System

Abstract

An object of a system according to an embodiment is to perform training and diet management for achieving a goal of a user in an integrated manner.SOLUTION: A system according to an embodiment includes an image generation unit, an exercise menu generation unit, an exercise form analysis unit, a meal plan suggestion unit, and a progress management unit. The image generation unit generates an appearance of the user before and after the goal is achieved as an image. The exercise menu generation unit generates an exercise menu according to a target and a physical strength level of the user. The exercise form analysis unit analyzes an exercise form of a user. The meal plan suggestion unit suggests a meal plan optimal for achieving a goal of the user. The progress management unit manages training progress of the user.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 technologies do not provide a unified approach to training and dietary management aimed at helping users achieve their goals, leaving room for improvement.

[0005] The system according to the embodiment aims to provide a unified training and dietary management system for users to achieve their goals. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, an exercise menu generation unit, an exercise form analysis unit, a meal plan proposal unit, and a progress management unit. The image generation unit generates images of how the user looks before and after achieving their goal. The exercise menu generation unit generates an exercise menu according to the user's goal and physical fitness level. The exercise form analysis unit analyzes the user's exercise form. The meal plan proposal unit proposes an optimal meal plan for achieving the user's goal. The progress management unit manages the user's training progress. [Effects of the Invention]

[0007] The system according to the embodiment can perform training and dietary management in an integrated manner to achieve the user's goals. [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 personal training system according to an embodiment of the present invention uses an image generation function to display the user's appearance before and after achieving their goal, and not only provides a personalized exercise menu, but also analyzes exercise form, proposes optimal meal plans for achieving goals, manages progress, etc. This allows the personal training system to provide comprehensive support for the user in achieving their goals.

[0029] A personal training system according to an embodiment includes an image generation unit, an exercise menu generation unit, an exercise form analysis unit, a meal plan proposal unit, and a progress management unit. The image generation unit generates images of a user's appearance before and after achieving a goal. For example, the generation AI simulates the user's body shape after achieving the goal based on the user's current body shape data and generates an image. The generation AI can also generate images based on prompts containing information about the user's target body shape. The exercise menu generation unit generates an exercise menu according to the user's goals and physical fitness level. For example, the generation AI can suggest exercises to train specific muscle groups for a user aiming to increase muscle strength. The generation AI can also generate an exercise menu according to the user's physical fitness level. The exercise form analysis unit analyzes the user's exercise form. For example, the generation AI can analyze a video of the user performing squats to determine whether the user is performing them with the correct form. The generation AI can also analyze the accuracy of the user's form based on the user's exercise video. The meal plan proposal unit proposes an optimal meal plan for achieving the user's goals. For example, the generation AI may propose a meal plan that takes into account calorie restriction and nutritional balance to a user who is trying to lose weight. The generation AI may also generate a meal plan based on the user's goals and dietary information. The progress management unit manages the user's training progress. For example, the generation AI may visualize the progress the user has made toward the goals they have set. The generation AI may also manage progress based on the user's training data and progress information. This allows the personal training system according to the embodiment to provide comprehensive support for users in achieving their goals. For example, users can maintain their motivation by checking images of their appearance before and after achieving their goals, and can effectively achieve their goals by implementing personalized exercise menus and meal plans. Furthermore, the analysis of exercise form and progress management can improve the quality of training.

[0030] The generative AI generates a 3D model of what the user will look like after achieving their goal, allowing them to check it in a 360-degree view. For example, the generative AI generates a 3D model based on the user's current body shape data and simulates what their body shape will look like after achieving their goal. The user can rotate this 3D model 360 degrees to check it. The generative AI can also set the display resolution and interactive operation method of the 3D model, allowing the user to check in detail what they will look like after achieving their goal.

[0031] The generative AI simulates what the user will look like after achieving their goal at different times of the day and in different seasons, providing the user with a variety of perspectives. For example, the generative AI simulates what the user will look like after achieving their goal at different times of the day, such as morning, noon, and night. This allows the user to visually check changes in their body shape throughout the day. The generative AI can also perform simulations that take into account changes in temperature and light depending on the season. This allows the user to see what they will look like after achieving their goal from a variety of perspectives.

