System

Abstract

An object of the system according to the embodiment is to provide a diet plan and a fitness plan that are customized based on individual health goals and lifestyles of users.SOLUTION: A system according to an embodiment includes an information collection unit, a data analysis unit, and a plan generation unit. The information collecting unit collects health goals, lifestyles, food likes and dislikes, and allergy information of the user. The data analysis unit analyzes the data collected by the information collection unit. The plan generation unit generates a diet plan and a fitness plan customized based on the data analyzed by the data analysis unit.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 fall short in providing a user with a customized diet and fitness plan based on their individual health goals and lifestyle, and there is room for improvement.

[0005] The system of the embodiment aims to provide a user with a customized meal plan and fitness plan based on their individual health goals and lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a data analysis unit, and a plan generation unit. The information collection unit collects information about a user's health goals, lifestyle, food likes and dislikes, and allergies. The data analysis unit analyzes the data collected by the information collection unit. The plan generation unit generates a customized meal plan and fitness plan based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] An embodiment of the system can provide a user with a customized meal plan and fitness plan based on their individual health goals and lifestyle. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The FitGeniusCoachAI system according to an embodiment of the present invention is a system that collects information on a user's health goals, lifestyle, food preferences, and allergies through a conversational AI interface, analyzes it using multimodal AI technology, including physiological data from wearable devices and visual data obtained from food photos uploaded by the user, and provides customized meal plans and fitness plans. In this way, the FitGeniusCoachAI system can support the user in achieving their health goals and promote lifestyle improvements.

[0029] The FitGeniusCoachAI system according to the embodiment includes an information collection unit, a data analysis unit, and a plan generation unit. The information collection unit collects information about a user's health goals, lifestyle, food preferences, and allergies. For example, if a user says, "I want to lose weight," the information collection unit asks questions such as, "How much weight do you want to lose and over what period of time?" to confirm the user's detailed goal. The information collection unit also collects information such as, "I don't like dairy products," and reflects this information in the meal plan. The data analysis unit analyzes the data collected by the information collection unit. For example, the data analysis unit collects physiological data from a wearable device and analyzes heart rate, step count, sleep data, and the like. The data analysis unit also analyzes meal photos uploaded by the user to identify the contents and nutrients of the meal. For example, the data analysis unit analyzes a photo of breakfast taken by the user and provides information such as, "This meal is lacking in protein." The plan generation unit generates a customized meal plan and fitness plan based on the data analyzed by the data analysis unit. For example, if a user's health goal is to "lose weight" and data from the wearable device indicates that "the number of steps taken per day is low," the plan generation unit will suggest a fitness plan such as "increase the amount of time you spend walking." This allows the FitGeniusCoachAI system according to the embodiment to support the user in achieving their health goal and promote lifestyle improvements.

[0030] The information collection unit can analyze the user's past conversation history and generate a question pattern that is optimal for the user. The information collection unit, for example, stores the user's past conversation history in a database and analyzes it. For example, it analyzes what answers were given to what questions in the past. The information collection unit also generates a question pattern that is optimal for the user based on the past conversation history. For example, if the user previously answered that they are "not good at exercise," it can adjust the question pattern to avoid questions related to exercise. This allows for more effective information collection by providing the user with an optimal question pattern.

[0031] The information collection unit can also collect health information about the user's family and friends using a conversational AI interface and propose a health plan for the entire family. The information collection unit, for example, asks questions to collect health information about the user's family and friends. For example, it may ask, "How is the health of your family?" The information collection unit also proposes a health plan for the entire family based on the collected health information of family and friends. For example, it can suggest menus that will encourage the whole family to eat healthy meals. This allows the health of the entire family to be supported by proposing a health plan for the entire family.

[0032] The information collection unit can make the conversational AI interface multilingual, enabling it to accommodate users from different languages ​​and cultural backgrounds. For example, the information collection unit builds multiple language databases to make the conversational AI interface multilingual. For example, it collects language data such as English, Spanish, and Chinese. The information collection unit also customizes the interface based on cultural background to accommodate users from different cultural backgrounds. For example, it can ask questions that take into account local customs and values. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0033] The data analysis unit collects data from the wearable device in real time and can immediately reflect changes in the user's health condition. The data analysis unit, for example, collects data from the wearable device in real time and monitors the user's health condition. For example, it displays heart rate and step count in real time. The data analysis unit also immediately reflects changes in the user's health condition based on the collected data. For example, it can issue an alert if the heart rate suddenly rises. This allows changes in the user's health condition to be reflected immediately, enabling a quick response.

