System

Abstract

An object of a system according to an embodiment is to make it possible to intuitively grasp a cause of a body shape change or a weight increase of a user.SOLUTION: A system includes a registration part, an input part, a simulation part, a calorie input part, a prediction part, a linkage part, and an analysis part. The registration unit registers a height, a weight, and a photograph of a user. The input unit measures and inputs a daily weight. The simulation unit simulates a change in appearance based on the data collected by the registration unit and the input unit. The calorie input section is used to input daily intake calories or nutrients. The prediction unit predicts a body shape after one year based on the data collected by the calorie input unit. The interlocking unit interlocks with the genetic test result. The analysis unit analyzes the cause of the weight gain.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to intuitively grasp the causes of changes in a user's body shape or weight gain.

[0005] The system according to the embodiment aims to enable a user to intuitively understand the causes of changes in body shape and weight gain. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an input unit, a simulation unit, a calorie input unit, a prediction unit, a linking unit, and an analysis unit. The registration unit registers the user's height, weight, and photo. The input unit measures and inputs the user's weight every day. The simulation unit simulates changes in appearance based on data collected by the registration unit and input unit. The calorie input unit inputs the user's daily calorie or nutrient intake. The prediction unit predicts the user's body shape one year from now based on the data collected by the calorie input unit. The linking unit links with genetic test results. The analysis unit analyzes the cause of weight gain. [Effects of the Invention]

[0007] The system according to the embodiment can enable the user to intuitively understand the causes of changes in body shape and weight gain. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows users to register their current height, weight, and photo, and then measure their weight daily to see changes in their physical appearance. Furthermore, it can simulate what they would look like if they gained or lost weight. By inputting their daily calorie and nutrient intake, users can see what their body shape will be one year from now if they continue their current diet. This system is useful for preventing weight gain and increasing motivation to diet. It also has a function that links with genetic testing results to suggest diet methods, daily meals, exercise, and supplements tailored to the user. It can also tell users whether weight gain is due to simple swelling or fat. For example, a user registers their current height, weight, and photo in the system. Next, they measure their weight daily and input it into the system. The system uses this data to display changes in the user's physical appearance. For example, it can simulate what the user would look like if they gained or lost weight. Furthermore, users input their daily calorie and nutrient intake into the system. Based on this data, the system predicts what their body shape will be one year from now if they continue their current diet. This helps users control weight gain and motivate them to diet. The system also works with genetic test results. When a user inputs their genetic test results into the system, the system suggests diet methods, daily meals, exercise, and supplements tailored to the user. For example, it can suggest what diet is effective based on a specific genetic type. The system also has the ability to analyze the cause of weight gain. For example, it can tell users whether weight gain is simply due to swelling or fat. This allows users to take appropriate measures. This makes the system a powerful tool for users to maintain a healthy body shape. For example, if a user wants to lose weight, the system can suggest appropriate diet and exercise methods, increasing their motivation. If a user wants to gain weight, the system can suggest effective meals and supplements to support healthy weight gain.

[0029] A health management system according to an embodiment includes a registration unit, an input unit, a simulation unit, a calorie input unit, a prediction unit, a linking unit, and an analysis unit. The registration unit registers a user's height, weight, and photo. For example, the user inputs their height in centimeters and their weight in kilograms. They can also take and register full-body and face photos. The input unit measures and inputs the user's weight every day. For example, the user can weigh themselves every morning and input that data into the system. The simulation unit simulates changes in appearance based on data collected by the registration unit and the input unit. For example, it can simulate how the user will look if they gain or lose weight. The calorie input unit inputs daily calorie intake and nutrients. For example, the user inputs the calories and nutrients of their meals, which the system records. The prediction unit predicts the user's body shape one year from now based on the data collected by the calorie input unit. For example, it can predict the user's body shape one year from now if they continue their current diet. The linking unit links with genetic test results. For example, when a user inputs the results of a genetic test into the system, the system will suggest a diet method, daily meals, exercise, supplements, etc. that are suited to the user. The analysis unit analyzes the cause of weight gain. For example, it will tell the user whether the weight gain is simply due to swelling or fat. As a result, the health management system according to the embodiment can predict changes in appearance and body shape, suggest diet methods, and analyze the causes of weight gain based on data such as the user's height, weight, photo, daily weight, calorie intake and nutrients, and genetic test results.

[0030] The simulation unit can simulate how the user will look if they gain or lose weight. For example, the simulation unit simulates how the user will look if they gain weight. For example, the simulation unit can display how the user will look if their weight increases by 5 kilograms. The simulation unit can also simulate how the user will look if they lose weight. For example, the simulation unit can display how the user will look if their weight decreases by 5 kilograms. The simulation unit can also compare how the user will look if they gain or lose weight. For example, the simulation unit can display how the user will look if their weight increases and decreases side by side. This allows the user to visually confirm changes in their body shape.

[0031] The prediction unit can predict what body shape the user will have in one year if they continue their current diet. For example, the prediction unit predicts the user's body shape in one year if they continue their current diet. For example, the prediction unit can predict the user's body fat percentage and muscle mass in one year based on the user's current calorie intake and nutrients. The prediction unit can also predict the user's weight in one year based on the user's current diet. For example, the prediction unit can display the user's weight in one year if they continue their current diet. The prediction unit can also predict changes in the user's body shape in one year based on the user's current diet. For example, the prediction unit can display a graph of changes in the user's body shape in one year if the user continues their current diet. This can increase the user's motivation to improve their eating habits and diet.

[0032] The interlocking unit can suggest a diet method, daily meals, exercise, supplements, etc., tailored to the user based on the results of the genetic test. For example, when the user inputs the results of the genetic test into the system, the interlocking unit suggests a diet method tailored to the user. For example, the interlocking unit can suggest what kind of meal is effective based on a specific genetic type. The interlocking unit can also suggest a daily meal tailored to the user. For example, the interlocking unit can suggest what ingredients should be consumed based on the user's genetic type. The interlocking unit can also suggest an exercise method tailored to the user. For example, the interlocking unit can suggest what kind of exercise is effective based on the user's genetic type. The interlocking unit can also suggest supplements tailored to the user. For example, the interlocking unit can suggest what kind of supplements should be taken based on the user's genetic type. This allows the interlocking unit to provide a personalized diet plan.