[0032] The generative AI simulates what the user will look like after achieving their goal with different fashion styles and hairstyles, suggesting new ways of self-expression to the user. For example, the generative AI simulates what the user will look like after achieving their goal with different fashion styles. For example, the user can try out styles such as casual, formal, and sporty. The generative AI also simulates what the user will look like after achieving their goal with different hairstyles. For example, the user can try out styles such as short, long, and permed. This makes it easier for users to find new ways of self-expression.

[0033] The AI ​​generates images that compare the user's appearance after achieving their goal with that of family and friends, thereby increasing the user's motivation. For example, the AI ​​generates images that compare the user's appearance after achieving their goal with the current appearance of their family and friends. This makes it easier for the user to realize the changes they have made since achieving their goal. The AI ​​can also provide messages to increase the user's motivation based on the comparison images with family and friends. This makes it easier for the user to increase their motivation.

[0034] The generation AI adjusts the optimal exercise menu in real time based on the user's past exercise history and physical condition data. For example, the generation AI analyzes the user's past exercise history and combines it with current physical condition data to generate the optimal exercise menu. For example, it suggests exercises that match the user's current physical condition based on past training data. The generation AI can also monitor the user's physical condition data in real time and adjust the exercise menu as appropriate. This allows the user to receive the optimal exercise menu in real time.

[0035] The generation AI collects feedback on the user's exercise menu and reflects it in the next menu. For example, the generation AI collects feedback on the user's exercise menu and adjusts the next menu based on that data. For example, if the user finds a particular exercise difficult, the generation AI adjusts that exercise. The generation AI can also evaluate the effectiveness of the exercise menu based on the user's feedback and reflect it in the next menu. This makes it possible to provide an exercise menu that reflects the user's feedback.

[0036] The generation AI combines music and rhythm with the user's exercise menu to enhance entertainment value. For example, the generation AI automatically generates music that matches the user's exercise menu and plays it during exercise. For example, it provides a beat that matches the rhythm of the exercise. The generation AI can also select music that matches the user's preferences and combine it with the exercise menu. This makes it easier for users to enjoy exercise.

[0037] The generating AI will share the user's exercise menu with other users, encouraging competition and cooperation within the community. The generating AI will, for example, share the user's exercise menu with other users, encouraging competition within the community. For example, a ranking function could be introduced, allowing users to compete over their exercise results. The generating AI can also share the user's exercise menu and train together. This makes it easier for users to increase their motivation through competition and cooperation with other users.

[0038] The generating AI analyzes the user's exercise form from different angles and suggests more accurate form corrections. For example, the generating AI analyzes the user's exercise form from different angles to check whether the form is correct. For example, it analyzes footage from the front, side, and back. The generating AI can also suggest form corrections based on footage from different angles. This allows the user to receive more accurate form corrections.

[0039] The generative AI compares the user's exercise form with that of other users, providing an opportunity to learn best practices. The generative AI, for example, compares the user's exercise form with that of top athletes and points out areas for improvement. The generative AI can also suggest best practices based on the exercise form of other users, allowing the user to learn from other users' best practices.

[0040] The generation AI applies the user's exercise form to different exercise types to improve overall athletic ability. The generation AI applies the user's exercise form to different exercise types to improve overall athletic ability. For example, it can improve jumping or running form based on squat form. The generation AI can also suggest form modifications according to different exercise types. This allows the user to improve their overall athletic ability.

[0041] The generating AI adjusts the optimal meal plan in real time based on the user's dietary history and nutritional data. For example, the generating AI analyzes the user's dietary history and combines it with current nutritional data to generate the optimal meal plan. For example, it proposes a meal plan that matches the user's current nutritional balance based on past dietary data. The generating AI can also monitor the user's nutritional data in real time and adjust the meal plan as appropriate. This allows the user to receive the optimal meal plan in real time.