[0034] The data analysis unit can analyze data from the wearable device, predict health risks for the user, and suggest preventive measures. The data analysis unit, for example, analyzes data collected from the wearable device and predicts health risks for the user. For example, the data analysis unit evaluates risks based on heart rate and sleep data. The data analysis unit also suggests appropriate preventive measures based on the predicted health risks. For example, the data analysis unit can recommend exercise or dietary improvements. This allows the user's health risks to be predicted and appropriate preventive measures to be suggested, improving health management.

[0035] The data analysis unit can link data from the wearable device with other smart devices to perform comprehensive health management. For example, the data analysis unit can link data from the wearable device with smart home devices to perform comprehensive health management. For example, the data analysis unit can adjust the room temperature based on heart rate data. The data analysis unit can also link with other smart devices to comprehensively manage the user's health condition. For example, the data analysis unit can link with a fitness tracker to collect exercise data and reflect it in health management. This makes comprehensive health management possible by linking with smart devices.

[0036] The data analysis unit can analyze data from the wearable device in association with the user's work environment and living environment and make suggestions for improving the environment. For example, the data analysis unit analyzes data collected from the wearable device in association with the user's work environment and living environment. For example, it evaluates the stress level at work. Furthermore, the data analysis unit makes suggestions for appropriate environmental improvements based on the analysis results. For example, it can suggest adjusting the temperature and lighting in the work environment. This makes it possible to make appropriate environmental improvements based on the user's work environment and living environment.

[0037] The data analysis unit can track the user's eating patterns and nutritional balance over the long term based on the analysis results of the food photos and suggest areas for improvement. The data analysis unit, for example, stores the analysis results of the food photos in a database and tracks the user's eating patterns and nutritional balance over the long term. For example, it records the contents of daily meals. The data analysis unit also suggests areas for improvement to the user's eating patterns and nutritional balance based on the analysis results. For example, it can suggest ingredients to make up for nutrient deficiencies. This makes it possible to improve the user's eating patterns and nutritional balance.

[0038] The data analysis unit can compare the analysis results of the meal photos with the data of other users and provide a benchmark. The data analysis unit, for example, compares the analysis results of the meal photos with the data of other users and provides a benchmark. For example, it compares the diet of users of the same age or with the same health goals. The data analysis unit also suggests improvements to the user's diet based on the benchmark results. For example, it can suggest ingredients to supplement nutrients that are lacking compared to other users. This allows the user to improve their diet by comparing with the data of other users.

[0039] The data analysis unit can save the analysis results of the meal photos as the user's meal history and compare it with past data to evaluate progress. The data analysis unit, for example, saves the analysis results of the meal photos as the user's meal history in a database. For example, it records the contents of daily meals. The data analysis unit also compares it with past data to evaluate the progress of the user's eating habits. For example, it can evaluate the degree of improvement in nutritional balance and revise the meal plan as necessary. This makes it possible to improve eating habits by evaluating progress based on the user's meal history.

[0040] The data analysis unit not only reflects the analysis results of the meal photos in the user's meal plan, but can also provide recipe suggestions and ingredient shopping lists. The data analysis unit, for example, customizes the user's meal plan based on the analysis results of the meal photos. For example, it proposes meal menus that take nutritional balance into consideration. The data analysis unit also provides recipe suggestions and ingredient shopping lists. For example, it can generate a list of necessary ingredients based on the proposed meal menu and suggest where to purchase them. This makes meal management easier by providing recipe suggestions and ingredient shopping lists in addition to the user's meal plan.

[0041] When integrating multimodal data, the data analysis unit performs a detailed analysis of the correlations between the data, enabling the provision of a more accurate health plan. The data analysis unit, for example, integrates multimodal data and performs a detailed analysis of the correlations between the data. For example, it evaluates the relationship between dietary data and exercise data. The data analysis unit also provides a more accurate health plan based on the results of the correlation analysis. For example, it can propose a health plan that takes into account the balance between dietary content and exercise volume. This makes it possible to provide a more accurate health plan by analyzing the correlations between the data in detail.

[0042] The data analysis unit can predict the user's health condition using multimodal data and suggest preventive measures. The data analysis unit, for example, analyzes the multimodal data and predicts the user's health condition. For example, it evaluates health risks based on dietary data and exercise data. The data analysis unit also suggests appropriate preventive measures based on the predicted health condition. For example, it can recommend exercise or dietary improvements. This allows the user's health condition to be predicted and appropriate preventive measures to be suggested, thereby improving health management.

[0043] The data analysis unit can integrate the multimodal data with other health data to provide comprehensive health management. For example, the data analysis unit can integrate the multimodal data with medical records and genetic information to build a system for comprehensive health management. For example, health risks can be assessed based on genetic information. The data analysis unit can also integrate the data with other health data to comprehensively manage the user's health status. For example, medical records and fitness data can be integrated and reflected in health management. This makes it possible to provide comprehensive health management by integrating the data with other health data.