[0033] The analysis unit can analyze whether the weight gain is due to simple swelling or fat. The analysis unit, for example, analyzes the cause of the user's weight gain. For example, the analysis unit can analyze whether the weight gain is due to simple swelling. The analysis unit can also analyze whether the weight gain is due to fat. For example, the analysis unit can analyze whether the weight gain is due to fat gain. The analysis unit can also analyze the cause of the weight gain in detail. For example, the analysis unit can analyze whether the weight gain is due to diet or lack of exercise. This allows the user to take appropriate measures.

[0034] The registration unit can analyze the user's past registration data and select an appropriate registration method. For example, the registration unit can preferentially suggest registration methods (voice, text, etc.) that the user has frequently used in the past. For example, the registration unit can send a notification encouraging registration at a specific time period based on the user's past registration data. The registration unit can also analyze the user's past registration history and suggest the most efficient registration method. For example, the registration unit can analyze the user's past registration history and suggest the most efficient registration method. In this way, the optimal registration method can be suggested by analyzing the user's past registration data.

[0035] The registration unit can perform filtering based on the user's current health condition and lifestyle habits at the time of registration. The registration unit can suggest appropriate registration items based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the registration unit can customize the registration items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The registration unit can also filter information required at the time of registration based on the user's health condition and lifestyle habits. For example, the registration unit can filter information required at the time of registration based on the user's health condition and lifestyle habits. In this way, appropriate registration items can be suggested by filtering based on the user's current health condition and lifestyle habits.

[0036] At the time of registration, the registration unit can select an appropriate registration means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the registration unit can provide voice input preferentially. For example, if the user prefers text input, the registration unit can provide text input preferentially. Furthermore, if the user prefers image input, the registration unit can also provide image input preferentially. For example, if the user prefers image input, the registration unit can provide image input preferentially. This allows the registration process to be carried out smoothly by selecting the optimal registration means according to the user's input method.

[0037] The input unit can analyze the user's past input data and select an appropriate input method. For example, the input unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. For example, the input unit can send a notification prompting input at a specific time period based on the user's past input data. The input unit can also analyze the user's past input history and suggest the most efficient input method. For example, the input unit can analyze the user's past input history and suggest the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past input data.

[0038] The input unit can perform filtering based on the user's current health condition and lifestyle habits at the time of input. The input unit can suggest appropriate input items based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the input unit can customize the input items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The input unit can also filter information required at the time of input based on the user's health condition and lifestyle habits. For example, the input unit can filter information required at the time of input based on the user's health condition and lifestyle habits. In this way, appropriate input items can be suggested by filtering based on the user's current health condition and lifestyle habits.

[0039] The input unit can select an appropriate input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user prefers voice input, the input unit can provide voice input preferentially. For example, if the user prefers text input, the input unit can provide text input preferentially. Furthermore, if the user prefers image input, the input unit can also provide image input preferentially. For example, if the user prefers image input, the input unit can provide image input preferentially. This allows input work to be performed smoothly by selecting the optimal input means depending on the user's input method.

[0040] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past weight fluctuation data. The simulation unit improves the accuracy of the simulation, for example, based on the user's past weight fluctuation data. For example, the simulation unit can analyze the user's past weight fluctuation patterns to improve the accuracy of the simulation. The simulation unit can also provide optimal simulation results by referring to the user's past weight fluctuation data. For example, the simulation unit can provide optimal simulation results by referring to the user's past weight fluctuation data. In this way, the accuracy of the simulation is improved by referring to the user's past weight fluctuation data.

[0041] During the simulation, the simulation unit can adjust the simulation parameters based on the user's health condition and lifestyle habits. The simulation unit adjusts the simulation parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the simulation unit can adjust the simulation parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The simulation unit can also optimize the simulation parameters based on the user's health condition and lifestyle habits. For example, the simulation unit can optimize the simulation parameters based on the user's health condition and lifestyle habits. In this way, by adjusting the simulation parameters based on the user's health condition and lifestyle habits, more accurate simulation results can be provided.

[0042] The simulation unit can improve the simulation method by reflecting user feedback during the simulation. The simulation unit improves the simulation method, for example, based on feedback provided by the user. For example, the simulation unit can propose an optimal simulation method based on the user feedback. The simulation unit can also improve the simulation interface by reflecting user feedback. For example, the simulation unit can improve the simulation interface by reflecting user feedback. In this way, by reflecting user feedback, the simulation method can be improved and simulation results that are more suitable for the user can be provided.

[0043] The calorie input unit can analyze the user's past calorie input data and select an appropriate input method. For example, the calorie input unit can prioritize and suggest calorie input methods (voice, text, etc.) that the user has frequently used in the past. For example, the calorie input unit can send a notification prompting input during a specific time period based on the user's past calorie input data. The calorie input unit can also analyze the user's past calorie input history and suggest the most efficient input method. For example, the calorie input unit can analyze the user's past calorie input history and suggest the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past calorie input data.

[0044] The calorie input unit can perform filtering based on the user's current dietary content and lifestyle habits when entering calories. The calorie input unit can suggest appropriate calorie input items based on, for example, the user's current dietary content (ingredients, cooking method, etc.). For example, the calorie input unit can customize the calorie input items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The calorie input unit can also filter information required when entering calories based on the user's dietary content and lifestyle habits. For example, the calorie input unit can filter information required when entering calories based on the user's dietary content and lifestyle habits. In this way, appropriate calorie input items can be suggested by filtering based on the user's current dietary content and lifestyle habits.

[0045] When inputting calories, the calorie input unit can select an appropriate input means depending on the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the calorie input unit can provide voice input preferentially. For example, if the user prefers text input, the calorie input unit can provide text input preferentially. Furthermore, if the user prefers image input, the calorie input unit can also provide image input preferentially. For example, if the user prefers image input, the calorie input unit can provide image input preferentially. This allows the user to smoothly input calories by selecting the optimal input means depending on their input method.

[0046] The prediction unit can improve the accuracy of the prediction by referring to the user's past weight fluctuation data when making a prediction. The prediction unit improves the accuracy of the prediction, for example, based on the user's past weight fluctuation data. For example, the prediction unit can analyze the user's past weight fluctuation patterns to improve the accuracy of the prediction. The prediction unit can also provide an optimal prediction result by referring to the user's past weight fluctuation data. For example, the prediction unit can provide an optimal prediction result by referring to the user's past weight fluctuation data. In this way, the accuracy of the prediction is improved by referring to the user's past weight fluctuation data.