[0042] The generation AI collects feedback on the user's meal plan and reflects it in the next plan. For example, the generation AI collects feedback on the user's meal plan and adjusts the next plan based on that data. For example, if the user does not like a particular ingredient, it will exclude that ingredient. The generation AI can also evaluate the effectiveness of the meal plan based on the user's feedback and reflect it in the next plan. This makes it possible to provide a meal plan that reflects the user's feedback.

[0043] The generative AI suggests new recipes and ingredients for the user's meal plan, increasing the variety of meals. The generative AI, for example, suggests new recipes for the user's meal plan, increasing the variety of meals. For example, it suggests new dishes that match the user's preferences and nutritional balance. The generative AI can also suggest new recipes based on seasonal ingredients or nutritious ingredients. This allows users to increase the variety of their meals.

[0044] The generative AI shares the user's meal plan with other users and promotes recipe exchange and feedback within the community. The generative AI, for example, shares the user's meal plan with other users and promotes recipe exchange within the community. For example, a user posts their own meal plan and receives feedback from other users. The generative AI can also improve the user's meal plan through recipe exchange within the community. This makes it easier for the user to improve their meal plan through recipe exchange and feedback with other users.

[0045] The generation AI analyzes the user's progress data in detail and proposes a specific action plan to achieve the goal. The generation AI analyzes the user's progress data in detail and proposes a specific action plan to achieve the goal. For example, it may propose recommended exercises as the next step based on the user's training data. The generation AI can also set short-term, medium-term, and long-term goals based on the user's progress data and evaluate the progress of each. This allows the user to receive a specific action plan.

[0046] The generating AI compares the user's progress data with other users and provides a benchmark. For example, the generating AI compares the user's progress data with other users' progress data to evaluate the user's progress. The generating AI can also set a benchmark based on the average scores of other users or the data of top performers, making it easier for users to evaluate their own progress in comparison with other users.

[0047] The generation AI visualizes the user's progress data, making it easier to understand intuitively. For example, the generation AI visualizes the user's progress data as graphs or charts, making it easier to understand intuitively. For example, it can display weight changes or training results in a line graph. The generation AI can also visually represent the progress data using infographics, making it easier for the user to intuitively understand the progress data.

[0048] The generation AI reevaluates the user's progress data for different goals and time periods, and supports new goal setting. The generation AI reevaluates the user's progress data for different goals and time periods, and supports new goal setting. For example, it can set short-term and long-term goals and evaluate the progress of each. The generation AI can also set SMART goals based on the user's progress data, and set specific goals. This makes it easier for users to receive support in setting new goals.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The personal training system can also analyze the user's sleep data and suggest optimal training and rest times. For example, it can analyze the user's sleep patterns and suggest the most effective training times. It can also provide advice on improving sleep quality. This helps users optimize the balance between training and rest.

[0051] The personal training system can also monitor the user's stress level and suggest exercises to reduce stress. For example, it can analyze the user's heart rate and breathing patterns and suggest relaxation exercises if it determines that stress is high. It can also provide guided breathing exercises and meditation to reduce stress, helping users to manage stress more effectively.

[0052] The personal training system can also provide a function to look back on past successes based on the user's exercise history. For example, it can display the goals the user has achieved in the past and successful training plans to increase motivation. It can also help users set new goals based on past successes. This makes it easier for users to take on new goals by leveraging their past successes.

[0053] The personal training system can also use the user's exercise data to suggest nutritional supplements to maximize the effectiveness of training. For example, it can suggest protein supplements to users who are looking to increase their muscle strength. It can also suggest vitamin and mineral supplements to promote fatigue recovery. This makes it easier for users to maximize the effectiveness of their training.

[0054] The personal training system can also generate animations to visualize the effects of training based on the user's exercise data. For example, it can display muscle growth or fat loss as animations, allowing the user to intuitively understand the effects of training. It can also display training progress as animations, making it easier for the user to visually confirm the effects of training.