[0044] The data analysis unit can monitor the user's health condition in real time using the multimodal data and issue an alert if an abnormality is detected. The data analysis unit, for example, monitors the multimodal data in real time to monitor the user's health condition. For example, it detects abnormalities in heart rate or blood pressure. The data analysis unit also issues an alert if an abnormality is detected. For example, it can issue a notification if the heart rate suddenly increases. This allows the user's health condition to be monitored in real time and allows for a quick response if an abnormality is detected.

[0045] The plan generation unit can dynamically adjust the meal plan and fitness plan according to the user's health goals and lifestyle. The plan generation unit, for example, builds a system that dynamically adjusts the meal plan and fitness plan according to the user's health goals and lifestyle. For example, it proposes a meal menu according to a target weight. The plan generation unit also adjusts the fitness plan based on the user's lifestyle. For example, it can propose an appropriate exercise plan for a user who does a lot of desk work. This makes it possible to provide an appropriate plan according to the user's health goals and lifestyle.

[0046] The plan generation unit can periodically evaluate the effectiveness of the meal plan and fitness plan and modify the plan as necessary. The plan generation unit, for example, builds a system that periodically evaluates the effectiveness of the meal plan and fitness plan. For example, the plan generation unit periodically monitors the user's health condition. The plan generation unit also modifies the plan as necessary based on the evaluation results. For example, the meal menu and exercise plan can be adjusted depending on the degree of goal achievement. This allows for more effective health management by periodically evaluating the effectiveness of the plan and modifying it as necessary.

[0047] The plan generation unit can customize the meal plan and fitness plan according to the user's work environment and living environment. The plan generation unit builds a system that customizes the meal plan and fitness plan based on the user's work environment and living environment, for example. For example, it proposes an appropriate exercise plan for a user who does a lot of desk work. The plan generation unit also adjusts the meal plan based on the user's living environment. For example, it can propose easy-to-prepare meal menus for a user who leads a busy life. This makes it possible to provide an appropriate plan according to the user's work environment and living environment.

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

[0049] The information collection unit can collect the user's hobbies and interests, as well as their health goals and lifestyle, and reflect these in a customized fitness plan. For example, if a user says, "I like yoga," the information collection unit can collect detailed information by asking questions such as, "How often do you do yoga?" The information collection unit can also collect information such as, "I like outdoor activities," and reflect this in the fitness plan. This makes it possible to provide a fitness plan based on the user's hobbies and interests.

[0050] The information collection unit can analyze the user's past conversation history and generate a question pattern that is optimal for the user. For example, the user's past conversation history is stored in a database and analyzed. For example, the information collection unit analyzes what answers were given to what questions in the past. The information collection unit also generates a question pattern that is optimal for the user based on the past conversation history. For example, if the user previously answered that they are "not good at exercise," the information collection unit can adjust the question pattern to avoid questions related to exercise. This allows for more effective information collection by providing the user with the optimal question pattern.

[0051] The information collection unit can also use a conversational AI interface to collect health information about the user's family and friends, and propose a health plan for the entire family. For example, it can ask questions to collect health information about the user's family and friends, such as, "How is the health of your family?" The information collection unit can also propose a health plan for the entire family based on the collected health information of family and friends. For example, it can suggest menus that will encourage the whole family to eat healthy meals. This allows the health of the entire family to be supported by proposing a health plan for the entire family.

[0052] The information gathering unit can make the conversational AI interface multilingual, enabling it to accommodate users from different languages ​​and cultural backgrounds. For example, to make the conversational AI interface multilingual, multiple language databases are built. For example, language data such as English, Spanish, and Chinese is collected. The information gathering unit also customizes the interface based on cultural background to accommodate users from different cultural backgrounds. For example, it can ask questions that take into account local customs and values. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0053] The data analysis unit collects data from the wearable device in real time and can immediately reflect changes in the user's health condition. For example, data from the wearable device can be collected in real time to monitor the user's health condition. For example, heart rate and step count can be displayed in real time. The data analysis unit also immediately reflects changes in the user's health condition based on the collected data. For example, an alert can be issued if the heart rate suddenly rises. This allows changes in the user's health condition to be reflected immediately, enabling a quick response.

