's emotions, more important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of information to be collected based on the result.

[0066] The collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting health checkup information or questionnaires. For example, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting health checkup information or questionnaires. For example, the collection unit collects relevant health information based on health risks in the area where the user lives. The collection unit can also collect information on health issues specific to the area based on the user's geographical location information. Furthermore, the collection unit can suggest optimal health checkup items taking the user's geographical location information into consideration. This makes it possible to address health risks specific to the area by collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant information.

[0067] The collection unit can analyze the user's social media activities and collect related information when collecting health checkup information or a questionnaire. For example, the collection unit can analyze the user's social media activities and collect related information when collecting health checkup information or a questionnaire. For example, the collection unit can identify health concerns from the user's social media activities and collect related information. The collection unit can also prioritize collection of items with high health risks based on the user's social media activities. Furthermore, the collection unit can analyze the user's social media activities and suggest optimal health checkup items. In this way, health concerns can be identified and related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related information.

[0068] The generation unit can estimate the user's emotions and adjust the expression style of the future avatar based on the estimated user emotions. The generation unit can, for example, estimate the user's emotions and adjust the expression style of the future avatar based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an avatar with a calm expression. Furthermore, if the user is stressed, the generation unit can generate an avatar with a relaxed posture. Furthermore, if the user is excited, the generation unit can generate an avatar with active movements. This allows for the generation of a more realistic avatar by adjusting the expression style of the avatar 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 generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the expression style of the avatar based on the user's emotions.

[0069] The generation unit can adjust the level of detail of the avatar based on the importance of the health information when generating a future avatar. For example, the generation unit can adjust the level of detail of the avatar based on the importance of the health information when generating a future avatar. For example, the generation unit can generate a detailed avatar based on health information with a high level of importance. The generation unit can also generate a simplified avatar based on health information with a low level of importance. Furthermore, the generation unit can adjust the representation method of the avatar based on the importance of the health information. By adjusting the level of detail of the avatar based on the importance of the health information, an avatar that reflects more important information can be generated. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input health information importance data into the generation AI, which can then adjust the level of detail of the avatar.

[0070] The generation unit can apply different generation algorithms depending on the category of health information when generating a future avatar. For example, the generation unit can apply different generation algorithms depending on the category of health information when generating a future avatar. For example, the generation unit can generate a detailed avatar based on physical health information. The generation unit can also generate an avatar that emphasizes facial expressions and posture based on mental health information. Furthermore, the generation unit can generate an avatar that recreates a lifestyle scene based on lifestyle information. In this way, by applying different generation algorithms depending on the category of health information, a more appropriate avatar can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input health information category data into the generation AI, and the generation AI can generate an avatar by applying different generation algorithms.

[0071] The generation unit can estimate the user's emotion and adjust the length of the future avatar based on the estimated user emotion. The generation unit can, for example, estimate the user's emotion and adjust the length of the future avatar based on the estimated user emotion. For example, the generation unit can generate a longer avatar if the user is relaxed. The generation unit can also generate a shorter avatar if the user is in a hurry. Furthermore, the generation unit can generate an avatar with a visually stimulating effect if the user is excited. This allows for the generation of a more appropriate avatar by adjusting the length of the avatar according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the avatar based on the user's emotion.

[0072] The generation unit can determine the priority of avatars based on the time of submission of health information when generating future avatars. For example, the generation unit can determine the priority of avatars based on the time of submission of health information when generating future avatars. For example, the generation unit can prioritize avatars based on the most recently submitted health information. The generation unit can also lower the priority of avatars based on health information that was submitted the oldest. Furthermore, the generation unit can adjust the order in which avatars are generated based on the time of submission of health information. By determining the priority of avatars based on the time of submission of health information, avatars that reflect the most recent information can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the time of submission of health information into the generation AI, which can then determine the priority of avatars.