[0055] The personal training system can also use the user's exercise data to suggest rest days and recovery plans to maximize the effectiveness of training. For example, it can set appropriate rest days to avoid overtraining. It can also suggest stretching and light exercise to promote recovery. This makes it easier for users to maximize the effectiveness of their training.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image generator generates images of the user's appearance before and after achieving their goal. For example, the generator AI simulates the user's body shape after achieving their goal based on the user's current body shape data and generates an image. The generator AI can also generate images based on prompts containing information about the user's goal body shape. Step 2: The exercise menu generator generates an exercise menu according to the user's goals and physical fitness level. For example, the generation AI will suggest exercises to train specific muscle groups for a user who wants to improve their muscle strength. The generation AI can also generate an exercise menu according to the user's physical fitness level. Step 3: The exercise form analysis unit analyzes the user's exercise form. For example, the generation AI analyzes a video of the user performing a squat to determine whether the form is correct. The generation AI can also analyze the accuracy of the user's exercise form based on the user's exercise video. Step 4: The meal plan suggestion unit proposes the optimal meal plan for achieving the user's goals. For example, the generation AI will propose a meal plan that takes into account calorie restriction and nutritional balance for a user who is trying to lose weight. The generation AI can also generate a meal plan based on the user's goals and dietary information. Step 5: The progress management unit manages the user's training progress. For example, the generation AI visualizes the progress the user has made toward the goals they set. The generation AI can also manage the user's progress based on their training data and information about their progress.

[0058] (Example 2) The personal training system according to an embodiment of the present invention uses an image generation function to display the user's appearance before and after achieving their goal, and not only provides a personalized exercise menu, but also analyzes exercise form, proposes optimal meal plans for achieving goals, manages progress, etc. This allows the personal training system to provide comprehensive support for the user in achieving their goals.

[0059] A personal training system according to an embodiment includes an image generation unit, an exercise menu generation unit, an exercise form analysis unit, a meal plan proposal unit, and a progress management unit. The image generation unit generates images of a user's appearance before and after achieving a goal. For example, the generation AI simulates the user's body shape after achieving the goal based on the user's current body shape data and generates an image. The generation AI can also generate images based on prompts containing information about the user's target body shape. The exercise menu generation unit generates an exercise menu according to the user's goals and physical fitness level. For example, the generation AI can suggest exercises to train specific muscle groups for a user aiming to increase muscle strength. The generation AI can also generate an exercise menu according to the user's physical fitness level. The exercise form analysis unit analyzes the user's exercise form. For example, the generation AI can analyze a video of the user performing squats to determine whether the user is performing them with the correct form. The generation AI can also analyze the accuracy of the user's form based on the user's exercise video. The meal plan proposal unit proposes an optimal meal plan for achieving the user's goals. For example, the generation AI may propose a meal plan that takes into account calorie restriction and nutritional balance to a user who is trying to lose weight. The generation AI may also generate a meal plan based on the user's goals and dietary information. The progress management unit manages the user's training progress. For example, the generation AI may visualize the progress the user has made toward the goals they have set. The generation AI may also manage progress based on the user's training data and progress information. This allows the personal training system according to the embodiment to provide comprehensive support for users in achieving their goals. For example, users can maintain their motivation by checking images of their appearance before and after achieving their goals, and can effectively achieve their goals by implementing personalized exercise menus and meal plans. Furthermore, the analysis of exercise form and progress management can improve the quality of training.

[0060] The generative AI generates a 3D model of what the user will look like after achieving their goal, allowing them to check it in a 360-degree view. For example, the generative AI generates a 3D model based on the user's current body shape data and simulates what their body shape will look like after achieving their goal. The user can rotate this 3D model 360 degrees to check it. The generative AI can also set the display resolution and interactive operation method of the 3D model, allowing the user to check in detail what they will look like after achieving their goal.

[0061] The generative AI simulates what the user will look like after achieving their goal at different times of the day and in different seasons, providing the user with a variety of perspectives. For example, the generative AI simulates what the user will look like after achieving their goal at different times of the day, such as morning, noon, and night. This allows the user to visually check changes in their body shape throughout the day. The generative AI can also perform simulations that take into account changes in temperature and light depending on the season. This allows the user to see what they will look like after achieving their goal from a variety of perspectives.

