System

Abstract

An object of a system according to an embodiment is to provide an exercise menu and a meal plan that are personalized according to a training purpose of a user.SOLUTION: A system includes a reception part, a generation part, an analysis part, a proposal part, and a management part. The reception unit inputs a training purpose of a user. The generation unit generates a personalized exercise menu based on the purpose input by the reception unit. The analysis unit analyzes an exercise form of the user on the basis of the exercise menu generated by the generation unit. The proposing section proposes correction on the basis of the exercise form analyzed by the analyzing section. The suggestion unit suggests a meal plan to achieve the goal based on the correction suggested by the suggestion unit. The management unit performs management based on the meal plan proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide personalized exercise menus and meal plans tailored to a user's training goals, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized exercise menu and meal plan according to the user's training goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an analysis unit, a proposal unit, and a management unit. The reception unit inputs the user's training objectives. The generation unit generates a personalized exercise menu based on the objectives input by the reception unit. The analysis unit analyzes the user's exercise form based on the exercise menu generated by the generation unit. The proposal unit suggests modifications based on the exercise form analyzed by the analysis unit. The proposal unit suggests a meal plan for achieving the goal based on the modifications proposed by the proposal unit. The management unit manages based on the meal plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized exercise menu and meal plan according to the user's training 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A personal training system according to an embodiment of the present invention generates a personalized exercise menu based on a user's training goals, analyzes the user's exercise form, suggests modifications, proposes a meal plan, and monitors progress. The personal training system allows the user to input their training goals, and the system generates a personalized exercise menu based on the goals. The system then analyzes the user's exercise form and suggests appropriate modifications. The system also suggests optimal meal plans for achieving the goals and monitors progress. Furthermore, the system uses an image generation function to display the user's "appearance" before and after achieving the goals, thereby motivating the user. For example, in a personal training system, the user inputs their training goals. For example, the user selects goals such as health, weight loss, or muscle building. The system then generates a personalized exercise menu based on the goals. The system then suggests optimal exercise menus based on the user's physical strength and exercise experience. For example, beginners are suggested to start with light exercises, while experienced users are suggested to start with more advanced exercises. The system also analyzes the user's exercise form and suggests appropriate modifications. The system monitors the user's exercise form in real time using cameras and sensors and provides advice on maintaining proper form. For example, if the user's knees are not positioned correctly during squats, the system suggests modifications. The system also suggests optimal meal plans for achieving the goals and monitors progress. The system analyzes the user's dietary history and nutritional balance to suggest an optimal meal plan. It also manages the user's training progress and provides advice for achieving goals. It also uses an image generation function to show the user what they will look like before and after achieving their goal, thereby increasing their motivation. Based on the user's current body shape, the system simulates what their body will look like after achieving their goal and displays it as an image. This makes it easier for users to visualize achieving their goal, increasing their motivation. In this way, the personal training system provides personalized training tailored to their goals, such as health, weight loss, and muscle building, and supports users in achieving their goals through analysis of their exercise form, meal plan suggestions, progress management, and image generation functions.This allows the personal training system to generate a personalized exercise menu based on the user's training goals, analyze their exercise form, suggest modifications, propose meal plans, and manage progress. For example, the user inputs their training goals, and the system generates a personalized exercise menu based on the goals. The system then analyzes the user's exercise form and suggests appropriate modifications. The system also proposes an optimal meal plan for achieving goals and manages progress. Furthermore, the image generation function can be used to show the user's appearance before and after achieving their goals, thereby increasing the user's motivation.

[0029] A personal training system according to an embodiment includes a reception unit, a generation unit, an analysis unit, a suggestion unit, and a management unit. The reception unit inputs a user's training goal. Examples of the user's training goal include, but are not limited to, health, weight loss, and muscle building. The reception unit provides, for example, an interface for the user to select a training goal. The reception unit can also support voice input or text input for inputting the user's training goal. The generation unit generates a personalized exercise menu based on the goal input by the reception unit. The generation unit can suggest an optimal exercise menu based on the user's physical strength and exercise experience. For example, the generation unit can suggest a menu starting with light exercises for beginners and more advanced exercises for experienced users. The generation unit can also simulate a body shape after achieving a goal based on the user's current body shape and present it as an image. For example, the generation unit inputs the user's current body shape data and generates an image using an algorithm that simulates a body shape after achieving a goal. The analysis unit analyzes the user's exercise form based on the exercise menu generated by the generation unit. The analysis unit monitors the user's exercise form in real time using, for example, a camera or a sensor, and provides advice to maintain correct form. For example, the analysis unit suggests corrections if the knee position is incorrect during squats. The analysis unit can also perform a detailed analysis of the user's exercise form and suggest appropriate corrections. The suggestion unit suggests corrections based on the exercise form analyzed by the analysis unit. For example, the suggestion unit suggests corrections if the knee position is incorrect during squats. The suggestion unit can also analyze the user's diet history and nutritional balance to suggest an optimal meal plan. For example, the suggestion unit inputs the user's diet history data and uses an algorithm that analyzes nutritional balance to suggest an optimal meal plan. The management unit manages progress based on the meal plan suggested by the suggestion unit. For example, the management unit manages the user's training progress and provides advice to achieve goals.For example, the management unit inputs the user's training data and uses an algorithm that manages progress to provide advice for achieving goals. This allows the personal training system according to the embodiment to generate a personalized exercise menu according to the user's training objectives, analyze exercise form, suggest corrections, propose meal plans, and manage progress.

[0030] The management unit can manage the user's training progress and provide advice toward achieving goals. The management unit, for example, builds a database for managing the user's training progress. For example, the management unit periodically updates the user's training data and records the progress. The management unit can also provide advice toward achieving goals based on the user's training progress. For example, the management unit analyzes the user's training data and provides advice for adjusting the frequency and intensity of training. Furthermore, the management unit has a function for visually displaying the user's training progress. For example, the management unit visually displays the user's training progress using graphs and charts. This allows the user to grasp their progress at a glance. This makes it easier to maintain the user's motivation by managing the user's training progress and providing advice toward achieving goals.

[0031] The generation unit can simulate the body shape after achieving the goal based on the user's current body shape and present it as an image. The generation unit, for example, inputs the user's current body shape data and generates the image using an algorithm that simulates the body shape after achieving the goal. For example, the generation unit simulates muscle gain and fat loss based on the user's body shape data and presents the body shape after achieving the goal as an image. The generation unit also has a function to visually display the simulation results. For example, the generation unit displays the simulation results as a 3D model so that the user can visually confirm the body shape after achieving the goal. In this way, the user's body shape after achieving the goal can be simulated and presented as an image, thereby increasing the user's motivation.