[0054] The data analysis unit can analyze data from the wearable device, predict the user's health risks, and suggest preventive measures. For example, it can analyze data collected from the wearable device and predict the user's health risks. For example, it can evaluate risks based on heart rate and sleep data. The data analysis unit can also suggest appropriate preventive measures based on the predicted health risks. For example, it can recommend exercise or dietary improvements. This allows the user's health risks to be predicted and appropriate preventive measures to be suggested, improving health management.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The information gathering unit collects information about the user's health goals, lifestyle, food likes and dislikes, and allergies. For example, if a user says, "I want to lose weight," the information gathering unit will ask questions such as, "How much weight do you want to lose and over what period of time?" to confirm the detailed goal. The information gathering unit will also collect information such as, "I don't like dairy products," and reflect this in the meal plan. Step 2: The data analysis unit analyzes the data collected by the information collection unit. For example, the data analysis unit collects physiological data from the wearable device and analyzes heart rate, step count, sleep data, etc. The data analysis unit also analyzes meal photos uploaded by the user to identify the contents and nutrients of the meal. For example, it analyzes a photo of breakfast taken by the user and provides information such as "This meal is lacking in protein." Step 3: The plan generator generates a customized diet plan and fitness plan based on the data analyzed by the data analyzer. For example, if the user's health goal is to "lose weight" and the data from the wearable device indicates that the user takes fewer steps per day, the plan generator may suggest a fitness plan such as "walk more."

[0057] (Example 2) The FitGeniusCoachAI system according to an embodiment of the present invention is a system that collects information on a user's health goals, lifestyle, food preferences, and allergies through a conversational AI interface, analyzes it using multimodal AI technology, including physiological data from wearable devices and visual data obtained from food photos uploaded by the user, and provides customized meal plans and fitness plans. In this way, the FitGeniusCoachAI system can support the user in achieving their health goals and promote lifestyle improvements.

[0058] The FitGeniusCoachAI system according to the embodiment includes an information collection unit, a data analysis unit, and a plan generation unit. The information collection unit collects information about a user's health goals, lifestyle, food preferences, and allergies. For example, if a user says, "I want to lose weight," the information collection unit asks questions such as, "How much weight do you want to lose and over what period of time?" to confirm the user's detailed goal. The information collection unit also collects information such as, "I don't like dairy products," and reflects this information in the meal plan. The data analysis unit analyzes the data collected by the information collection unit. For example, the data analysis unit collects physiological data from a wearable device and analyzes heart rate, step count, sleep data, and the like. The data analysis unit also analyzes meal photos uploaded by the user to identify the contents and nutrients of the meal. For example, the data analysis unit analyzes a photo of breakfast taken by the user and provides information such as, "This meal is lacking in protein." The plan generation unit generates a customized meal plan and fitness plan based on the data analyzed by the data analysis unit. For example, if a user's health goal is to "lose weight" and data from the wearable device indicates that "the number of steps taken per day is low," the plan generation unit will suggest a fitness plan such as "increase the amount of time you spend walking." This allows the FitGeniusCoachAI system according to the embodiment to support the user in achieving their health goal and promote lifestyle improvements.

[0059] The information collection unit analyzes the user's tone of voice and speaking speed to estimate their stress level and emotional state, and can adjust the content and timing of questions based on this. For example, the information collection unit analyzes the tone and speaking speed of the user's voice in real time to estimate their stress level. For example, if the user's voice is high-pitched and fast, it can determine that the user is stressed and ask questions to help them relax. The information collection unit also analyzes the user's speaking speed to estimate their emotional state. For example, if the user speaks slowly and calmly, it can determine that the user is relaxed and ask more detailed questions. This makes it possible to ask appropriate questions according to the user's stress level and emotional state.

[0060] The information collection unit can analyze the user's past conversation history and generate a question pattern that is optimal for the user. The information collection unit, for example, stores the user's past conversation history in a database and analyzes it. For example, it analyzes what answers were given to what questions in the past. The information collection unit also generates a question pattern that is optimal for the user based on the past conversation history. For example, if the user previously answered that they are "not good at exercise," it can adjust the question pattern to avoid questions related to exercise. This allows for more effective information collection by providing the user with an optimal question pattern.

[0061] The information collection unit uses the emotion estimation function to analyze the emotions of the user while they are speaking in real time and can ask questions that elicit positive emotions. The information collection unit, for example, analyzes the user's speaking style and facial expressions to estimate emotions in real time. For example, if the user smiles a lot, it determines that the emotion is positive. The information collection unit also analyzes the tone and speed of the user's voice to estimate emotions. For example, if the voice is bright and fast, it determines that the emotion is positive and can ask questions that further elicit positive emotions. This enables more effective information collection by eliciting positive emotions from the user.

[0062] The information collection unit can also collect health information about the user's family and friends using a conversational AI interface and propose a health plan for the entire family. The information collection unit, for example, asks questions to collect health information about the user's family and friends. For example, it may ask, "How is the health of your family?" The information collection unit also proposes a health plan for the entire family based on the collected health information of family and friends. For example, it can suggest menus that will encourage the whole family to eat healthy meals. This allows the health of the entire family to be supported by proposing a health plan for the entire family.