[0062] The emotion estimation function analyzes the emotions a user feels about their appearance after achieving a goal and provides feedback to elicit positive emotions. The emotion estimation function, for example, analyzes in real time the emotions a user feels about their appearance after achieving a goal. For example, it analyzes the user's facial expressions and voice, and if positive emotions are strong, it provides feedback to reinforce those emotions. The emotion estimation function can also evaluate emotions based on the user's biometric data and provide advice to elicit positive emotions. This makes it easier for users to maintain positive emotions.

[0063] The generative AI simulates what the user will look like after achieving their goal with different fashion styles and hairstyles, suggesting new ways of self-expression to the user. For example, the generative AI simulates what the user will look like after achieving their goal with different fashion styles. For example, the user can try out styles such as casual, formal, and sporty. The generative AI also simulates what the user will look like after achieving their goal with different hairstyles. For example, the user can try out styles such as short, long, and permed. This makes it easier for users to find new ways of self-expression.

[0064] The AI ​​generates images that compare the user's appearance after achieving their goal with that of family and friends, thereby increasing the user's motivation. For example, the AI ​​generates images that compare the user's appearance after achieving their goal with the current appearance of their family and friends. This makes it easier for the user to realize the changes they have made since achieving their goal. The AI ​​can also provide messages to increase the user's motivation based on the comparison images with family and friends. This makes it easier for the user to increase their motivation.

[0065] The emotion estimation function monitors in real time how the user feels about their appearance after achieving their goal, and provides advice on how to maintain positive emotions. The emotion estimation function, for example, monitors in real time how the user feels about their appearance after achieving their goal. For example, it analyzes the user's facial expressions and voice to check whether positive emotions are being maintained. The emotion estimation function can also evaluate emotions based on the user's biometric data and provide advice on how to maintain positive emotions. This makes it easier for the user to maintain positive emotions.

[0066] The generation AI adjusts the optimal exercise menu in real time based on the user's past exercise history and physical condition data. For example, the generation AI analyzes the user's past exercise history and combines it with current physical condition data to generate the optimal exercise menu. For example, it suggests exercises that match the user's current physical condition based on past training data. The generation AI can also monitor the user's physical condition data in real time and adjust the exercise menu as appropriate. This allows the user to receive the optimal exercise menu in real time.

[0067] The generation AI collects feedback on the user's exercise menu and reflects it in the next menu. For example, the generation AI collects feedback on the user's exercise menu and adjusts the next menu based on that data. For example, if the user finds a particular exercise difficult, the generation AI adjusts that exercise. The generation AI can also evaluate the effectiveness of the exercise menu based on the user's feedback and reflect it in the next menu. This makes it possible to provide an exercise menu that reflects the user's feedback.

[0068] The emotion estimation function analyzes the user's emotions while exercising and suggests an exercise menu to maintain motivation. For example, the emotion estimation function analyzes the user's emotions while exercising in real time and suggests an exercise menu to maintain positive emotions. For example, it can increase the number of exercises that the user enjoys. The emotion estimation function can also analyze the user's facial expressions and voice to evaluate their emotions while exercising. This makes it easier for the user to maintain motivation.

[0069] The generation AI combines music and rhythm with the user's exercise menu to enhance entertainment value. For example, the generation AI automatically generates music that matches the user's exercise menu and plays it during exercise. For example, it provides a beat that matches the rhythm of the exercise. The generation AI can also select music that matches the user's preferences and combine it with the exercise menu. This makes it easier for users to enjoy exercise.

[0070] The generating AI will share the user's exercise menu with other users, encouraging competition and cooperation within the community. The generating AI will, for example, share the user's exercise menu with other users, encouraging competition within the community. For example, a ranking function could be introduced, allowing users to compete over their exercise results. The generating AI can also share the user's exercise menu and train together. This makes it easier for users to increase their motivation through competition and cooperation with other users.