[0073] The generation unit can adjust the order of avatars based on the relevance of health information when generating future avatars. For example, the generation unit can adjust the order of avatars based on the relevance of health information when generating future avatars. For example, the generation unit can prioritize generating avatars based on highly relevant health information. The generation unit can also lower the order of avatar generation based on less relevant health information. Furthermore, the generation unit can adjust the order of avatar generation according to the relevance of health information. In this way, by adjusting the order of avatars based on the relevance of health information, more relevant avatars can be prioritized for generation. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input health information relevance data into the generation AI, and the generation AI can adjust the order of avatars.

[0074] The advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The advice providing unit can, for example, estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is relaxed, the advice providing unit can provide advice in a calm tone. Furthermore, if the user is stressed, the advice providing unit can provide concise and easy-to-understand advice. Furthermore, if the user is excited, the advice providing unit can provide advice in a positive tone. This allows for more effective advice to be provided by adjusting the way the advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice providing unit can input user's emotion data into the generation AI, which can then adjust the way the advice is expressed based on the user's emotions.

[0075] The advice providing unit can adjust the level of detail of the advice based on the importance of the health information when providing the advice. The advice providing unit can, for example, adjust the level of detail of the advice based on the importance of the health information when providing the advice. For example, the advice providing unit can provide detailed advice based on health information of high importance. The advice providing unit can also provide simplified advice based on health information of low importance. Furthermore, the advice providing unit can adjust the level of detail of the advice according to the importance of the health information. In this way, by adjusting the level of detail of the advice according to the importance of the health information, it is possible to provide advice based on more important information. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input health information importance data to AI, and the AI ​​can adjust the level of detail of the advice.

[0076] The advice providing unit can apply different advice algorithms depending on the category of health information when providing advice. The advice providing unit can apply different advice algorithms depending on the category of health information when providing advice. For example, the advice providing unit can provide advice regarding exercise and diet based on physical health information. The advice providing unit can also provide advice regarding stress management and relaxation based on mental health information. Furthermore, the advice providing unit can provide advice regarding improving daily life based on lifestyle habit information. In this way, by applying different advice algorithms depending on the category of health information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice providing unit can input health information category data into AI, and the AI ​​can provide advice by applying different advice algorithms.

[0077] The advice providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The advice providing unit can, for example, estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, the advice providing unit can provide detailed advice when the user is relaxed. Furthermore, the advice providing unit can provide short, to-the-point advice when the user is in a hurry. Furthermore, the advice providing unit can provide advice with visually stimulating effects when the user is excited. This allows the length of advice to be adjusted according to the user's emotions, thereby providing advice of a more appropriate length. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice providing unit can input user's emotion data into the generation AI, which can then adjust the length of the advice based on the user's emotions.

[0078] The advice providing unit can determine the priority of advice based on the time of submission of the health information when providing the advice. The advice providing unit can determine the priority of advice based on the time of submission of the health information when providing the advice, for example. For example, the advice providing unit can preferentially provide advice based on the most recently submitted health information. The advice providing unit can also lower the priority of advice based on the health information that was submitted the oldest. Furthermore, the advice providing unit can adjust the order in which advice is provided depending on the time of submission of the health information. In this way, by determining the priority of advice based on the time of submission of the health information, advice based on the most recent information can be provided. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input data on the time of submission of health information into AI, and the AI ​​can determine the priority of advice.