[0063] The information collection unit can make the conversational AI interface multilingual, enabling it to accommodate users from different languages ​​and cultural backgrounds. For example, the information collection unit builds multiple language databases to make the conversational AI interface multilingual. For example, it collects language data such as English, Spanish, and Chinese. The information collection unit also customizes the interface based on cultural background to accommodate users from different cultural backgrounds. For example, it can ask questions that take into account local customs and values. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0064] The information collection unit can use the emotion estimation function to analyze the emotion a user expresses when speaking and provide feedback according to that emotion. The information collection unit, for example, analyzes the user's speaking style and facial expressions to estimate the emotion in real time. For example, if the user smiles frequently, it can determine that the emotion is positive. The information collection unit also analyzes the tone and speed of the user's voice to estimate the emotion. For example, if the voice is bright and fast, it can determine that the emotion is positive and provide further positive feedback. This makes it possible to provide appropriate feedback according to the user's emotion.

[0065] The data analysis unit collects data from the wearable device in real time and can immediately reflect changes in the user's health condition. The data analysis unit, for example, collects data from the wearable device in real time and monitors the user's health condition. For example, it displays heart rate and step count in real time. The data analysis unit also immediately reflects changes in the user's health condition based on the collected data. For example, it can issue an alert if the heart rate suddenly rises. This allows changes in the user's health condition to be reflected immediately, enabling a quick response.

[0066] The data analysis unit can analyze data from the wearable device, predict health risks for the user, and suggest preventive measures. The data analysis unit, for example, analyzes data collected from the wearable device and predicts health risks for the user. For example, the data analysis unit evaluates risks based on heart rate and sleep data. The data analysis unit also suggests appropriate preventive measures based on the predicted health risks. For example, the data analysis unit can recommend exercise or dietary improvements. This allows the user's health risks to be predicted and appropriate preventive measures to be suggested, improving health management.

[0067] The data analysis unit can use the emotion estimation function to associate the user's physiological data with their emotional state and suggest measures to improve stress management and mental health. The data analysis unit, for example, analyzes physiological data collected from a wearable device and uses the emotion estimation function to estimate the user's emotional state. For example, it can evaluate stress levels based on heart rate fluctuations. The data analysis unit can also use the emotion estimation function to associate the user's physiological data with their emotional state and suggest measures to improve stress management and mental health. For example, it can recommend relaxation techniques or counseling. This enables the user to improve their stress management and mental health.

[0068] The data analysis unit can link data from the wearable device with other smart devices to perform comprehensive health management. For example, the data analysis unit can link data from the wearable device with smart home devices to perform comprehensive health management. For example, the data analysis unit can adjust the room temperature based on heart rate data. The data analysis unit can also link with other smart devices to comprehensively manage the user's health condition. For example, the data analysis unit can link with a fitness tracker to collect exercise data and reflect it in health management. This makes comprehensive health management possible by linking with smart devices.

[0069] The data analysis unit can analyze data from the wearable device in association with the user's work environment and living environment and make suggestions for improving the environment. For example, the data analysis unit analyzes data collected from the wearable device in association with the user's work environment and living environment. For example, it evaluates the stress level at work. Furthermore, the data analysis unit makes suggestions for appropriate environmental improvements based on the analysis results. For example, it can suggest adjusting the temperature and lighting in the work environment. This makes it possible to make appropriate environmental improvements based on the user's work environment and living environment.

[0070] The data analysis unit can track the user's eating patterns and nutritional balance over the long term based on the analysis results of the food photos and suggest areas for improvement. The data analysis unit, for example, stores the analysis results of the food photos in a database and tracks the user's eating patterns and nutritional balance over the long term. For example, it records the contents of daily meals. The data analysis unit also suggests areas for improvement to the user's eating patterns and nutritional balance based on the analysis results. For example, it can suggest ingredients to make up for nutrient deficiencies. This makes it possible to improve the user's eating patterns and nutritional balance.

[0071] The data analysis unit can compare the analysis results of the meal photos with the data of other users and provide a benchmark. The data analysis unit, for example, compares the analysis results of the meal photos with the data of other users and provides a benchmark. For example, it compares the diet of users of the same age or with the same health goals. The data analysis unit also suggests improvements to the user's diet based on the benchmark results. For example, it can suggest ingredients to supplement nutrients that are lacking compared to other users. This allows the user to improve their diet by comparing with the data of other users.

[0072] The data analysis unit takes into account the user's emotional state when analyzing food photos using the emotion estimation function, and can make meal suggestions according to the user's emotions. For example, when analyzing food photos, the data analysis unit estimates the user's emotional state in real time. For example, it analyzes facial expressions and tone of voice while eating. The data analysis unit also uses the emotion estimation function to make meal suggestions according to the user's emotional state. For example, if the user is under high stress, it can suggest meals that have a relaxing effect. This makes it possible to make appropriate meal suggestions according to the user's emotional state.