[0071] The emotion estimation function monitors the user's emotions in real time while exercising and provides advice to bring out positive emotions. The emotion estimation function, for example, monitors the user's emotions in real time while exercising and provides advice to bring out positive emotions. For example, it analyzes the user's facial expressions and voice to check whether positive emotions are being maintained. The emotion estimation function can also evaluate the user's emotions based on their biometric data and provide advice to bring out positive emotions. This makes it easier for the user to maintain positive emotions.

[0072] The generating AI analyzes the user's exercise form from different angles and suggests more accurate form corrections. For example, the generating AI analyzes the user's exercise form from different angles to check whether the form is correct. For example, it analyzes footage from the front, side, and back. The generating AI can also suggest form corrections based on footage from different angles. This allows the user to receive more accurate form corrections.

[0073] The emotion estimation function analyzes the emotions a user feels in response to feedback on their exercise form and provides advice to elicit positive emotions. For example, the emotion estimation function analyzes the emotions a user feels in response to feedback on their exercise form in real time and provides advice to elicit positive emotions. For example, if the user feels negative emotions in response to the feedback, it may display an encouraging message. The emotion estimation function can also analyze the user's facial expressions and voice to evaluate their emotions. This makes it easier for the user to maintain positive emotions.

[0074] The generative AI compares the user's exercise form with that of other users, providing an opportunity to learn best practices. The generative AI, for example, compares the user's exercise form with that of top athletes and points out areas for improvement. The generative AI can also suggest best practices based on the exercise form of other users, allowing the user to learn from other users' best practices.

[0075] The generation AI applies the user's exercise form to different exercise types to improve overall athletic ability. The generation AI applies the user's exercise form to different exercise types to improve overall athletic ability. For example, it can improve jumping or running form based on squat form. The generation AI can also suggest form modifications according to different exercise types. This allows the user to improve their overall athletic ability.

[0076] The emotion estimation function monitors the user's emotional response to their exercise form in real time and provides advice to help them maintain positive emotions. For example, the emotion estimation function can display an encouraging message if the user has negative emotions in response to feedback on their exercise form. The emotion estimation function can also analyze the user's facial expressions and voice to evaluate their emotions, making it easier for the user to maintain positive emotions.

[0077] The generating AI adjusts the optimal meal plan in real time based on the user's dietary history and nutritional data. For example, the generating AI analyzes the user's dietary history and combines it with current nutritional data to generate the optimal meal plan. For example, it proposes a meal plan that matches the user's current nutritional balance based on past dietary data. The generating AI can also monitor the user's nutritional data in real time and adjust the meal plan as appropriate. This allows the user to receive the optimal meal plan in real time.

[0078] The generation AI collects feedback on the user's meal plan and reflects it in the next plan. For example, the generation AI collects feedback on the user's meal plan and adjusts the next plan based on that data. For example, if the user does not like a particular ingredient, it will exclude that ingredient. The generation AI can also evaluate the effectiveness of the meal plan based on the user's feedback and reflect it in the next plan. This makes it possible to provide a meal plan that reflects the user's feedback.

[0079] The emotion estimation function analyzes the user's emotions regarding the meal plan and makes meal suggestions to elicit positive emotions. The emotion estimation function, for example, analyzes the user's emotions regarding the meal plan in real time and makes meal suggestions to elicit positive emotions. For example, if the user has negative emotions regarding the meal, it displays an encouraging message. The emotion estimation function can also analyze the user's facial expressions and voice to evaluate their emotions. This makes it easier for the user to maintain positive emotions.

[0080] The generative AI suggests new recipes and ingredients for the user's meal plan, increasing the variety of meals. The generative AI, for example, suggests new recipes for the user's meal plan, increasing the variety of meals. For example, it suggests new dishes that match the user's preferences and nutritional balance. The generative AI can also suggest new recipes based on seasonal ingredients or nutritious ingredients. This allows users to increase the variety of their meals.