[0079] The advice providing unit can adjust the order of advice based on the relevance of the health information when providing advice. The advice providing unit can, for example, adjust the order of advice based on the relevance of the health information when providing advice. For example, the advice providing unit can prioritize providing advice based on highly relevant health information. The advice providing unit can also lower the order of providing advice based on less relevant health information. Furthermore, the advice providing unit can adjust the order of providing advice according to the relevance of the health information. In this way, by adjusting the order of advice based on the relevance of the health information, more relevant advice can be provided preferentially. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI, for example. For example, the advice providing unit can input health information relevance data into AI, and the AI ​​can adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and advice provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects health checkup information, questionnaires, and treatment history using the camera 42 and microphone 38B of the smart device 14, estimates the user's emotions using the control unit 46A, and adjusts the collection timing. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and creates a future avatar. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the generated future avatar with current health information and suggests past lifestyle habits and treatment methods. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, and advice provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects health checkup information, questionnaires, and treatment history using the camera 42 and microphone 238 of the smart glasses 214, estimates the user's emotions using the control unit 46A, and adjusts the collection timing. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and creates a future avatar. The advice provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, compares the generated future avatar with current health information, and suggests past lifestyle habits and treatment methods. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, and advice provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects health checkup information, questionnaires, and treatment history using the camera 42 and microphone 238 of the headset-type terminal 314, estimates the user's emotions using the control unit 46A, and adjusts the collection timing. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and creates a future avatar. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the generated future avatar with current health information and suggests past lifestyle habits and treatment methods. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, and advice provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health checkup information, questionnaires, and treatment history using the camera 42 and microphone 238 of the robot 414, estimates the user's emotions using the control unit 46A, and adjusts the collection timing. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and creates a future avatar. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the generated future avatar with current health information and suggests past lifestyle habits and treatment methods.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The health prediction system may further include a sleep analysis unit that monitors the user's sleep patterns. The sleep analysis unit collects the user's sleep data and analyzes the quality and patterns of sleep. For example, the sleep analysis unit may record the user's sleep time, the ratio of deep sleep to light sleep, the number of awakenings during the night, etc., and evaluate the quality of sleep based on this data. Furthermore, the sleep analysis unit may suggest ways to improve sleep based on the user's sleep data. For example, it may provide advice on how to relax before bed or on creating an appropriate sleeping environment. This allows the user to improve their sleep quality and their overall health.

[0082] The health prediction system may further include a dietary recorder that collects the user's dietary data. The dietary recorder records the user's daily dietary intake and analyzes the nutritional balance. For example, the dietary recorder may record the calories, proteins, fats, carbohydrates, vitamins, minerals, and other nutrients ingested by the user, and evaluate the nutritional balance based on this data. Furthermore, the dietary recorder may suggest ways to improve the nutritional balance based on the user's dietary data. For example, if a specific nutrient is lacking, the dietary recorder may advise the user to consume foods containing that nutrient. This allows the user to review their dietary intake and achieve a healthier diet.

[0083] The health prediction system may further include an exercise recording unit that collects the user's exercise data. The exercise recording unit records the type, duration, intensity, etc. of exercise performed by the user and analyzes the effects of the exercise. For example, the exercise recording unit can collect exercise data such as walking, running, and strength training performed by the user and evaluate the effects of the exercise based on this data. Furthermore, the exercise recording unit can also suggest ways to improve the user's exercise based on the user's exercise data. For example, if a specific exercise is lacking, the exercise recording unit can advise the user to add that exercise. This allows the user to review their exercise habits and live a healthier life.

[0084] The health prediction system may further include a stress analysis unit that monitors the user's stress level. The stress analysis unit collects biometric data such as the user's heart rate, respiratory rate, and electrodermal activity, and analyzes the stress level. For example, the stress analysis unit may record fluctuations in the user's heart rate and respiratory rate, and evaluate the stress level based on this data. Furthermore, the stress analysis unit may suggest stress reduction methods based on the user's stress data. For example, the stress analysis unit may advise on relaxation techniques and stress management techniques. This allows the user to manage their own stress level and maintain their physical and mental health.

[0085] The health prediction system can also estimate the user's emotions and adjust the content of advice based on the estimated emotions. For example, if the user is feeling stressed, the system can provide advice on relaxation methods and stress management. If the user is relaxed, the system can suggest ways to maintain a healthy lifestyle. Furthermore, if the user is excited, the system can provide advice on exercises and activities to effectively utilize energy. This allows for more effective health management by providing appropriate advice according to the user's emotions.

[0086] The health prediction system can further estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is relaxed, an avatar with a calm expression can be generated. If the user is stressed, an avatar with a relaxed posture can be generated. Furthermore, if the user is excited, an avatar with active movements can be generated. In this way, by adjusting the avatar's expression according to the user's emotions, more realistic avatars can be generated.