[0073] The data analysis unit can save the analysis results of the meal photos as the user's meal history and compare it with past data to evaluate progress. The data analysis unit, for example, saves the analysis results of the meal photos as the user's meal history in a database. For example, it records the contents of daily meals. The data analysis unit also compares it with past data to evaluate the progress of the user's eating habits. For example, it can evaluate the degree of improvement in nutritional balance and revise the meal plan as necessary. This makes it possible to improve eating habits by evaluating progress based on the user's meal history.

[0074] The data analysis unit not only reflects the analysis results of the meal photos in the user's meal plan, but can also provide recipe suggestions and ingredient shopping lists. The data analysis unit, for example, customizes the user's meal plan based on the analysis results of the meal photos. For example, it proposes meal menus that take nutritional balance into consideration. The data analysis unit also provides recipe suggestions and ingredient shopping lists. For example, it can generate a list of necessary ingredients based on the proposed meal menu and suggest where to purchase them. This makes meal management easier by providing recipe suggestions and ingredient shopping lists in addition to the user's meal plan.

[0075] The data analysis unit takes into account the user's emotional state when analyzing food photos using the emotion estimation function, and can make meal suggestions according to the user's emotions. For example, when analyzing food photos, the data analysis unit estimates the user's emotional state in real time. For example, it analyzes facial expressions and tone of voice while eating. The data analysis unit also uses the emotion estimation function to make meal suggestions according to the user's emotional state. For example, if the user is under high stress, it can suggest meals that have a relaxing effect. This makes it possible to make appropriate meal suggestions according to the user's emotional state.

[0076] When integrating multimodal data, the data analysis unit performs a detailed analysis of the correlations between the data, enabling the provision of a more accurate health plan. The data analysis unit, for example, integrates multimodal data and performs a detailed analysis of the correlations between the data. For example, it evaluates the relationship between dietary data and exercise data. The data analysis unit also provides a more accurate health plan based on the results of the correlation analysis. For example, it can propose a health plan that takes into account the balance between dietary content and exercise volume. This makes it possible to provide a more accurate health plan by analyzing the correlations between the data in detail.

[0077] The data analysis unit can predict the user's health condition using multimodal data and suggest preventive measures. The data analysis unit, for example, analyzes the multimodal data and predicts the user's health condition. For example, it evaluates health risks based on dietary data and exercise data. The data analysis unit also suggests appropriate preventive measures based on the predicted health condition. For example, it can recommend exercise or dietary improvements. This allows the user's health condition to be predicted and appropriate preventive measures to be suggested, thereby improving health management.

[0078] The data analysis unit can take the user's emotional state into consideration when analyzing multimodal data using the emotion estimation function, and provide a health plan that matches the user's emotions. For example, when analyzing multimodal data, the data analysis unit estimates the user's emotional state in real time. For example, it associates dietary data and exercise data with the emotional state. The data analysis unit also uses the emotion estimation function to provide a health plan that matches the user's emotional state. For example, if the user is highly stressed, it can suggest a health plan that has a relaxing effect. This makes it possible to provide an appropriate health plan that matches the user's emotional state.

[0079] The data analysis unit can integrate the multimodal data with other health data to provide comprehensive health management. For example, the data analysis unit can integrate the multimodal data with medical records and genetic information to build a system for comprehensive health management. For example, health risks can be assessed based on genetic information. The data analysis unit can also integrate the data with other health data to comprehensively manage the user's health status. For example, medical records and fitness data can be integrated and reflected in health management. This makes it possible to provide comprehensive health management by integrating the data with other health data.

[0080] The data analysis unit can monitor the user's health condition in real time using the multimodal data and issue an alert if an abnormality is detected. The data analysis unit, for example, monitors the multimodal data in real time to monitor the user's health condition. For example, it detects abnormalities in heart rate or blood pressure. The data analysis unit also issues an alert if an abnormality is detected. For example, it can issue a notification if the heart rate suddenly increases. This allows the user's health condition to be monitored in real time and allows for a quick response if an abnormality is detected.

[0081] The data analysis unit can take the user's emotional state into consideration when analyzing multimodal data using the emotion estimation function, and provide a health plan that matches the user's emotions. For example, when analyzing multimodal data, the data analysis unit estimates the user's emotional state in real time. For example, it associates dietary data and exercise data with the emotional state. The data analysis unit also uses the emotion estimation function to provide a health plan that matches the user's emotional state. For example, if the user is highly stressed, it can suggest a health plan that has a relaxing effect. This makes it possible to provide an appropriate health plan that matches the user's emotional state.

[0082] The plan generation unit can dynamically adjust the meal plan and fitness plan according to the user's health goals and lifestyle. The plan generation unit, for example, builds a system that dynamically adjusts the meal plan and fitness plan according to the user's health goals and lifestyle. For example, it proposes a meal menu according to a target weight. The plan generation unit also adjusts the fitness plan based on the user's lifestyle. For example, it can propose an appropriate exercise plan for a user who does a lot of desk work. This makes it possible to provide an appropriate plan according to the user's health goals and lifestyle.