[0081] The generative AI shares the user's meal plan with other users and promotes recipe exchange and feedback within the community. The generative AI, for example, shares the user's meal plan with other users and promotes recipe exchange within the community. For example, a user posts their own meal plan and receives feedback from other users. The generative AI can also improve the user's meal plan through recipe exchange within the community. This makes it easier for the user to improve their meal plan through recipe exchange and feedback with other users.

[0082] The emotion estimation function monitors a user's emotional reactions to a meal plan in real time and provides advice to maintain positive emotions. The emotion estimation function, for example, monitors a user's emotional reactions to a meal plan in real time and provides advice to maintain positive emotions. For example, if a user has negative emotions about a meal, it may display an encouraging message. The emotion estimation function can also analyze a user's facial expressions and voice to evaluate their emotions. This makes it easier for the user to maintain positive emotions.

[0083] The generation AI analyzes the user's progress data in detail and proposes a specific action plan to achieve the goal. The generation AI analyzes the user's progress data in detail and proposes a specific action plan to achieve the goal. For example, it may propose recommended exercises as the next step based on the user's training data. The generation AI can also set short-term, medium-term, and long-term goals based on the user's progress data and evaluate the progress of each. This allows the user to receive a specific action plan.

[0084] The generating AI compares the user's progress data with other users and provides a benchmark. For example, the generating AI compares the user's progress data with other users' progress data to evaluate the user's progress. The generating AI can also set a benchmark based on the average scores of other users or the data of top performers, making it easier for users to evaluate their own progress in comparison with other users.

[0085] The emotion estimation function analyzes the user's emotions regarding their progress and provides feedback to elicit positive emotions. The emotion estimation function, for example, analyzes the user's emotions regarding their progress in real time and provides feedback to elicit positive emotions. For example, if the user has negative emotions regarding their progress, it may display an encouraging message. The emotion estimation function can also analyze the user's facial expressions and voice to evaluate their emotions. This makes it easier for the user to maintain positive emotions.

[0086] The generation AI visualizes the user's progress data, making it easier to understand intuitively. For example, the generation AI visualizes the user's progress data as graphs or charts, making it easier to understand intuitively. For example, it can display weight changes or training results in a line graph. The generation AI can also visually represent the progress data using infographics, making it easier for the user to intuitively understand the progress data.

[0087] The generation AI reevaluates the user's progress data for different goals and time periods, and supports new goal setting. The generation AI reevaluates the user's progress data for different goals and time periods, and supports new goal setting. For example, it can set short-term and long-term goals and evaluate the progress of each. The generation AI can also set SMART goals based on the user's progress data, and set specific goals. This makes it easier for users to receive support in setting new goals.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The personal training system can also analyze the user's sleep data and suggest optimal training and rest times. For example, it can analyze the user's sleep patterns and suggest the most effective training times. It can also provide advice on improving sleep quality. This helps users optimize the balance between training and rest.

[0090] The personal training system can also monitor the user's stress level and suggest exercises to reduce stress. For example, it can analyze the user's heart rate and breathing patterns and suggest relaxation exercises if it determines that stress is high. It can also provide guided breathing exercises and meditation to reduce stress, helping users to manage stress more effectively.

[0091] The personal training system can also provide a function to look back on past successes based on the user's exercise history. For example, it can display the goals the user has achieved in the past and successful training plans to increase motivation. It can also help users set new goals based on past successes. This makes it easier for users to take on new goals by leveraging their past successes.

[0092] The emotion estimation function can monitor the user's emotions in real time during training and provide music and messages to elicit positive emotions. For example, if the user feels tired, an encouraging message can be displayed to maintain motivation. It can also play music that matches the user's emotions, making training more enjoyable. This makes it easier for the user to maintain positive emotions.

[0093] The personal training system can also use the user's exercise data to suggest nutritional supplements to maximize the effectiveness of training. For example, it can suggest protein supplements to users who are looking to increase their muscle strength. It can also suggest vitamin and mineral supplements to promote fatigue recovery. This makes it easier for users to maximize the effectiveness of their training.