[0032] The analysis unit can monitor the user's exercise form in real time using a camera or a sensor. The analysis unit can monitor the user's exercise form in real time using, for example, a camera. For example, the analysis unit can analyze video captured by a camera and evaluate the user's exercise form. The analysis unit can also monitor the user's exercise form in real time using a sensor. For example, the analysis unit can analyze data acquired by a sensor and evaluate the user's exercise form. The analysis unit can also analyze the user's exercise form in detail and suggest appropriate corrections. For example, the analysis unit can suggest corrections if the knee position is incorrect during a squat. In this way, by using a camera or a sensor, the user's exercise form can be monitored in real time and advice can be provided to maintain correct form.

[0033] The suggestion unit can analyze the user's diet history and nutritional balance and propose an optimal meal plan. For example, the suggestion unit inputs the user's diet history data and uses an algorithm that analyzes nutritional balance to propose an optimal meal plan. For example, the suggestion unit calculates the necessary nutrients and calories based on the user's diet history and proposes an optimal meal plan. The suggestion unit can also analyze the user's nutritional balance and suggest improvements to the user's diet. For example, the suggestion unit analyzes the user's diet history and, if a specific nutrient is lacking, proposes a meal plan to supplement that nutrient. Furthermore, the suggestion unit has a function to visually display the user's meal plan. For example, the suggestion unit displays the meal plan as a graph or chart, allowing the user to understand the contents of the meal at a glance. In this way, by analyzing the user's diet history and nutritional balance, an optimal meal plan can be proposed and the user can be supported in achieving their goals.

[0034] The suggestion unit may suggest corrections if the knee position is improper during squats. For example, the suggestion unit may suggest corrections if the knee position is improper during squats. For example, if the knee position is incorrect, the suggestion unit may provide advice on correcting the knee angle or position. The suggestion unit may also analyze the user's exercise form in detail and suggest appropriate corrections. For example, if the knee position is incorrect during squats, the suggestion unit may suggest a specific method for correcting the knee position. In this way, by suggesting corrections if the knee position is incorrect during squats, the user's exercise form can be improved and the risk of injury can be reduced.

[0035] The reception unit can analyze the user's past training history and suggest an optimal goal input method. The reception unit, for example, builds a database for analyzing the user's past training history. For example, the reception unit periodically updates the user's past training data and records the history. The reception unit also suggests an optimal goal input method based on the user's past training history. For example, the reception unit automatically displays training goals that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit predicts and suggests training goals to be used in a specific time period based on the user's past training history. In this way, by analyzing the user's past training history, an optimal goal input method can be suggested and input efficiency can be improved.

[0036] The reception unit can filter the training goals based on the user's current health condition or lifestyle habits when the training goal is input. The reception unit, for example, provides an interface for inputting the user's current health condition. For example, the reception unit provides a question-type interface for the user to input the health condition. The reception unit can also provide an interface for inputting the user's lifestyle habits (sleep time, dietary habits, etc.). Furthermore, the reception unit filters the training goals based on the user's health condition and lifestyle habits. For example, when the user inputs their current health condition, the reception unit suggests an appropriate training goal. The reception unit can also suggest an optimal training goal taking the user's lifestyle habits into consideration. Furthermore, the reception unit can filter the training goals based on the user's health data (heart rate, blood pressure, etc.). In this way, by filtering the training goals based on the user's current health condition and lifestyle habits, more appropriate training goals can be suggested.

[0037] When a user inputs a training goal, the reception unit can select an optimal input means according to the user's input method. For example, when the user inputs the training goal by voice, the reception unit supports the input using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software. Also, when the user inputs the training goal by text, an input completion function can be provided. For example, the reception unit analyzes the text entered by the user and presents appropriate candidates. Furthermore, when the user inputs the training goal by image, the reception unit can support the input using image recognition technology. For example, the reception unit analyzes an image uploaded by the user and extracts the training goal. This allows the optimal input means to be selected according to the user's input method, thereby improving input efficiency.

[0038] When inputting a training purpose, the reception unit can prioritize inputting highly relevant purposes by taking into account the user's geographical location information. The reception unit, for example, has a GPS function for acquiring the user's geographical location information. For example, the reception unit acquires the user's current location and takes this into account when inputting the training purpose. The reception unit also suggests highly relevant training purposes based on the user's geographical location information. For example, if the user is near a gym, training purposes that can be done at the gym can be prioritized. Also, if the user is near a park, training purposes that can be done outdoors can be prioritized. Furthermore, if the user is at home, training purposes that can be done at home can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant training purposes can be input preferentially.

[0039] When a training goal is input, the reception unit can analyze the user's social media activity and suggest related goals. The reception unit, for example, builds a database for analyzing the user's social media activity. For example, the reception unit records the content of the user's social media posts and the frequency of activity. The reception unit also suggests related training goals based on the user's social media activity. For example, the reception unit suggests related goals based on training goals shared by the user on social media. The reception unit can also analyze the content of the user's social media posts to suggest related training goals. Furthermore, related training goals can be suggested based on the activities of the user's friends on social media. In this way, related training goals can be suggested by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting for training purposes. The reception unit, for example, builds a database for collecting the user's past feedback. For example, the reception unit records and analyzes the user's past feedback. The reception unit also customizes the input method based on the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The input procedure can also be simplified based on the user's past feedback. Furthermore, the input interface can be improved by analyzing the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback, and input that meets the user's needs can be provided.

[0041] When generating an exercise menu, the generation unit can adjust the level of detail of the menu based on the user's physical strength and exercise experience. The generation unit, for example, builds a database for evaluating the user's physical strength and exercise experience. For example, the generation unit records and analyzes the user's physical strength data and exercise experience. The generation unit also adjusts the level of detail of the exercise menu based on the user's physical strength and exercise experience. For example, a menu that starts with basic exercises is generated for beginners. A menu that includes slightly more difficult exercises is generated for intermediate exercisers. A menu that includes more advanced exercises is generated for advanced exercisers. In this way, by adjusting the level of detail of the menu based on the user's physical strength and exercise experience, it is possible to provide an optimal exercise menu for the user.

[0042] When generating an exercise menu, the generation unit can apply different generation algorithms depending on the user's health condition. The generation unit, for example, builds a database for evaluating the user's health condition. For example, the generation unit records and analyzes the user's health checkup results and self-reported health information. The generation unit also applies different generation algorithms depending on the user's health condition. For example, if the user is in good health, a normal exercise menu is generated. If the user is in poor health, a menu including light exercise is generated. Furthermore, if the user has a specific health problem, an exercise menu that addresses that problem is generated. In this way, by applying different generation algorithms depending on the user's health condition, it is possible to provide the user with an optimal exercise menu.

[0043] When generating an exercise menu, the generation unit can improve the accuracy of the menu by referring to the user's past training results. The generation unit, for example, builds a database for recording the user's past training results. For example, the generation unit periodically updates and records the user's past training data. The generation unit also analyzes the user's past training results to improve the accuracy of the menu. For example, the generation unit generates an effective exercise menu based on the user's past training results. The generation unit can also suggest an optimal exercise menu based on the user's past training history. Furthermore, the user's past training data can be utilized to improve the accuracy of the menu. In this way, by referring to the user's past training results, the accuracy of the menu can be improved and a more effective exercise menu can be provided.