[0047] The prediction unit can adjust prediction parameters based on the user's health condition and lifestyle habits when making predictions. The prediction unit adjusts the prediction parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the prediction unit can adjust the prediction parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The prediction unit can also optimize the prediction parameters based on the user's health condition and lifestyle habits. For example, the prediction unit can optimize the prediction parameters based on the user's health condition and lifestyle habits. In this way, by adjusting the prediction parameters based on the user's health condition and lifestyle habits, more accurate prediction results can be provided.

[0048] The prediction unit can improve the prediction method by reflecting user feedback during prediction. The prediction unit improves the prediction method, for example, based on feedback provided by the user. For example, the prediction unit can propose an optimal prediction method based on the user feedback. The prediction unit can also improve the prediction interface by reflecting user feedback. For example, the prediction unit can improve the prediction interface by reflecting user feedback. In this way, by reflecting user feedback, the prediction method can be improved and prediction results that are more suitable for the user can be provided.

[0049] During linking, the interlocking unit can improve the accuracy of linking by referring to the user's past genetic test data. The interlocking unit, for example, improves the accuracy of linking based on the user's past genetic test data. For example, the interlocking unit can analyze the user's past genetic test results and improve the accuracy of linking. The interlocking unit can also provide optimal linking results by referring to the user's past genetic test data. For example, the interlocking unit can provide optimal linking results by referring to the user's past genetic test data. In this way, the accuracy of linking is improved by referring to the user's past genetic test data.

[0050] During linkage, the linking unit can adjust linkage parameters based on the user's health condition and lifestyle habits. The linking unit adjusts linkage parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the linking unit can adjust linkage parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The linking unit can also optimize linkage parameters based on the user's health condition and lifestyle habits. For example, the linking unit can optimize linkage parameters based on the user's health condition and lifestyle habits. This makes it possible to provide more accurate linkage results by adjusting linkage parameters based on the user's health condition and lifestyle habits.

[0051] The interlocking unit can improve the interlocking method by reflecting user feedback during interlocking. The interlocking unit improves the interlocking method, for example, based on feedback provided by the user. For example, the interlocking unit can propose an optimal interlocking method based on user feedback. The interlocking unit can also improve the interlocking interface by reflecting user feedback. For example, the interlocking unit can improve the interlocking interface by reflecting user feedback. In this way, the interlocking method can be improved by reflecting user feedback, and interlocking results that are more suitable for the user can be provided.

[0052] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past weight fluctuation data. The analysis unit improves the accuracy of the analysis, for example, based on the user's past weight fluctuation data. For example, the analysis unit can analyze the user's past weight fluctuation patterns to improve the accuracy of the analysis. The analysis unit can also provide optimal analysis results by referring to the user's past weight fluctuation data. For example, the analysis unit can provide optimal analysis results by referring to the user's past weight fluctuation data. In this way, the accuracy of the analysis is improved by referring to the user's past weight fluctuation data.

[0053] During analysis, the analysis unit can adjust the analysis parameters based on the user's health condition and lifestyle habits. The analysis unit adjusts the analysis parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the analysis unit can adjust the analysis parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The analysis unit can also optimize the analysis parameters based on the user's health condition and lifestyle habits. For example, the analysis unit can optimize the analysis parameters based on the user's health condition and lifestyle habits. In this way, by adjusting the analysis parameters based on the user's health condition and lifestyle habits, more accurate analysis results can be provided.

[0054] The analysis unit can improve the analysis method by reflecting user feedback during analysis. The analysis unit improves the analysis method, for example, based on feedback provided by the user. For example, the analysis unit can suggest an optimal analysis method based on user feedback. The analysis unit can also improve the analysis interface by reflecting user feedback. For example, the analysis unit can improve the analysis interface by reflecting user feedback. In this way, by reflecting user feedback, the analysis method can be improved and analysis results that are more suitable for the user can be provided.

[0055] During analysis, the analysis unit can adjust analysis parameters based on the user's geographical location information. For example, when the user is in a specific region, the analysis unit can adjust the analysis parameters taking into account the climate and environment of that region. For example, when the user is traveling, the analysis unit can adjust the analysis parameters taking into account the environment of the travel destination. Furthermore, when the user is at home, the analysis unit can also adjust the analysis parameters taking into account the home environment. For example, when the user is at home, the analysis unit can adjust the analysis parameters taking into account the home environment. In this way, by taking into account the user's geographical location information, more accurate analysis results can be provided.

[0056] During the analysis, the analysis unit can analyze the user's social media activities and incorporate related data into the analysis. For example, the analysis unit can reflect the meal details shared by the user on social media in the analysis. For example, the analysis unit can analyze the content posted by the user on social media and incorporate related data into the analysis. The analysis unit can also refer to the activities of the user's friends on social media and incorporate related data into the analysis. For example, the analysis unit can refer to the activities of the user's friends on social media and incorporate related data into the analysis. In this way, by analyzing the user's social media activities, related data can be incorporated into the analysis, making it possible to provide more accurate analysis results.

[0057] During analysis, the analysis unit can adjust the analysis method by reflecting the user's past feedback. The analysis unit can improve the analysis method, for example, based on feedback provided by the user in the past. For example, the analysis unit can suggest an optimal analysis method based on the user's past feedback. The analysis unit can also customize the analysis interface by reflecting the user's feedback. For example, the analysis unit can customize the analysis interface by reflecting the user's past feedback. In this way, the analysis method can be customized by reflecting the user's past feedback, making it possible to provide analysis results that are more suitable for the user.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The input unit can analyze the user's past input data and select an appropriate input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also send notifications prompting input at specific times based on the user's past input data. It can also analyze the user's past input history and suggest the most efficient input method. This makes it possible to suggest the optimal input method by analyzing the user's past input data.

[0060] The calorie input unit can perform filtering based on the user's current dietary content and lifestyle habits. For example, it can suggest appropriate calorie input items based on the user's current dietary content (ingredients, cooking method, etc.). It can also customize calorie input items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). It can also filter information required when entering calories based on the user's dietary content and lifestyle habits. This makes it possible to suggest appropriate calorie input items by filtering based on the user's current dietary content and lifestyle habits.