[0094] The emotion estimation function can analyze the sense of accomplishment or satisfaction a user feels after training and provide feedback to reinforce those feelings. For example, if a user feels positive emotions after training, a message to reinforce those feelings can be displayed. It can also suggest training menus that are more likely to produce a sense of accomplishment. This makes it easier for users to maintain the sense of satisfaction after training.

[0095] The personal training system can also generate animations to visualize the effects of training based on the user's exercise data. For example, it can display muscle growth or fat loss as animations, allowing the user to intuitively understand the effects of training. It can also display training progress as animations, making it easier for the user to visually confirm the effects of training.

[0096] The emotion estimation function can monitor the anxiety and tension the user feels during training in real time and provide advice on how to relax. For example, if the user feels anxious, it can suggest breathing techniques to help them relax. It can also suggest stretches and light exercises to relieve tension. This makes it easier for users to reduce anxiety and tension during training.

[0097] The personal training system can also use the user's exercise data to suggest rest days and recovery plans to maximize the effectiveness of training. For example, it can set appropriate rest days to avoid overtraining. It can also suggest stretching and light exercise to promote recovery. This makes it easier for users to maximize the effectiveness of their training.

[0098] The emotion estimation function can monitor the joy and enjoyment a user feels during training in real time and suggest exercises to enhance those emotions. For example, it can increase the exercises the user enjoys, making training more enjoyable. It can also suggest exercises that match the user's emotions, helping to maintain motivation during training. This makes training more enjoyable for the user.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The image generator generates images of the user's appearance before and after achieving their goal. For example, the generator AI simulates the user's body shape after achieving their goal based on the user's current body shape data and generates an image. The generator AI can also generate images based on prompts containing information about the user's goal body shape. Step 2: The exercise menu generator generates an exercise menu according to the user's goals and physical fitness level. For example, the generation AI will suggest exercises to train specific muscle groups for a user who wants to improve their muscle strength. The generation AI can also generate an exercise menu according to the user's physical fitness level. Step 3: The exercise form analysis unit analyzes the user's exercise form. For example, the generation AI analyzes a video of the user performing a squat to determine whether the form is correct. The generation AI can also analyze the accuracy of the user's exercise form based on the user's exercise video. Step 4: The meal plan suggestion unit proposes the optimal meal plan for achieving the user's goals. For example, the generation AI will propose a meal plan that takes into account calorie restriction and nutritional balance for a user who is trying to lose weight. The generation AI can also generate a meal plan based on the user's goals and dietary information. Step 5: The progress management unit manages the user's training progress. For example, the generation AI visualizes the progress the user has made toward the goals they set. The generation AI can also manage the user's progress based on their training data and information about their progress.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, in order to avoid confusion and to 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.

[0167] 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]

[0168] 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 image generation unit that generates images of the user's appearance before and after achieving the goal; an exercise menu generation unit that generates an exercise menu according to the user's goal and physical fitness level; an exercise form analysis unit that analyzes the exercise form of the user; a meal plan suggestion unit that suggests an optimal meal plan for achieving the user's goals; a progress management unit that manages the training progress of the user. A system characterized by:

2. The generated AI is Generate a 3D model of what the user will look like after achieving their goal, and enable them to check it in a 360-degree view.

2. The system of claim 1.

3. The generated AI is The system simulates the user's appearance after achieving the goal with different fashion styles and hairstyles, and suggests new ways of expressing themselves to the user.

2. The system of claim 1.

4. The generated AI is The optimal exercise menu is adjusted in real time based on the user's past exercise history and physical condition data.

2. The system of claim 1.

5. The generated AI is The exercise form of the user is analyzed as the 3D model, and detailed feedback is provided.

2. The system of claim 1.

6. The generated AI is The optimal meal plan is adjusted in real time based on the user's dietary history and nutritional data.

2. The system of claim 1.

7. The generated AI is Analyze the user's progress data in detail and propose a specific action plan to achieve the goal 2. The system of claim 1.

8. The emotion estimation function is Analyze the user's feelings about their appearance after achieving their goal, and provide feedback to elicit positive emotions 2. The system of claim 1.

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