[0087] The health prediction system can further estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, advice can be provided in a calm tone. If the user is stressed, advice can be provided in a concise and easy-to-understand tone. Furthermore, if the user is excited, advice can be provided in a more aggressive tone. In this way, more effective advice can be provided by adjusting the way advice is expressed depending on the user's emotions.

[0088] The health prediction system can further estimate the user's emotions and adjust the length of advice based on the estimated emotions. For example, if the user is relaxed, detailed advice can be provided. If the user is in a hurry, short and to the point advice can be provided. Furthermore, if the user is excited, advice with visually stimulating effects can be provided. In this way, by adjusting the length of advice according to the user's emotions, more appropriate advice can be provided.

[0089] The health prediction system can further estimate the user's emotions and prioritize the information to be collected based on the estimated emotions. For example, if the user is feeling stressed, stress-related health information can be collected with priority. If the user is relaxed, detailed health information can be collected with priority. Furthermore, if the user is busy, health information of high importance can be collected with priority. Thus, by prioritizing information according to the user's emotions, more important information can be collected with priority.

[0090] The health prediction system can also prioritize the collection of highly relevant information based on the user's geographical location information. For example, it can collect relevant health information based on the health risks in the area where the user lives. It can also collect information on health issues specific to the area based on the user's geographical location information. It can also suggest optimal health checkup items taking the user's geographical location information into consideration. This allows the system to address health risks specific to the area by collecting highly relevant information based on the user's geographical location information.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection unit collects health checkup information or questionnaires and treatment history. The collection unit can collect the health checkup information, questionnaires, and treatment history, for example, through a dedicated application. The collection unit can efficiently collect information from the user, for example, using a smartphone app or web application. The collection unit can also estimate the user's emotions and adjust the timing of collecting the health checkup information and questionnaires based on the estimated user emotions. For example, if the user is feeling stressed, the health checkup information and questionnaires can be collected during a time when the user is able to relax. Step 2: The generation unit uses a generation AI to analyze the information collected by the collection unit and create a future avatar. The generation unit uses, for example, a machine learning algorithm to predict future health conditions from past data and create the future avatar. The generation unit can predict future health conditions with high accuracy using, for example, machine learning algorithms such as neural networks and decision trees. When generating the future avatar, the generation unit can also adjust the level of detail of the avatar based on the importance of the health information. For example, a detailed avatar can be generated based on health information with high importance. Step 3: The advice providing unit compares the future avatar generated by the generation unit with the current health information and suggests what lifestyle habits and treatments would have been better in the past. For example, the advice providing unit can compare the future avatar with the current health information and make specific suggestions about what lifestyle habits and treatments would have been better in the past. For example, it can suggest improvements to diet, recommended exercise, changes to treatment methods, etc.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 for collecting health checkup information or questionnaires and medical history; a generation unit that analyzes the information collected by the collection unit and creates a future avatar; and an advice providing unit that compares the future avatar generated by the generation unit with current health information and suggests what lifestyle habits and treatments would have been appropriate in the past. A system characterized by:

2. The collecting unit Collecting medical examination information, questionnaires, and medical history through a dedicated application 2. The system of claim 1.

3. The generation unit Uses machine learning algorithms to accurately predict future health status from past data and create future avatars 2. The system of claim 1.

4. The advice providing unit Comparing a future avatar with current health information and suggesting what lifestyle habits and treatments would have been better in the past 2. The system of claim 1.

5. The generation unit Create avatars for both your "healthy future self" and your "future self in the event of an emergency" 2. The system of claim 1.

6. The advice providing unit Providing specific advice to help users improve their current lifestyle and treatment regimes 2. The system of claim 1.

7. The collecting unit Estimates user emotions and adjusts the timing of collecting health checkup information and questionnaires based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past health checkup history and select the optimal collection method 2. The system of claim 1.

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