[0061] The linking unit can improve the accuracy of linking by referring to the user's past genetic test data. For example, the accuracy of linking can be improved based on the user's past genetic test data. The accuracy of linking can also be improved by analyzing the user's past genetic test results. Furthermore, the optimal linking results can be provided by referring to the user's past genetic test data. In this way, the accuracy of linking is improved by referring to the user's past genetic test data.

[0062] The analysis unit can improve the accuracy of the analysis by referring to the user's past weight fluctuation data. For example, the accuracy of the analysis can be improved based on the user's past weight fluctuation data. The accuracy of the analysis can also be improved by analyzing the user's past weight fluctuation patterns. Furthermore, the analysis unit can provide optimal analysis results by referring to the user's past weight fluctuation data. Thus, the accuracy of the analysis can be improved by referring to the user's past weight fluctuation data.

[0063] During the simulation, the simulation unit can adjust the simulation parameters based on the user's health condition and lifestyle habits. For example, the simulation parameters can be adjusted based on the user's current health condition (weight, height, BMI, etc.). The simulation parameters can also be adjusted taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). Furthermore, the simulation parameters can be optimized based on the user's health condition and lifestyle habits. This makes it possible to provide more accurate simulation results by adjusting the simulation parameters based on the user's health condition and lifestyle habits.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The registration unit registers the user's height, weight, and photo. For example, the user enters their height in centimeters and their weight in kilograms. They can also take and register full-body and face photos. Step 2: The input unit measures and inputs the user's weight every day. For example, the user can measure their weight every morning and input the data into the system. Step 3: The simulation unit simulates changes in appearance based on the data collected by the registration unit and the input unit. For example, it can simulate how the user will look if they gain or lose weight. Step 4: The calorie input section inputs the daily calorie intake and nutrients. For example, the user inputs the calories and nutrients of the food they eat, and the system records them. Step 5: The prediction unit predicts your body shape one year from now based on the data collected by the calorie input unit. For example, it can predict what your body shape will be one year from now if you continue eating the same way. Step 6: The linking unit links with the genetic test results. For example, when a user inputs the genetic test results into the system, the system will suggest diet methods, daily meals, exercise, supplements, etc. that are suitable for the user. Step 7: The analysis section will analyze the cause of the weight gain, for example, whether the weight gain is due to simple swelling or fat.

[0066] (Example 2) A system according to an embodiment of the present invention allows users to register their current height, weight, and photo, and then measure their weight daily to see changes in their physical appearance. Furthermore, it can simulate what they would look like if they gained or lost weight. By inputting their daily calorie and nutrient intake, users can see what their body shape will be one year from now if they continue their current diet. This system is useful for preventing weight gain and increasing motivation to diet. It also has a function that links with genetic testing results to suggest diet methods, daily meals, exercise, and supplements tailored to the user. It can also tell users whether weight gain is due to simple swelling or fat. For example, a user registers their current height, weight, and photo in the system. Next, they measure their weight daily and input it into the system. The system uses this data to display changes in the user's physical appearance. For example, it can simulate what the user would look like if they gained or lost weight. Furthermore, users input their daily calorie and nutrient intake into the system. Based on this data, the system predicts what their body shape will be one year from now if they continue their current diet. This helps users control weight gain and motivate them to diet. The system also works with genetic test results. When a user inputs their genetic test results into the system, the system suggests diet methods, daily meals, exercise, and supplements tailored to the user. For example, it can suggest what diet is effective based on a specific genetic type. The system also has the ability to analyze the cause of weight gain. For example, it can tell users whether weight gain is simply due to swelling or fat. This allows users to take appropriate measures. This makes the system a powerful tool for users to maintain a healthy body shape. For example, if a user wants to lose weight, the system can suggest appropriate diet and exercise methods, increasing their motivation. If a user wants to gain weight, the system can suggest effective meals and supplements to support healthy weight gain.

[0067] A health management system according to an embodiment includes a registration unit, an input unit, a simulation unit, a calorie input unit, a prediction unit, a linking unit, and an analysis unit. The registration unit registers a user's height, weight, and photo. For example, the user inputs their height in centimeters and their weight in kilograms. They can also take and register full-body and face photos. The input unit measures and inputs the user's weight every day. For example, the user can weigh themselves every morning and input that data into the system. The simulation unit simulates changes in appearance based on data collected by the registration unit and the input unit. For example, it can simulate how the user will look if they gain or lose weight. The calorie input unit inputs daily calorie intake and nutrients. For example, the user inputs the calories and nutrients of their meals, which the system records. The prediction unit predicts the user's body shape one year from now based on the data collected by the calorie input unit. For example, it can predict the user's body shape one year from now if they continue their current diet. The linking unit links with genetic test results. For example, when a user inputs the results of a genetic test into the system, the system will suggest a diet method, daily meals, exercise, supplements, etc. that are suited to the user. The analysis unit analyzes the cause of weight gain. For example, it will tell the user whether the weight gain is simply due to swelling or fat. As a result, the health management system according to the embodiment can predict changes in appearance and body shape, suggest diet methods, and analyze the causes of weight gain based on data such as the user's height, weight, photo, daily weight, calorie intake and nutrients, and genetic test results.

[0068] The simulation unit can simulate how the user will look if they gain or lose weight. For example, the simulation unit simulates how the user will look if they gain weight. For example, the simulation unit can display how the user will look if their weight increases by 5 kilograms. The simulation unit can also simulate how the user will look if they lose weight. For example, the simulation unit can display how the user will look if their weight decreases by 5 kilograms. The simulation unit can also compare how the user will look if they gain or lose weight. For example, the simulation unit can display how the user will look if their weight increases and decreases side by side. This allows the user to visually confirm changes in their body shape.

[0069] The prediction unit can predict what body shape the user will have in one year if they continue their current diet. For example, the prediction unit predicts the user's body shape in one year if they continue their current diet. For example, the prediction unit can predict the user's body fat percentage and muscle mass in one year based on the user's current calorie intake and nutrients. The prediction unit can also predict the user's weight in one year based on the user's current diet. For example, the prediction unit can display the user's weight in one year if they continue their current diet. The prediction unit can also predict changes in the user's body shape in one year based on the user's current diet. For example, the prediction unit can display a graph of changes in the user's body shape in one year if the user continues their current diet. This can increase the user's motivation to improve their eating habits and diet.