[0044] When generating an exercise menu, the generation unit can determine the priority of the menu based on the start time of training. The generation unit, for example, builds a database for recording the start time of the user's training. For example, the generation unit records and analyzes the start date and season of the user's training. The generation unit also determines the priority of the menu based on the start time of training. For example, if the start time of training is approaching, the generation unit prioritizes the generation of the exercise menu. Also, if the start time of training is far away, the generation unit postpones the generation of the exercise menu. Furthermore, the priority of the menu can be adjusted depending on the start time of training. In this way, by determining the priority of the menu based on the start time of training, an exercise menu that suits the user's schedule can be provided.

[0045] When generating an exercise menu, the generation unit can adjust the order of the menu based on the relevance of the training. The generation unit, for example, builds a database for evaluating the relevance of the training. For example, the generation unit records and analyzes the type, purpose, and effect of the training. The generation unit also adjusts the order of the menu based on the relevance of the training. For example, exercises with high relevance to the training can be preferentially incorporated into the menu. Also, exercises with low relevance to the training can be incorporated into the menu later. Furthermore, the order of the menu can be adjusted based on the relevance of the training. In this way, by adjusting the order of the menu based on the relevance of the training, effective training can be provided.

[0046] When generating an exercise menu, the generation unit can adjust the use of technical terms in the menu according to the user's level of expertise. The generation unit, for example, builds a database for evaluating the user's level of expertise. For example, the generation unit records and analyzes the user's knowledge and years of experience regarding exercise. The generation unit also adjusts the use of technical terms in the menu according to the user's level of expertise. For example, a menu can be provided to beginners using easy-to-understand language that avoids technical terms. Also, a menu can be provided to intermediate exercisers using a moderate amount of technical terms. Furthermore, a menu can be provided to advanced exercisers using a lot of technical terms. In this way, by adjusting the use of technical terms in the menu according to the user's level of expertise, an exercise menu that is easy for the user to understand can be provided.

[0047] The analysis unit can improve the accuracy of the analysis based on the user's body type and muscle strength when analyzing the exercise form. The analysis unit, for example, builds a database for recording the user's body type data. For example, the analysis unit records and analyzes the user's height, weight, body fat percentage, etc. The analysis unit can also build a database for recording the user's muscle strength data. For example, the analysis unit records and analyzes the user's muscle strength test results and the weight they can lift. The analysis unit can also improve the accuracy of the analysis based on the user's body type and muscle strength. For example, the analysis unit sets optimal analysis criteria based on the user's body type data. The analysis accuracy can also be improved based on the user's muscle strength data. This improves the accuracy of the analysis based on the user's body type and muscle strength, making it possible to provide a more accurate analysis of the exercise form.

[0048] When analyzing an exercise form, the analysis unit can perform analysis based on the user's exercise history. The analysis unit, for example, builds a database for recording the user's exercise history. For example, the analysis unit records and analyzes the user's past training content, frequency, and achievement level. The analysis unit also performs analysis based on the user's exercise history. For example, the analysis unit improves the accuracy of the analysis based on the user's past exercise history. It can also set optimal analysis criteria taking the user's exercise history into consideration. Furthermore, it can also improve the accuracy of the analysis by utilizing the user's exercise history. In this way, a more accurate analysis of the exercise form can be provided by taking the user's exercise history into consideration.

[0049] When analyzing an exercise form, the analysis unit can weight the analysis based on the user's exercise frequency. The analysis unit, for example, builds a database for recording the user's exercise frequency. For example, the analysis unit records and analyzes the number of times the user exercises per week and the duration of the exercise. The analysis unit also weights the analysis based on the user's exercise frequency. For example, if the user exercises frequently, a detailed analysis can be performed. Alternatively, if the user does not exercise often, a simplified analysis can be performed. Furthermore, the weighting of the analysis can be adjusted according to the user's exercise frequency. In this way, by weighting the analysis based on the user's exercise frequency, an analysis appropriate for the user can be provided.

[0050] The analysis unit can take the user's geographical environment into consideration when analyzing the exercise form. The analysis unit, for example, builds a database for evaluating the user's geographical environment. For example, the analysis unit records and analyzes the user's weather conditions, topography, and the presence or absence of exercise facilities. The analysis unit also analyzes the exercise form based on the user's geographical environment. For example, if the user is exercising outdoors, the analysis can be performed according to the geographical environment. If the user is exercising at a gym, the analysis can be performed according to the gym's equipment. Furthermore, if the user is exercising at home, the analysis can be performed according to the home environment. This makes it possible to provide a more appropriate analysis of the exercise form by taking the user's geographical environment into consideration.

[0051] The analysis unit can improve the accuracy of the analysis by referring to related exercise science literature when analyzing exercise form. The analysis unit, for example, builds a database for referring to related exercise science literature. For example, the analysis unit records and analyzes the latest exercise science literature and past research reports. The analysis unit also analyzes exercise form by referring to related exercise science literature. For example, the analysis unit improves the accuracy of the analysis by referring to the latest exercise science literature. Analysis standards can also be set based on past exercise science literature. Furthermore, the accuracy of the analysis can be improved by utilizing related exercise science literature. In this way, by referring to related exercise science literature, the accuracy of the analysis can be improved and a more accurate analysis of exercise form can be provided.

[0052] When analyzing an exercise form, the analysis unit can perform an analysis based on the user's level of goal achievement. The analysis unit, for example, builds a database for evaluating the user's level of goal achievement. For example, the analysis unit records and analyzes the number of goals achieved by the user and the level of achievement. The analysis unit also analyzes the exercise form based on the user's level of goal achievement. For example, if the user's level of goal achievement is high, a detailed analysis can be performed. Also, if the user's level of goal achievement is low, a simplified analysis can be performed. Furthermore, the accuracy of the analysis can be adjusted depending on the user's level of goal achievement. This makes it possible to provide a more appropriate analysis of the exercise form by taking the user's level of goal achievement into consideration.

[0053] When proposing a correction, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the user's exercise form. The suggestion unit, for example, builds a database for evaluating the importance of the user's exercise form. For example, the suggestion unit records and analyzes the injury risk and exercise effect of the user's exercise form. The suggestion unit also adjusts the level of detail of the suggestion based on the importance of the user's exercise form. For example, for an important exercise form, a detailed correction suggestion is made. Also, for an exercise form that is not so important, a simplified correction suggestion can be made. Furthermore, the level of detail of the suggestion can be adjusted according to the importance of the user's exercise form. In this way, by adjusting the level of detail of the suggestion based on the importance of the user's exercise form, it is possible to provide a correction suggestion appropriate for the user.