[0083] The plan generation unit can periodically evaluate the effectiveness of the meal plan and fitness plan and modify the plan as necessary. The plan generation unit, for example, builds a system that periodically evaluates the effectiveness of the meal plan and fitness plan. For example, the plan generation unit periodically monitors the user's health condition. The plan generation unit also modifies the plan as necessary based on the evaluation results. For example, the meal menu and exercise plan can be adjusted depending on the degree of goal achievement. This allows for more effective health management by periodically evaluating the effectiveness of the plan and modifying it as necessary.

[0084] The plan generation unit can use the emotion estimation function to consider the user's emotional state and provide a meal plan and a fitness plan that correspond to the user's emotions. The plan generation unit, for example, uses the emotion estimation function to estimate the user's emotional state in real time. For example, if the user is highly stressed, the plan generation unit can suggest meals and exercises that have a relaxing effect. The plan generation unit also provides a meal plan and a fitness plan that correspond to the user's emotional state. For example, it can suggest meal menus and exercise plans that elicit positive emotions. This makes it possible to provide an appropriate meal plan and fitness plan that corresponds to the user's emotional state.

[0085] The plan generation unit can customize the meal plan and fitness plan according to the user's work environment and living environment. The plan generation unit builds a system that customizes the meal plan and fitness plan based on the user's work environment and living environment, for example. For example, it proposes an appropriate exercise plan for a user who does a lot of desk work. The plan generation unit also adjusts the meal plan based on the user's living environment. For example, it can propose easy-to-prepare meal menus for a user who leads a busy life. This makes it possible to provide an appropriate plan according to the user's work environment and living environment.

[0086] The plan generation unit can use the emotion estimation function to consider the user's emotional state and provide a meal plan and a fitness plan that correspond to the user's emotions. The plan generation unit, for example, uses the emotion estimation function to estimate the user's emotional state in real time. For example, if the user is highly stressed, the plan generation unit can suggest meals and exercises that have a relaxing effect. The plan generation unit also provides a meal plan and a fitness plan that correspond to the user's emotional state. For example, it can suggest meal menus and exercise plans that elicit positive emotions. This makes it possible to provide an appropriate meal plan and fitness plan that corresponds to the user's emotional state.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The information collection unit can collect the user's hobbies and interests, as well as their health goals and lifestyle, and reflect these in a customized fitness plan. For example, if a user says, "I like yoga," the information collection unit can collect detailed information by asking questions such as, "How often do you do yoga?" The information collection unit can also collect information such as, "I like outdoor activities," and reflect this in the fitness plan. This makes it possible to provide a fitness plan based on the user's hobbies and interests.

[0089] The information collection unit analyzes the user's tone of voice and speaking speed to estimate their stress level and emotional state, and can adjust the content and timing of questions based on this. For example, the information collection unit can analyze the tone and speaking speed of the user's voice in real time to estimate their stress level. For example, if the user's voice is high-pitched and fast, it can determine that the user is stressed and ask questions to help them relax. The information collection unit also analyzes the user's speaking speed to estimate their emotional state. For example, if the user speaks slowly and calmly, it can determine that the user is relaxed and ask more detailed questions. This makes it possible to ask appropriate questions according to the user's stress level and emotional state.

[0090] The information collection unit can analyze the user's past conversation history and generate a question pattern that is optimal for the user. For example, the user's past conversation history is stored in a database and analyzed. For example, the information collection unit analyzes what answers were given to what questions in the past. The information collection unit also generates a question pattern that is optimal for the user based on the past conversation history. For example, if the user previously answered that they are "not good at exercise," the information collection unit can adjust the question pattern to avoid questions related to exercise. This allows for more effective information collection by providing the user with the optimal question pattern.

[0091] The information collection unit uses the emotion estimation function to analyze the emotions of the user while they are speaking in real time and can ask questions that draw out positive emotions. For example, it analyzes the user's speaking style and facial expressions to estimate their emotions in real time. For example, if they smile a lot, it can determine that their emotions are positive. The information collection unit also analyzes the tone and speed of the user's voice to estimate their emotions. For example, if their voice is bright and fast, it can determine that their emotions are positive and ask questions that draw out even more positive emotions. This allows for more effective information collection by drawing out the user's positive emotions.

[0092] The information collection unit can also use a conversational AI interface to collect health information about the user's family and friends, and propose a health plan for the entire family. For example, it can ask questions to collect health information about the user's family and friends, such as, "How is the health of your family?" The information collection unit can also propose a health plan for the entire family based on the collected health information of family and friends. For example, it can suggest menus that will encourage the whole family to eat healthy meals. This allows the health of the entire family to be supported by proposing a health plan for the entire family.