[0070] The interlocking unit can suggest a diet method, daily meals, exercise, supplements, etc., tailored to the user based on the results of the genetic test. For example, when the user inputs the results of the genetic test into the system, the interlocking unit suggests a diet method tailored to the user. For example, the interlocking unit can suggest what kind of meal is effective based on a specific genetic type. The interlocking unit can also suggest a daily meal tailored to the user. For example, the interlocking unit can suggest what ingredients should be consumed based on the user's genetic type. The interlocking unit can also suggest an exercise method tailored to the user. For example, the interlocking unit can suggest what kind of exercise is effective based on the user's genetic type. The interlocking unit can also suggest supplements tailored to the user. For example, the interlocking unit can suggest what kind of supplements should be taken based on the user's genetic type. This allows the interlocking unit to provide a personalized diet plan.

[0071] The analysis unit can analyze whether the weight gain is due to simple swelling or fat. The analysis unit, for example, analyzes the cause of the user's weight gain. For example, the analysis unit can analyze whether the weight gain is due to simple swelling. The analysis unit can also analyze whether the weight gain is due to fat. For example, the analysis unit can analyze whether the weight gain is due to fat gain. The analysis unit can also analyze the cause of the weight gain in detail. For example, the analysis unit can analyze whether the weight gain is due to diet or lack of exercise. This allows the user to take appropriate measures.

[0072] The registration unit can estimate the user's emotions and adjust the timing of registration based on the estimated user's emotions. For example, if the user is feeling stressed, the registration unit sends a notification prompting the user to register during a time when the user is able to relax. For example, if the user is relaxed, the registration unit sends a notification prompting the user to register, thereby enabling the user to complete the registration process smoothly. Furthermore, if the user is in a hurry, the registration unit can provide a simplified registration procedure, allowing the user to complete the registration quickly. For example, if the user is in a hurry, the registration unit can provide a simplified registration procedure, allowing the user to complete the registration quickly. In this way, the timing of registration can be adjusted according to the user's emotions, enabling the user to complete the registration process smoothly.

[0073] The registration unit can analyze the user's past registration data and select an appropriate registration method. For example, the registration unit can preferentially suggest registration methods (voice, text, etc.) that the user has frequently used in the past. For example, the registration unit can send a notification encouraging registration at a specific time period based on the user's past registration data. The registration unit can also analyze the user's past registration history and suggest the most efficient registration method. For example, the registration unit can analyze the user's past registration history and suggest the most efficient registration method. In this way, the optimal registration method can be suggested by analyzing the user's past registration data.

[0074] The registration unit can perform filtering based on the user's current health condition and lifestyle habits at the time of registration. The registration unit can suggest appropriate registration items based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the registration unit can customize the registration items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The registration unit can also filter information required at the time of registration based on the user's health condition and lifestyle habits. For example, the registration unit can filter information required at the time of registration based on the user's health condition and lifestyle habits. In this way, appropriate registration items can be suggested by filtering based on the user's current health condition and lifestyle habits.

[0075] At the time of registration, the registration unit can select an appropriate registration means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the registration unit can provide voice input preferentially. For example, if the user prefers text input, the registration unit can provide text input preferentially. Furthermore, if the user prefers image input, the registration unit can also provide image input preferentially. For example, if the user prefers image input, the registration unit can provide image input preferentially. This allows the registration process to be carried out smoothly by selecting the optimal registration means according to the user's input method.

[0076] The input unit can estimate the user's emotions and adjust the timing of input based on the estimated user's emotions. For example, if the user is feeling stressed, the input unit sends a notification prompting the user to input during a time when the user is able to relax. For example, if the user is relaxed, the input unit sends a notification prompting the user to input, thereby enabling the user to perform input work smoothly. Furthermore, if the user is in a hurry, the input unit can provide a simplified input procedure so that the user can complete input quickly. For example, if the user is in a hurry, the input unit can provide a simplified input procedure so that the user can complete input quickly. In this way, the input timing can be adjusted according to the user's emotions, enabling the user to perform input work smoothly.

[0077] The input unit can analyze the user's past input data and select an appropriate input method. For example, the input unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. For example, the input unit can send a notification prompting input at a specific time period based on the user's past input data. The input unit can also analyze the user's past input history and suggest the most efficient input method. For example, the input unit can analyze the user's past input history and suggest the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past input data.

[0078] The input unit can perform filtering based on the user's current health condition and lifestyle habits at the time of input. The input unit can suggest appropriate input items based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the input unit can customize the input items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The input unit can also filter information required at the time of input based on the user's health condition and lifestyle habits. For example, the input unit can filter information required at the time of input based on the user's health condition and lifestyle habits. In this way, appropriate input items can be suggested by filtering based on the user's current health condition and lifestyle habits.

[0079] The input unit can select an appropriate input means depending on the user's input method (voice, text, image, etc.) at the time of input. For example, if the user prefers voice input, the input unit can provide voice input preferentially. For example, if the user prefers text input, the input unit can provide text input preferentially. Furthermore, if the user prefers image input, the input unit can also provide image input preferentially. For example, if the user prefers image input, the input unit can provide image input preferentially. This allows input work to be performed smoothly by selecting the optimal input means depending on the user's input method.

[0080] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user's emotions. For example, if the user is nervous, the simulation unit can provide a simple, highly visible display method. For example, if the user is relaxed, the simulation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the simulation unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the simulation unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the simulation according to the user's emotions, a highly visible display is possible.

[0081] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past weight fluctuation data. The simulation unit improves the accuracy of the simulation, for example, based on the user's past weight fluctuation data. For example, the simulation unit can analyze the user's past weight fluctuation patterns to improve the accuracy of the simulation. The simulation unit can also provide optimal simulation results by referring to the user's past weight fluctuation data. For example, the simulation unit can provide optimal simulation results by referring to the user's past weight fluctuation data. In this way, the accuracy of the simulation is improved by referring to the user's past weight fluctuation data.