[0054] When proposing modifications, the suggestion unit can apply different suggestion algorithms depending on the user's exercise category. The suggestion unit, for example, builds a database for evaluating the user's exercise category. For example, the suggestion unit records and analyzes the type and purpose of the user's exercise. The suggestion unit also applies different suggestion algorithms depending on the user's exercise category. For example, in the case of strength training, the suggestion unit makes modification suggestions specialized for improving muscle strength. In addition, in the case of aerobic exercise, the suggestion unit can make modification suggestions specialized for improving endurance. Furthermore, in the case of flexibility training, the suggestion unit can make modification suggestions specialized for improving flexibility. In this way, by applying different suggestion algorithms depending on the user's exercise category, it is possible to provide modification suggestions appropriate for the user.

[0055] When proposing a correction, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past correction results. The suggestion unit, for example, builds a database for recording the user's past correction results. For example, the suggestion unit periodically updates and records the user's past correction results. The suggestion unit also analyzes the user's past correction results to improve the accuracy of the suggestion. For example, the suggestion unit makes optimal correction suggestions based on the user's past correction results. The accuracy of the suggestion can also be improved based on the user's past correction history. Furthermore, the accuracy of the suggestion can also be improved by utilizing the user's past correction data. In this way, by referring to the user's past correction results, the accuracy of the suggestion can be improved and more effective correction suggestions can be provided.

[0056] When proposing modifications, the suggestion unit can determine the priority of the suggestions based on the timing of the exercise. The suggestion unit, for example, builds a database for recording the timing of the user's exercise. For example, the suggestion unit records and analyzes the date and season of the user's exercise. The suggestion unit also determines the priority of the suggestions based on the timing of the exercise. For example, the suggestion unit may give priority to suggesting modifications to an exercise that will be performed soon. The suggestion unit may also postpone suggesting modifications to an exercise that is further away. Furthermore, the priority of the suggestions can be adjusted depending on the timing of the exercise. In this way, by determining the priority of the suggestions based on the timing of the exercise, modification suggestions can be provided at an appropriate time for the user.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of the exercises when suggesting modifications. The suggestion unit, for example, builds a database for evaluating the relevance of the exercises. For example, the suggestion unit records and analyzes the type, purpose, and effect of the exercises. The suggestion unit also adjusts the order of suggestions based on the relevance of the exercises. For example, the suggestion unit prioritizes modification suggestions for highly relevant exercises. Also, the suggestion unit can postpone modification suggestions for less relevant exercises. Furthermore, the order of suggestions can be adjusted based on the relevance of the exercises. In this way, by adjusting the order of suggestions based on the relevance of the exercises, it is possible to provide modification suggestions that are effective for the user.

[0058] When proposing a correction, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, builds a database for evaluating the user's level of expertise. For example, the suggestion unit records and analyzes the user's knowledge and years of experience regarding exercise. The suggestion unit also adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, for beginners, the suggestion unit can make correction suggestions using easy-to-understand language while avoiding technical terminology. For intermediate users, the suggestion unit can also make correction suggestions using a moderate amount of technical terminology. Furthermore, for advanced users, the suggestion unit can also make correction suggestions using a lot of technical terminology. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide correction suggestions that are easy for the user to understand.

[0059] The management unit can analyze the user's past training history and select the optimal management method when managing progress. The management unit, for example, builds a database for recording the user's past training history. For example, the management unit records and analyzes the user's past training content, frequency, and achievement level. The management unit also selects the optimal management method based on the user's past training history. For example, the management unit suggests the optimal progress management method based on the user's past training history. The management unit can also analyze the user's past training data to improve the accuracy of progress management. Furthermore, the user's past training history can be used to select the progress management method. In this way, the optimal progress management method can be selected by analyzing the user's past training history, and the user can be supported in achieving their goals.

[0060] The management unit can customize the management means based on the user's current living situation when managing progress. The management unit, for example, builds a database for evaluating the user's current living situation. For example, the management unit records and analyzes the user's work schedule, home environment, and lifestyle habits. The management unit also customizes the management means based on the user's current living situation. For example, the management unit proposes an optimal progress management method taking into consideration the user's current living situation (work, home, etc.). The progress management method can also be customized based on the user's lifestyle habits (sleep time, meal contents, etc.). Furthermore, the progress management means can be adjusted according to the user's living situation. In this way, by customizing the management means based on the user's current living situation, it is possible to provide progress management that is appropriate for the user.

[0061] The management unit can improve the management method by reflecting user feedback during progress management. For example, the management unit builds a database for collecting user feedback. For example, the management unit records and analyzes user feedback. The management unit also improves the management method based on user feedback. For example, the management unit improves the progress management method based on user feedback. The management unit can also analyze past user feedback and propose an optimal progress management method. Furthermore, the user feedback can be utilized to improve the accuracy of progress management. In this way, the management method can be improved by reflecting user feedback, and more effective progress management can be provided.

[0062] The management unit can select the optimal management method by taking into account the user's geographical location information when managing progress. The management unit, for example, has a GPS function for acquiring the user's geographical location information. For example, the management unit acquires the user's current location and takes it into account when managing progress. The management unit also selects the optimal management method based on the user's geographical location information. For example, if the user is near a gym, priority can be given to progress management at the gym. Also, if the user is near a park, priority can be given to outdoor progress management. Furthermore, if the user is at home, priority can be given to progress management at home. In this way, by taking into account the user's geographical location information, the optimal progress management method can be selected and the user can be supported in achieving their goals.

[0063] The management unit can analyze the user's social media activity and suggest management methods when managing progress. The management unit, for example, builds a database for analyzing the user's social media activity. For example, the management unit records and analyzes the content of the user's social media posts and the frequency of activity. The management unit also suggests optimal progress management methods based on the user's social media activity. For example, the management unit can suggest optimal progress management methods based on the user's social media activity. The management unit can also analyze the content of the user's social media posts and customize the progress management method. Furthermore, the management unit can also suggest progress management methods based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest optimal progress management methods and support the user in achieving their goals.

[0064] The management unit can customize the management method by reflecting the user's past feedback when managing progress. For example, the management unit builds a database for collecting the user's past feedback. For example, the management unit records and analyzes the user's past feedback. The management unit also customizes the management method based on the user's past feedback. For example, the management unit proposes an optimal progress management method based on the user's past feedback. The management unit can also analyze the user's past feedback and improve the progress management method. Furthermore, the user's past feedback can be utilized to improve the accuracy of progress management. In this way, the management method can be customized by reflecting the user's past feedback, and more effective progress management can be provided.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When the user inputs a training goal, the reception unit can suggest the most suitable training goal based on the user's past training data. For example, it can prioritize and display training goals that the user has frequently selected in the past. It can also analyze the user's past training data and suggest training goals according to the season or time of day. It can also suggest achievable goals by taking into account the user's past training results. This makes it possible to support the input of more personalized training goals by utilizing the user's past training data.

[0067] The generation unit can dynamically adjust the difficulty level of the exercise menu according to the user's training goal. For example, if the user's goal is to increase muscle strength, the generation unit can suggest an exercise menu that gradually increases the load. Also, if the user's goal is to lose weight, the generation unit can suggest an exercise menu that emphasizes calorie consumption. Furthermore, the generation unit can adjust the difficulty level of the exercise menu in real time based on user feedback. This makes it possible to provide the optimal exercise menu according to the user's training goal.