[0093] The information gathering unit can make the conversational AI interface multilingual, enabling it to accommodate users from different languages ​​and cultural backgrounds. For example, to make the conversational AI interface multilingual, multiple language databases are built. For example, language data such as English, Spanish, and Chinese is collected. The information gathering unit also customizes the interface based on cultural background to accommodate users from different cultural backgrounds. For example, it can ask questions that take into account local customs and values. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0094] The information collection unit can use the emotion estimation function to analyze the emotion a user is feeling when speaking and provide feedback according to that emotion. For example, it can analyze the user's speaking style and facial expressions to estimate their emotion in real time. For example, if the user smiles a lot, it can determine that the emotion is positive. The information collection unit can also analyze the tone and speed of the user's voice to estimate their emotion. For example, if the voice is bright and fast, it can determine that the emotion is positive and provide further positive feedback. This makes it possible to provide appropriate feedback according to the user's emotion.

[0095] The data analysis unit collects data from the wearable device in real time and can immediately reflect changes in the user's health condition. For example, data from the wearable device can be collected in real time to monitor the user's health condition. For example, heart rate and step count can be displayed in real time. The data analysis unit also immediately reflects changes in the user's health condition based on the collected data. For example, an alert can be issued if the heart rate suddenly rises. This allows changes in the user's health condition to be reflected immediately, enabling a quick response.

[0096] The data analysis unit can analyze data from the wearable device, predict the user's health risks, and suggest preventive measures. For example, it can analyze data collected from the wearable device and predict the user's health risks. For example, it can evaluate risks based on heart rate and sleep data. The data analysis unit can also suggest appropriate preventive measures based on the predicted health risks. For example, it can recommend exercise or dietary improvements. This allows the user's health risks to be predicted and appropriate preventive measures to be suggested, improving health management.

[0097] The data analysis unit can use the emotion estimation function to associate the user's physiological data with their emotional state and suggest measures to improve stress management and mental health. For example, the data analysis unit analyzes physiological data collected from a wearable device and uses the emotion estimation function to estimate the user's emotional state. For example, the data analysis unit can evaluate the stress level based on heart rate fluctuations. The data analysis unit can also use the emotion estimation function to associate the user's physiological data with their emotional state and suggest measures to improve stress management and mental health. For example, the data analysis unit can recommend relaxation techniques or counseling. This can improve the user's stress management and mental health.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The information gathering unit collects information about the user's health goals, lifestyle, food likes and dislikes, and allergies. For example, if a user says, "I want to lose weight," the information gathering unit will ask questions such as, "How much weight do you want to lose and over what period of time?" to confirm the detailed goal. The information gathering unit will also collect information such as, "I don't like dairy products," and reflect this in the meal plan. Step 2: The data analysis unit analyzes the data collected by the information collection unit. For example, the data analysis unit collects physiological data from the wearable device and analyzes heart rate, step count, sleep data, etc. The data analysis unit also analyzes meal photos uploaded by the user to identify the contents and nutrients of the meal. For example, it analyzes a photo of breakfast taken by the user and provides information such as "This meal is lacking in protein." Step 3: The plan generator generates a customized diet plan and fitness plan based on the data analyzed by the data analyzer. For example, if the user's health goal is to "lose weight" and the data from the wearable device indicates that the user takes fewer steps per day, the plan generator may suggest a fitness plan such as "walk more."

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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. [Explanation of symbols]

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

Claims

1. an information collection unit that collects information about the user's health goals, lifestyle, food likes and dislikes, and allergies; a data analysis unit that analyzes the data collected by the information collection unit; a plan generator that generates a customized diet plan and fitness plan based on the data analyzed by the data analyzer. A system characterized by:

2. The information collecting unit Analyze the user's tone of voice and speaking rate to estimate their stress level and emotional state, and adjust the content and timing of questions accordingly.

2. The system of claim 1.

3. The information collecting unit Using a conversational AI interface, the system also collects health information from the user's family and friends and proposes a health plan for the entire family.

2. The system of claim 1.

4. The data analysis unit Collecting data from wearable devices in real time and immediately reflecting changes in the user's health status 2. The system of claim 1.

5. The data analysis unit Based on the analysis of meal photos, the system tracks the user's eating patterns and nutritional balance over the long term and suggests areas for improvement.

2. The system of claim 1.

6. The data analysis unit When integrating multimodal data, detailed analysis of correlations between said data is performed to provide more accurate health plans.

2. The system of claim 1.

7. The plan generation unit Dynamically adjusting the diet plan and the fitness plan in response to the health goals and lifestyle of the user.

2. The system of claim 1.

8. The plan generation unit Considering the emotional state of the user and providing the diet plan and fitness plan according to the emotions 2. The system of claim 1.

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