[0082] During the simulation, the simulation unit can adjust the simulation parameters based on the user's health condition and lifestyle habits. The simulation unit adjusts the simulation parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the simulation unit can adjust the simulation parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The simulation unit can also optimize the simulation parameters based on the user's health condition and lifestyle habits. For example, the simulation unit can optimize the simulation parameters based on the user's health condition and lifestyle habits. In this way, by adjusting the simulation parameters based on the user's health condition and lifestyle habits, more accurate simulation results can be provided.

[0083] The simulation unit can improve the simulation method by reflecting user feedback during the simulation. The simulation unit improves the simulation method, for example, based on feedback provided by the user. For example, the simulation unit can propose an optimal simulation method based on the user feedback. The simulation unit can also improve the simulation interface by reflecting user feedback. For example, the simulation unit can improve the simulation interface by reflecting user feedback. In this way, by reflecting user feedback, the simulation method can be improved and simulation results that are more suitable for the user can be provided.

[0084] The calorie input unit can estimate the user's emotions and adjust the timing of calorie input based on the estimated user's emotions. For example, if the user is feeling stressed, the calorie input unit sends a notification prompting the user to input calories during a time when the user is able to relax. For example, if the user is relaxed, the calorie input unit sends a notification prompting the user to input calories, allowing the user to perform the input work smoothly. Furthermore, if the user is in a hurry, the calorie input unit can provide a simplified calorie input procedure so that the user can complete the input quickly. For example, if the user is in a hurry, the calorie input unit can provide a simplified calorie input procedure so that the user can complete the input quickly. In this way, the timing of calorie input can be adjusted according to the user's emotions, allowing the user to perform the input work smoothly.

[0085] The calorie input unit can analyze the user's past calorie input data and select an appropriate input method. For example, the calorie input unit can prioritize and suggest calorie input methods (voice, text, etc.) that the user has frequently used in the past. For example, the calorie input unit can send a notification prompting input during a specific time period based on the user's past calorie input data. The calorie input unit can also analyze the user's past calorie input history and suggest the most efficient input method. For example, the calorie input unit can analyze the user's past calorie input history and suggest the most efficient input method. In this way, the optimal input method can be suggested by analyzing the user's past calorie input data.

[0086] The calorie input unit can perform filtering based on the user's current dietary content and lifestyle habits when entering calories. The calorie input unit can suggest appropriate calorie input items based on, for example, the user's current dietary content (ingredients, cooking method, etc.). For example, the calorie input unit can customize the calorie input items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The calorie input unit can also filter information required when entering calories based on the user's dietary content and lifestyle habits. For example, the calorie input unit can filter information required when entering calories based on the user's dietary content and lifestyle habits. In this way, appropriate calorie input items can be suggested by filtering based on the user's current dietary content and lifestyle habits.

[0087] When inputting calories, the calorie input unit can select an appropriate input means depending on the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the calorie input unit can provide voice input preferentially. For example, if the user prefers text input, the calorie input unit can provide text input preferentially. Furthermore, if the user prefers image input, the calorie input unit can also provide image input preferentially. For example, if the user prefers image input, the calorie input unit can provide image input preferentially. This allows the user to smoothly input calories by selecting the optimal input means depending on their input method.

[0088] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated user's emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. For example, if the user is relaxed, the prediction unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the prediction unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the prediction unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the prediction according to the user's emotions, a highly visible display is possible.

[0089] The prediction unit can improve the accuracy of the prediction by referring to the user's past weight fluctuation data when making a prediction. The prediction unit improves the accuracy of the prediction, for example, based on the user's past weight fluctuation data. For example, the prediction unit can analyze the user's past weight fluctuation patterns to improve the accuracy of the prediction. The prediction unit can also provide an optimal prediction result by referring to the user's past weight fluctuation data. For example, the prediction unit can provide an optimal prediction result by referring to the user's past weight fluctuation data. In this way, the accuracy of the prediction is improved by referring to the user's past weight fluctuation data.

[0090] The prediction unit can adjust prediction parameters based on the user's health condition and lifestyle habits when making predictions. The prediction unit adjusts the prediction parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the prediction unit can adjust the prediction parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The prediction unit can also optimize the prediction parameters based on the user's health condition and lifestyle habits. For example, the prediction unit can optimize the prediction parameters based on the user's health condition and lifestyle habits. In this way, by adjusting the prediction parameters based on the user's health condition and lifestyle habits, more accurate prediction results can be provided.

[0091] The prediction unit can improve the prediction method by reflecting user feedback during prediction. The prediction unit improves the prediction method, for example, based on feedback provided by the user. For example, the prediction unit can propose an optimal prediction method based on the user feedback. The prediction unit can also improve the prediction interface by reflecting user feedback. For example, the prediction unit can improve the prediction interface by reflecting user feedback. In this way, by reflecting user feedback, the prediction method can be improved and prediction results that are more suitable for the user can be provided.

[0092] The interlocking unit can estimate the user's emotions and adjust the timing of the interlocking based on the estimated user's emotions. For example, if the user is feeling stressed, the interlocking unit sends a notification encouraging the user to interlock during a time when the user is able to relax. For example, if the user is relaxed, the interlocking unit sends a notification encouraging the user to interlock, thereby enabling the user to perform the interlocking task smoothly. Furthermore, if the user is in a hurry, the interlocking unit can provide a simplified interlocking procedure to quickly complete the interlocking. For example, if the user is in a hurry, the interlocking unit can provide a simplified interlocking procedure to quickly complete the interlocking. In this way, the interlocking timing can be adjusted according to the user's emotions, enabling the user to perform the interlocking task smoothly.

[0093] During linking, the interlocking unit can improve the accuracy of linking by referring to the user's past genetic test data. The interlocking unit, for example, improves the accuracy of linking based on the user's past genetic test data. For example, the interlocking unit can analyze the user's past genetic test results and improve the accuracy of linking. The interlocking unit can also provide optimal linking results by referring to the user's past genetic test data. For example, the interlocking unit can provide optimal linking results by referring to the user's past genetic test data. In this way, the accuracy of linking is improved by referring to the user's past genetic test data.

[0094] During linkage, the linking unit can adjust linkage parameters based on the user's health condition and lifestyle habits. The linking unit adjusts linkage parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the linking unit can adjust linkage parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The linking unit can also optimize linkage parameters based on the user's health condition and lifestyle habits. For example, the linking unit can optimize linkage parameters based on the user's health condition and lifestyle habits. This makes it possible to provide more accurate linkage results by adjusting linkage parameters based on the user's health condition and lifestyle habits.