[0068] When analyzing a user's exercise form, the analysis unit can improve the accuracy of the analysis based on the user's body type and muscle strength. For example, it can propose an optimal exercise form taking into account the user's height, weight, and muscle mass. It can also suggest exercise form modifications based on the user's muscle strength data. Furthermore, it can adjust the analysis criteria for the exercise form according to the user's body type and muscle strength. This makes it possible to provide a highly accurate analysis of the exercise form based on the user's individual body type and muscle strength.

[0069] When analyzing the user's dietary history and nutritional balance, the suggestion unit can propose an optimal meal plan taking into account the user's dietary preferences and allergy information. For example, if the user is allergic to a specific ingredient, the suggestion unit can propose a meal plan that avoids that ingredient. It can also propose a meal plan that prioritizes the user's favorite ingredients. Furthermore, it can propose a meal plan that takes nutritional balance into consideration based on the user's dietary preferences and allergy information. This makes it possible to provide an optimal meal plan based on the user's individual dietary preferences and allergy information.

[0070] When managing a user's training progress, the management unit can adjust the progress management method taking into account the user's lifestyle and schedule. For example, when the user is busy, it can suggest a short and effective training menu. It can also adjust the training time according to the user's lifestyle (for example, whether they are a night owl or a morning person). It can also adjust the frequency and intensity of training based on the user's schedule. This makes it possible to provide flexible progress management that suits the user's lifestyle and schedule.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit inputs the user's training goal. Examples of the user's training goal include, but are not limited to, health, weight loss, and muscle building. The reception unit provides, for example, an interface for the user to select the training goal. The reception unit can also support voice input or text input for inputting the user's training goal. Step 2: The generation unit generates a personalized exercise menu based on the purpose input by the reception unit. The generation unit suggests an optimal exercise menu based on, for example, the user's physical strength and exercise experience. For example, the generation unit suggests a menu starting with light exercise for beginners and more advanced exercise for experienced users. The generation unit can also simulate the body shape after achieving the goal based on the user's current body shape and present it as an image. For example, the generation unit uses an algorithm that takes the user's current body shape data as input and simulates the body shape after achieving the goal to generate an image. Step 3: The analysis unit analyzes the user's exercise form based on the exercise menu generated by the generation unit. The analysis unit monitors the user's exercise form in real time using, for example, a camera or sensor, and provides advice on maintaining correct form. For example, the analysis unit suggests corrections if the knee position is incorrect during a squat. The analysis unit can also analyze the user's exercise form in detail and suggest appropriate corrections. Step 4: The suggestion unit suggests corrections based on the exercise form analyzed by the analysis unit. For example, if the knee position is incorrect during squats, the suggestion unit suggests corrections. The suggestion unit can also analyze the user's diet history and nutritional balance to suggest an optimal meal plan. For example, the suggestion unit inputs the user's diet history data and uses an algorithm that analyzes nutritional balance to suggest an optimal meal plan. Step 5: The management unit manages progress based on the meal plan proposed by the proposal unit. The management unit, for example, manages the user's training progress and provides advice for achieving goals. For example, the management unit inputs the user's training data and uses an algorithm for managing progress to provide advice for achieving goals.

[0073] (Example 2) A personal training system according to an embodiment of the present invention generates a personalized exercise menu based on a user's training goals, analyzes the user's exercise form, suggests modifications, proposes a meal plan, and monitors progress. The personal training system allows the user to input their training goals, and the system generates a personalized exercise menu based on the goals. The system then analyzes the user's exercise form and suggests appropriate modifications. The system also suggests optimal meal plans for achieving the goals and monitors progress. Furthermore, the system uses an image generation function to display the user's "appearance" before and after achieving the goals, thereby motivating the user. For example, in a personal training system, the user inputs their training goals. For example, the user selects goals such as health, weight loss, or muscle building. The system then generates a personalized exercise menu based on the goals. The system then suggests optimal exercise menus based on the user's physical strength and exercise experience. For example, beginners are suggested to start with light exercises, while experienced users are suggested to start with more advanced exercises. The system also analyzes the user's exercise form and suggests appropriate modifications. The system monitors the user's exercise form in real time using cameras and sensors and provides advice on maintaining proper form. For example, if the user's knees are not positioned correctly during squats, the system suggests modifications. The system also suggests optimal meal plans for achieving the goals and monitors progress. The system analyzes the user's dietary history and nutritional balance to suggest an optimal meal plan. It also manages the user's training progress and provides advice for achieving goals. It also uses an image generation function to show the user what they will look like before and after achieving their goal, thereby increasing their motivation. Based on the user's current body shape, the system simulates what their body will look like after achieving their goal and displays it as an image. This makes it easier for users to visualize achieving their goal, increasing their motivation. In this way, the personal training system provides personalized training tailored to their goals, such as health, weight loss, and muscle building, and supports users in achieving their goals through analysis of their exercise form, meal plan suggestions, progress management, and image generation functions.This allows the personal training system to generate a personalized exercise menu based on the user's training goals, analyze their exercise form, suggest modifications, propose meal plans, and manage progress. For example, the user inputs their training goals, and the system generates a personalized exercise menu based on the goals. The system then analyzes the user's exercise form and suggests appropriate modifications. The system also proposes an optimal meal plan for achieving goals and manages progress. Furthermore, the image generation function can be used to show the user's appearance before and after achieving their goals, thereby increasing the user's motivation.

[0074] A personal training system according to an embodiment includes a reception unit, a generation unit, an analysis unit, a suggestion unit, and a management unit. The reception unit inputs a user's training goal. Examples of the user's training goal include, but are not limited to, health, weight loss, and muscle building. The reception unit provides, for example, an interface for the user to select a training goal. The reception unit can also support voice input or text input for inputting the user's training goal. The generation unit generates a personalized exercise menu based on the goal input by the reception unit. The generation unit can suggest an optimal exercise menu based on the user's physical strength and exercise experience. For example, the generation unit can suggest a menu starting with light exercises for beginners and more advanced exercises for experienced users. The generation unit can also simulate a body shape after achieving a goal based on the user's current body shape and present it as an image. For example, the generation unit inputs the user's current body shape data and generates an image using an algorithm that simulates a body shape after achieving a goal. The analysis unit analyzes the user's exercise form based on the exercise menu generated by the generation unit. The analysis unit monitors the user's exercise form in real time using, for example, a camera or a sensor, and provides advice to maintain correct form. For example, the analysis unit suggests corrections if the knee position is incorrect during squats. The analysis unit can also perform a detailed analysis of the user's exercise form and suggest appropriate corrections. The suggestion unit suggests corrections based on the exercise form analyzed by the analysis unit. For example, the suggestion unit suggests corrections if the knee position is incorrect during squats. The suggestion unit can also analyze the user's diet history and nutritional balance to suggest an optimal meal plan. For example, the suggestion unit inputs the user's diet history data and uses an algorithm that analyzes nutritional balance to suggest an optimal meal plan. The management unit manages progress based on the meal plan suggested by the suggestion unit. For example, the management unit manages the user's training progress and provides advice to achieve goals.For example, the management unit inputs the user's training data and uses an algorithm that manages progress to provide advice for achieving goals. This allows the personal training system according to the embodiment to generate a personalized exercise menu according to the user's training objectives, analyze exercise form, suggest corrections, propose meal plans, and manage progress.

[0075] The management unit can manage the user's training progress and provide advice toward achieving goals. The management unit, for example, builds a database for managing the user's training progress. For example, the management unit periodically updates the user's training data and records the progress. The management unit can also provide advice toward achieving goals based on the user's training progress. For example, the management unit analyzes the user's training data and provides advice for adjusting the frequency and intensity of training. Furthermore, the management unit has a function for visually displaying the user's training progress. For example, the management unit visually displays the user's training progress using graphs and charts. This allows the user to grasp their progress at a glance. This makes it easier to maintain the user's motivation by managing the user's training progress and providing advice toward achieving goals.

[0076] The generation unit can simulate the body shape after achieving the goal based on the user's current body shape and present it as an image. The generation unit, for example, inputs the user's current body shape data and generates the image using an algorithm that simulates the body shape after achieving the goal. For example, the generation unit simulates muscle gain and fat loss based on the user's body shape data and presents the body shape after achieving the goal as an image. The generation unit also has a function to visually display the simulation results. For example, the generation unit displays the simulation results as a 3D model so that the user can visually confirm the body shape after achieving the goal. In this way, the user's body shape after achieving the goal can be simulated and presented as an image, thereby increasing the user's motivation.

[0077] The analysis unit can monitor the user's exercise form in real time using a camera or a sensor. The analysis unit can monitor the user's exercise form in real time using, for example, a camera. For example, the analysis unit can analyze video captured by a camera and evaluate the user's exercise form. The analysis unit can also monitor the user's exercise form in real time using a sensor. For example, the analysis unit can analyze data acquired by a sensor and evaluate the user's exercise form. The analysis unit can also analyze the user's exercise form in detail and suggest appropriate corrections. For example, the analysis unit can suggest corrections if the knee position is incorrect during a squat. In this way, by using a camera or a sensor, the user's exercise form can be monitored in real time and advice can be provided to maintain correct form.

[0078] The suggestion unit can analyze the user's diet history and nutritional balance and propose an optimal meal plan. For example, the suggestion unit inputs the user's diet history data and uses an algorithm that analyzes nutritional balance to propose an optimal meal plan. For example, the suggestion unit calculates the necessary nutrients and calories based on the user's diet history and proposes an optimal meal plan. The suggestion unit can also analyze the user's nutritional balance and suggest improvements to the user's diet. For example, the suggestion unit analyzes the user's diet history and, if a specific nutrient is lacking, proposes a meal plan to supplement that nutrient. Furthermore, the suggestion unit has a function to visually display the user's meal plan. For example, the suggestion unit displays the meal plan as a graph or chart, allowing the user to understand the contents of the meal at a glance. In this way, by analyzing the user's diet history and nutritional balance, an optimal meal plan can be proposed and the user can be supported in achieving their goals.

[0079] The suggestion unit may suggest corrections if the knee position is improper during squats. For example, the suggestion unit may suggest corrections if the knee position is improper during squats. For example, if the knee position is incorrect, the suggestion unit may provide advice on correcting the knee angle or position. The suggestion unit may also analyze the user's exercise form in detail and suggest appropriate corrections. For example, if the knee position is incorrect during squats, the suggestion unit may suggest a specific method for correcting the knee position. In this way, by suggesting corrections if the knee position is incorrect during squats, the user's exercise form can be improved and the risk of injury can be reduced.

[0080] The reception unit can estimate the user's emotions and adjust the training goal input method based on the estimated user's emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotions. The reception unit further adjusts the training goal input method based on the user's emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. If the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input to enable the user to quickly input the training goal. In this way, by adjusting the training goal input method according to the user's emotions, it is possible to reduce the user's stress and improve input efficiency.

[0081] The reception unit can analyze the user's past training history and suggest an optimal goal input method. The reception unit, for example, builds a database for analyzing the user's past training history. For example, the reception unit periodically updates the user's past training data and records the history. The reception unit also suggests an optimal goal input method based on the user's past training history. For example, the reception unit automatically displays training goals that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit predicts and suggests training goals to be used in a specific time period based on the user's past training history. In this way, by analyzing the user's past training history, an optimal goal input method can be suggested and input efficiency can be improved.

[0082] The reception unit can filter the training goals based on the user's current health condition or lifestyle habits when the training goal is input. The reception unit, for example, provides an interface for inputting the user's current health condition. For example, the reception unit provides a question-type interface for the user to input the health condition. The reception unit can also provide an interface for inputting the user's lifestyle habits (sleep time, dietary habits, etc.). Furthermore, the reception unit filters the training goals based on the user's health condition and lifestyle habits. For example, when the user inputs their current health condition, the reception unit suggests an appropriate training goal. The reception unit can also suggest an optimal training goal taking the user's lifestyle habits into consideration. Furthermore, the reception unit can filter the training goals based on the user's health data (heart rate, blood pressure, etc.). In this way, by filtering the training goals based on the user's current health condition and lifestyle habits, more appropriate training goals can be suggested.

[0083] When a user inputs a training goal, the reception unit can select an optimal input means according to the user's input method. For example, when the user inputs the training goal by voice, the reception unit supports the input using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software. Also, when the user inputs the training goal by text, an input completion function can be provided. For example, the reception unit analyzes the text entered by the user and presents appropriate candidates. Furthermore, when the user inputs the training goal by image, the reception unit can support the input using image recognition technology. For example, the reception unit analyzes an image uploaded by the user and extracts the training goal. This allows the optimal input means to be selected according to the user's input method, thereby improving input efficiency.

[0084] The reception unit can estimate the user's emotions and prioritize the input training goals based on the estimated user's emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit prioritizes the input training goals based on the user's emotions. For example, if the user is feeling stressed, the reception unit prioritizes training goals that have a relaxing effect. If the user is feeling motivated, the reception unit prioritizes challenging training goals. If the user is tired, the reception unit prioritizes light exercise. In this way, by prioritizing the training goals based on the user's emotions, it is possible to provide training that meets the user's needs.

[0085] When inputting a training purpose, the reception unit can prioritize inputting highly relevant purposes by taking into account the user's geographical location information. The reception unit, for example, has a GPS function for acquiring the user's geographical location information. For example, the reception unit acquires the user's current location and takes this into account when inputting the training purpose. The reception unit also suggests highly relevant training purposes based on the user's geographical location information. For example, if the user is near a gym, training purposes that can be done at the gym can be prioritized. Also, if the user is near a park, training purposes that can be done outdoors can be prioritized. Furthermore, if the user is at home, training purposes that can be done at home can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant training purposes can be input preferentially.