[0095] The interlocking unit can improve the interlocking method by reflecting user feedback during interlocking. The interlocking unit improves the interlocking method, for example, based on feedback provided by the user. For example, the interlocking unit can propose an optimal interlocking method based on user feedback. The interlocking unit can also improve the interlocking interface by reflecting user feedback. For example, the interlocking unit can improve the interlocking interface by reflecting user feedback. In this way, the interlocking method can be improved by reflecting user feedback, and interlocking results that are more suitable for the user can be provided.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis according to the user's emotions, a highly visible display is possible.

[0097] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past weight fluctuation data. The analysis unit improves the accuracy of the analysis, for example, based on the user's past weight fluctuation data. For example, the analysis unit can analyze the user's past weight fluctuation patterns to improve the accuracy of the analysis. The analysis unit can also provide optimal analysis results by referring to the user's past weight fluctuation data. For example, the analysis unit can provide optimal analysis results by referring to the user's past weight fluctuation data. In this way, the accuracy of the analysis is improved by referring to the user's past weight fluctuation data.

[0098] During analysis, the analysis unit can adjust the analysis parameters based on the user's health condition and lifestyle habits. The analysis unit adjusts the analysis parameters based on, for example, the user's current health condition (weight, height, BMI, etc.). For example, the analysis unit can adjust the analysis parameters taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). The analysis unit can also optimize the analysis parameters based on the user's health condition and lifestyle habits. For example, the analysis unit can optimize the analysis parameters based on the user's health condition and lifestyle habits. In this way, by adjusting the analysis parameters based on the user's health condition and lifestyle habits, more accurate analysis results can be provided.

[0099] The analysis unit can improve the analysis method by reflecting user feedback during analysis. The analysis unit improves the analysis method, for example, based on feedback provided by the user. For example, the analysis unit can suggest an optimal analysis method based on user feedback. The analysis unit can also improve the analysis interface by reflecting user feedback. For example, the analysis unit can improve the analysis interface by reflecting user feedback. In this way, by reflecting user feedback, the analysis method can be improved and analysis results that are more suitable for the user can be provided.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. For example, when the user is relaxed, the analysis unit can display detailed analysis results. The analysis unit can also prioritize displaying the most important analysis results when the user is in a hurry. For example, when the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results. In this way, by determining the priority of analysis according to the user's emotions, important analysis results can be prioritized and displayed.

[0101] During analysis, the analysis unit can adjust analysis parameters based on the user's geographical location information. For example, when the user is in a specific region, the analysis unit can adjust the analysis parameters taking into account the climate and environment of that region. For example, when the user is traveling, the analysis unit can adjust the analysis parameters taking into account the environment of the travel destination. Furthermore, when the user is at home, the analysis unit can also adjust the analysis parameters taking into account the home environment. For example, when the user is at home, the analysis unit can adjust the analysis parameters taking into account the home environment. In this way, by taking into account the user's geographical location information, more accurate analysis results can be provided.

[0102] During the analysis, the analysis unit can analyze the user's social media activities and incorporate related data into the analysis. For example, the analysis unit can reflect the meal details shared by the user on social media in the analysis. For example, the analysis unit can analyze the content posted by the user on social media and incorporate related data into the analysis. The analysis unit can also refer to the activities of the user's friends on social media and incorporate related data into the analysis. For example, the analysis unit can refer to the activities of the user's friends on social media and incorporate related data into the analysis. In this way, by analyzing the user's social media activities, related data can be incorporated into the analysis, making it possible to provide more accurate analysis results.

[0103] During analysis, the analysis unit can adjust the analysis method by reflecting the user's past feedback. The analysis unit can improve the analysis method, for example, based on feedback provided by the user in the past. For example, the analysis unit can suggest an optimal analysis method based on the user's past feedback. The analysis unit can also customize the analysis interface by reflecting the user's feedback. For example, the analysis unit can customize the analysis interface by reflecting the user's past feedback. In this way, the analysis method can be customized by reflecting the user's past feedback, making it possible to provide analysis results that are more suitable for the user. === Hard Collateral 1-1 === For example, each of the multiple elements including the registration unit, input unit, simulation unit, calorie input unit, prediction unit, linkage unit, and analysis unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and registers the user's height, weight, and photo. The input unit is realized by the control unit 46A of the smart device 14 and measures and inputs the user's weight every day. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates changes in appearance. The calorie input unit is realized by the control unit 46A of the smart device 14 and inputs the user's daily calorie intake and nutrient intake. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's body shape one year from now. The linkage unit is realized by the specific processing unit 290 of the data processing device 12 and links with the results of a genetic test. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the cause of weight gain. === Hard Collateral 1-2 === For example, each of the multiple elements including the registration unit, input unit, simulation unit, calorie input unit, prediction unit, linkage unit, and analysis unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and registers the user's height, weight, and photo. The input unit is realized by the control unit 46A of the smart glasses 214 and measures and inputs the user's weight every day. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates changes in appearance. The calorie input unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's daily calorie intake and nutrients. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's body shape one year from now. The linkage unit is realized by the specific processing unit 290 of the data processing device 12 and links with the genetic test results. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the cause of weight gain. === Hard Collateral 1-3 === For example, each of the multiple elements including the registration unit, input unit, simulation unit, calorie input unit, prediction unit, linkage unit, and analysis unit is realized by at least one of the headset-type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset-type terminal 314 and registers the user's height, weight, and photo. The input unit is realized by the control unit 46A of the headset-type terminal 314 and measures and inputs the user's weight every day. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates changes in appearance. The calorie input unit is realized by the control unit 46A of the headset-type terminal 314 and inputs the user's daily calorie intake and nutrient intake. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's body shape one year from now. The linkage unit is realized by the specific processing unit 290 of the data processing device 12 and links with the results of a genetic test. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the cause of weight gain. === Hard Collateral 1-4 === For example, each of the multiple elements including the registration unit, input unit, simulation unit, calorie input unit, prediction unit, linkage unit, and analysis unit is realized by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and registers the user's height, weight, and photo. The input unit is realized by the control unit 46A of the robot 414 and measures and inputs the user's weight every day. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates changes in appearance. The calorie input unit is realized by the control unit 46A of the robot 414 and inputs the user's daily calorie intake and nutrients. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's body shape one year from now. The linkage unit is realized by the specific processing unit 290 of the data processing device 12 and links with the results of a genetic test. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the cause of weight gain.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The registration unit can estimate the user's emotions and adjust the timing of registration based on the estimated emotions. For example, if the user is feeling stressed, a notification urging the user to register can be sent during a time when the user is feeling relaxed. Also, if the user is relaxed, a notification urging the user to register can be sent, allowing the user to complete the registration process smoothly. Furthermore, if the user is in a hurry, a simplified registration procedure can be provided, allowing the user to complete the registration quickly. In this way, the registration timing can be adjusted according to the user's emotions, allowing the user to complete the registration process smoothly.