[0086] When a training goal is input, the reception unit can analyze the user's social media activity and suggest related goals. The reception unit, for example, builds a database for analyzing the user's social media activity. For example, the reception unit records the content of the user's social media posts and the frequency of activity. The reception unit also suggests related training goals based on the user's social media activity. For example, the reception unit suggests related goals based on training goals shared by the user on social media. The reception unit can also analyze the content of the user's social media posts to suggest related training goals. Furthermore, related training goals can be suggested based on the activities of the user's friends on social media. In this way, related training goals can be suggested by analyzing the user's social media activity.

[0087] The reception unit can customize the input method by reflecting the user's past feedback when inputting for training purposes. The reception unit, for example, builds a database for collecting the user's past feedback. For example, the reception unit records and analyzes the user's past feedback. The reception unit also customizes the input method based on the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The input procedure can also be simplified based on the user's past feedback. Furthermore, the input interface can be improved by analyzing the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback, and input that meets the user's needs can be provided.

[0088] The generation unit can estimate the user's emotions and adjust the presentation method of the exercise menu based on the estimated user's emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit adjusts the presentation method of the exercise menu based on the user's emotions. For example, if the user is relaxed, the generation unit generates an exercise menu that progresses at a leisurely pace. If the user is in a hurry, the generation unit generates an exercise menu that is effective in a short amount of time. Furthermore, if the user is excited, the generation unit generates an exercise menu that adds visually stimulating effects. In this way, the user's motivation can be increased by adjusting the presentation method of the exercise menu according to the user's emotions.

[0089] When generating an exercise menu, the generation unit can adjust the level of detail of the menu based on the user's physical strength and exercise experience. The generation unit, for example, builds a database for evaluating the user's physical strength and exercise experience. For example, the generation unit records and analyzes the user's physical strength data and exercise experience. The generation unit also adjusts the level of detail of the exercise menu based on the user's physical strength and exercise experience. For example, a menu that starts with basic exercises is generated for beginners. A menu that includes slightly more difficult exercises is generated for intermediate exercisers. A menu that includes more advanced exercises is generated for advanced exercisers. In this way, by adjusting the level of detail of the menu based on the user's physical strength and exercise experience, it is possible to provide an optimal exercise menu for the user.

[0090] When generating an exercise menu, the generation unit can apply different generation algorithms depending on the user's health condition. The generation unit, for example, builds a database for evaluating the user's health condition. For example, the generation unit records and analyzes the user's health checkup results and self-reported health information. The generation unit also applies different generation algorithms depending on the user's health condition. For example, if the user is in good health, a normal exercise menu is generated. If the user is in poor health, a menu including light exercise is generated. Furthermore, if the user has a specific health problem, an exercise menu that addresses that problem is generated. In this way, by applying different generation algorithms depending on the user's health condition, it is possible to provide the user with an optimal exercise menu.

[0091] When generating an exercise menu, the generation unit can improve the accuracy of the menu by referring to the user's past training results. The generation unit, for example, builds a database for recording the user's past training results. For example, the generation unit periodically updates and records the user's past training data. The generation unit also analyzes the user's past training results to improve the accuracy of the menu. For example, the generation unit generates an effective exercise menu based on the user's past training results. The generation unit can also suggest an optimal exercise menu based on the user's past training history. Furthermore, the user's past training data can be utilized to improve the accuracy of the menu. In this way, by referring to the user's past training results, the accuracy of the menu can be improved and a more effective exercise menu can be provided.

[0092] The generation unit can estimate the user's emotions and adjust the length of the exercise menu based on the estimated user's emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit adjusts the length of the exercise menu based on the user's emotions. For example, if the user is relaxed, a longer exercise menu is generated. If the user is in a hurry, a short and effective exercise menu is generated. Furthermore, if the user is excited, an exercise menu with visually stimulating effects is generated. In this way, by adjusting the length of the exercise menu according to the user's emotions, an exercise menu that meets the user's needs can be provided.

[0093] When generating an exercise menu, the generation unit can determine the priority of the menu based on the start time of training. The generation unit, for example, builds a database for recording the start time of the user's training. For example, the generation unit records and analyzes the start date and season of the user's training. The generation unit also determines the priority of the menu based on the start time of training. For example, if the start time of training is approaching, the generation unit prioritizes the generation of the exercise menu. Also, if the start time of training is far away, the generation unit postpones the generation of the exercise menu. Furthermore, the priority of the menu can be adjusted depending on the start time of training. In this way, by determining the priority of the menu based on the start time of training, an exercise menu that suits the user's schedule can be provided.

[0094] When generating an exercise menu, the generation unit can adjust the order of the menu based on the relevance of the training. The generation unit, for example, builds a database for evaluating the relevance of the training. For example, the generation unit records and analyzes the type, purpose, and effect of the training. The generation unit also adjusts the order of the menu based on the relevance of the training. For example, exercises with high relevance to the training can be preferentially incorporated into the menu. Also, exercises with low relevance to the training can be incorporated into the menu later. Furthermore, the order of the menu can be adjusted based on the relevance of the training. In this way, by adjusting the order of the menu based on the relevance of the training, effective training can be provided.

[0095] When generating an exercise menu, the generation unit can adjust the use of technical terms in the menu according to the user's level of expertise. The generation unit, for example, builds a database for evaluating the user's level of expertise. For example, the generation unit records and analyzes the user's knowledge and years of experience regarding exercise. The generation unit also adjusts the use of technical terms in the menu according to the user's level of expertise. For example, a menu can be provided to beginners using easy-to-understand language that avoids technical terms. Also, a menu can be provided to intermediate exercisers using a moderate amount of technical terms. Furthermore, a menu can be provided to advanced exercisers using a lot of technical terms. In this way, by adjusting the use of technical terms in the menu according to the user's level of expertise, an exercise menu that is easy for the user to understand can be provided.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis criteria for the exercise form based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit adjusts the analysis criteria for the exercise form based on the user's emotions. For example, if the user is relaxed, detailed analysis criteria are applied. If the user is in a hurry, simplified analysis criteria are applied. If the user is excited, analysis criteria with visually stimulating effects are applied. In this way, by adjusting the analysis criteria for the exercise form according to the user's emotions, it is possible to provide an appropriate analysis for the user.

[0097] The analysis unit can improve the accuracy of the analysis based on the user's body type and muscle strength when analyzing the exercise form. The analysis unit, for example, builds a database for recording the user's body type data. For example, the analysis unit records and analyzes the user's height, weight, body fat percentage, etc. The analysis unit can also build a database for recording the user's muscle strength data. For example, the analysis unit records and analyzes the user's muscle strength test results and the weight they can lift. The analysis unit can also improve the accuracy of the analysis based on the user's body type and muscle strength. For example, the analysis unit sets optimal analysis criteria based on the user's body type data. The analysis accuracy can also be improved based on the user's muscle strength data. This improves the accuracy of the analysis based on the user's body type and muscle strength, making it possible to provide a more accurate analysis of the exercise form.