[0106] The input unit can analyze the user's past input data and select an appropriate input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also send notifications prompting input at specific times based on the user's past input data. It can also analyze the user's past input history and suggest the most efficient input method. This makes it possible to suggest the optimal input method by analyzing the user's past input data.

[0107] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the simulation according to the user's emotions, a highly visible display can be achieved.

[0108] The calorie input unit can perform filtering based on the user's current dietary content and lifestyle habits. For example, it can suggest appropriate calorie input items based on the user's current dietary content (ingredients, cooking method, etc.). It can also customize calorie input items taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). It can also filter information required when entering calories based on the user's dietary content and lifestyle habits. This makes it possible to suggest appropriate calorie input items by filtering based on the user's current dietary content and lifestyle habits.

[0109] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the prediction according to the user's emotions, a highly visible display can be achieved.

[0110] The linking unit can improve the accuracy of linking by referring to the user's past genetic test data. For example, the accuracy of linking can be improved based on the user's past genetic test data. The accuracy of linking can also be improved by analyzing the user's past genetic test results. Furthermore, the optimal linking results can be provided by referring to the user's past genetic test data. In this way, the accuracy of linking is improved by referring to the user's past genetic test data.

[0111] The analysis unit can estimate the user's emotions and adjust the display method of the analysis based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis according to the user's emotions, a highly visible display can be achieved.

[0112] The analysis unit can improve the accuracy of the analysis by referring to the user's past weight fluctuation data. For example, the accuracy of the analysis can be improved based on the user's past weight fluctuation data. The accuracy of the analysis can also be improved by analyzing the user's past weight fluctuation patterns. Furthermore, the analysis unit can provide optimal analysis results by referring to the user's past weight fluctuation data. Thus, the accuracy of the analysis can be improved by referring to the user's past weight fluctuation data.

[0113] During the simulation, the simulation unit can adjust the simulation parameters based on the user's health condition and lifestyle habits. For example, the simulation parameters can be adjusted based on the user's current health condition (weight, height, BMI, etc.). The simulation parameters can also be adjusted taking into account the user's lifestyle habits (diet, exercise, sleep, etc.). Furthermore, the simulation parameters can be optimized based on the user's health condition and lifestyle habits. This makes it possible to provide more accurate simulation results by adjusting the simulation parameters based on the user's health condition and lifestyle habits.

[0114] The calorie input unit can estimate the user's emotions and adjust the timing of calorie input based on the estimated emotions. For example, if the user is feeling stressed, a notification can be sent to prompt the user to input calories during a time when the user is able to relax. Also, if the user is relaxed, a notification can be sent to prompt the user to input calories, allowing the user to complete the input work smoothly. Furthermore, if the user is in a hurry, a simplified calorie input procedure can be provided to allow the user to complete the input quickly. In this way, the timing of calorie input can be adjusted according to the user's emotions, allowing the user to complete the input work smoothly.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The registration unit registers the user's height, weight, and photo. For example, the user enters their height in centimeters and their weight in kilograms. They can also take and register full-body and face photos. Step 2: The input unit measures and inputs the user's weight every day. For example, the user can measure their weight every morning and input the data into the system. Step 3: The simulation unit simulates changes in appearance based on the data collected by the registration unit and the input unit. For example, it can simulate how the user will look if they gain or lose weight. Step 4: The calorie input section inputs the daily calorie intake and nutrients. For example, the user inputs the calories and nutrients of the food they eat, and the system records them. Step 5: The prediction unit predicts your body shape one year from now based on the data collected by the calorie input unit. For example, it can predict what your body shape will be one year from now if you continue eating the same way. Step 6: The linking unit links with the genetic test results. For example, when a user inputs the genetic test results into the system, the system will suggest diet methods, daily meals, exercise, supplements, etc. that are suitable for the user. Step 7: The analysis section will analyze the cause of the weight gain, for example, whether the weight gain is due to simple swelling or fat.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0140] The 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.

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

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

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

[0144] Fig. 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.

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

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

[0147] In the 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.

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

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

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

[0151] The data processing system 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.

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

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

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

[0156] The 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.

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

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

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 registration section for registering the user's height, weight, and photo; An input unit for measuring and inputting daily weight; a simulation unit that simulates changes in appearance based on the data collected by the registration unit and the input unit; A calorie input section for inputting daily calorie intake or nutrients; a prediction unit that predicts a body shape one year from now based on the data collected by the calorie input unit; A linking unit that links with the genetic test results; An analysis department that analyzes the causes of weight gain, Equipped with A system characterized by:

2. The simulation unit Simulate what it would look like if the user gained or lost weight The system of claim 1 .

3. The prediction unit Predict what your body shape will be in a year if you continue eating the way you do now The system of claim 1 .

4. The interlocking portion is Based on the results of genetic testing, the service suggests dietary methods, daily meals, exercise, supplements, etc. that are suited to the user. The system of claim 1 .

5. The analysis unit Analyze whether weight gain is simply due to swelling or fat The system of claim 1 .

6. The registration unit Estimate the user's emotions and adjust the timing of registration based on the estimated user emotions. The system of claim 1 .

7. The registration unit Analyze users' past registration data and select the appropriate registration method The system of claim 1 .

8. The registration unit Filtering based on the user's current health or lifestyle at the time of registration The system of claim 1 .

9. The registration unit During registration, select the appropriate registration method depending on the user's input method The system of claim 1 .

10. The input unit Estimate the user's emotions and adjust the timing of inputs based on the estimated user emotions. The system of claim 1 .

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