[0098] When analyzing an exercise form, the analysis unit can perform analysis based on the user's exercise history. The analysis unit, for example, builds a database for recording the user's exercise history. For example, the analysis unit records and analyzes the user's past training content, frequency, and achievement level. The analysis unit also performs analysis based on the user's exercise history. For example, the analysis unit improves the accuracy of the analysis based on the user's past exercise history. It can also set optimal analysis criteria taking the user's exercise history into consideration. Furthermore, it can also improve the accuracy of the analysis by utilizing the user's exercise history. In this way, a more accurate analysis of the exercise form can be provided by taking the user's exercise history into consideration.

[0099] When analyzing an exercise form, the analysis unit can weight the analysis based on the user's exercise frequency. The analysis unit, for example, builds a database for recording the user's exercise frequency. For example, the analysis unit records and analyzes the number of times the user exercises per week and the duration of the exercise. The analysis unit also weights the analysis based on the user's exercise frequency. For example, if the user exercises frequently, a detailed analysis can be performed. Alternatively, if the user does not exercise often, a simplified analysis can be performed. Furthermore, the weighting of the analysis can be adjusted according to the user's exercise frequency. In this way, by weighting the analysis based on the user's exercise frequency, an analysis appropriate for the user can be provided.

[0100] The analysis unit can estimate the user's emotions and adjust the order in which the exercise form analysis results are displayed based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions captured with a camera to estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit also adjusts the order in which the exercise form analysis results are displayed based on the user's emotions. For example, if the user is relaxed, detailed analysis results are displayed first. If the user is in a hurry, key analysis results are displayed first. If the user is excited, analysis results with visually stimulating effects are displayed first. In this way, by adjusting the order in which the exercise form analysis results are displayed based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0101] The analysis unit can take the user's geographical environment into consideration when analyzing the exercise form. The analysis unit, for example, builds a database for evaluating the user's geographical environment. For example, the analysis unit records and analyzes the user's weather conditions, topography, and the presence or absence of exercise facilities. The analysis unit also analyzes the exercise form based on the user's geographical environment. For example, if the user is exercising outdoors, the analysis can be performed according to the geographical environment. If the user is exercising at a gym, the analysis can be performed according to the gym's equipment. Furthermore, if the user is exercising at home, the analysis can be performed according to the home environment. This makes it possible to provide a more appropriate analysis of the exercise form by taking the user's geographical environment into consideration.

[0102] The analysis unit can improve the accuracy of the analysis by referring to related exercise science literature when analyzing exercise form. The analysis unit, for example, builds a database for referring to related exercise science literature. For example, the analysis unit records and analyzes the latest exercise science literature and past research reports. The analysis unit also analyzes exercise form by referring to related exercise science literature. For example, the analysis unit improves the accuracy of the analysis by referring to the latest exercise science literature. Analysis standards can also be set based on past exercise science literature. Furthermore, the accuracy of the analysis can be improved by utilizing related exercise science literature. In this way, by referring to related exercise science literature, the accuracy of the analysis can be improved and a more accurate analysis of exercise form can be provided.

[0103] When analyzing an exercise form, the analysis unit can perform an analysis based on the user's level of goal achievement. The analysis unit, for example, builds a database for evaluating the user's level of goal achievement. For example, the analysis unit records and analyzes the number of goals achieved by the user and the level of achievement. The analysis unit also analyzes the exercise form based on the user's level of goal achievement. For example, if the user's level of goal achievement is high, a detailed analysis can be performed. Also, if the user's level of goal achievement is low, a simplified analysis can be performed. Furthermore, the accuracy of the analysis can be adjusted depending on the user's level of goal achievement. This makes it possible to provide a more appropriate analysis of the exercise form by taking the user's level of goal achievement into consideration.

[0104] The suggestion unit can estimate the user's emotion and adjust the way in which the revision suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the suggestion unit analyzes the user's facial expression captured by a camera to estimate the emotion. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the suggestion unit adjusts the way in which the revision suggestion is expressed based on the user's emotion. For example, if the user is relaxed, the suggestion unit provides a detailed revision suggestion. If the user is in a hurry, the suggestion unit provides a concise revision suggestion. If the user is excited, the suggestion unit provides a revision suggestion with a visually stimulating effect. In this way, by adjusting the way in which the revision suggestion is expressed based on the user's emotion, it is possible to provide revision suggestions that are easy for the user to understand.

[0105] When proposing a correction, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the user's exercise form. The suggestion unit, for example, builds a database for evaluating the importance of the user's exercise form. For example, the suggestion unit records and analyzes the injury risk and exercise effect of the user's exercise form. The suggestion unit also adjusts the level of detail of the suggestion based on the importance of the user's exercise form. For example, for an important exercise form, a detailed correction suggestion is made. Also, for an exercise form that is not so important, a simplified correction suggestion can be made. Furthermore, the level of detail of the suggestion can be adjusted according to the importance of the user's exercise form. In this way, by adjusting the level of detail of the suggestion based on the importance of the user's exercise form, it is possible to provide a correction suggestion appropriate for the user.

[0106] When proposing modifications, the suggestion unit can apply different suggestion algorithms depending on the user's exercise category. The suggestion unit, for example, builds a database for evaluating the user's exercise category. For example, the suggestion unit records and analyzes the type and purpose of the user's exercise. The suggestion unit also applies different suggestion algorithms depending on the user's exercise category. For example, in the case of strength training, the suggestion unit makes modification suggestions specialized for improving muscle strength. In addition, in the case of aerobic exercise, the suggestion unit can make modification suggestions specialized for improving endurance. Furthermore, in the case of flexibility training, the suggestion unit can make modification suggestions specialized for improving flexibility. In this way, by applying different suggestion algorithms depending on the user's exercise category, it is possible to provide modification suggestions appropriate for the user.

[0107] When proposing a correction, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past correction results. The suggestion unit, for example, builds a database for recording the user's past correction results. For example, the suggestion unit periodically updates and records the user's past correction results. The suggestion unit also analyzes the user's past correction results to improve the accuracy of the suggestion. For example, the suggestion unit makes optimal correction suggestions based on the user's past correction results. The accuracy of the suggestion can also be improved based on the user's past correction history. Furthermore, the accuracy of the suggestion can also be improved by utilizing the user's past correction data. In this way, by referring to the user's past correction results, the accuracy of the suggestion can be improved and more effective correction suggestions can be provided.

[0108] The suggestion unit can estimate the user's emotion and adjust the length of the suggested revision based on the estimated user's emotion. The suggestion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the suggestion unit analyzes the user's facial expression captured by a camera to estimate the emotion. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the suggestion unit adjusts the length of the suggested revision based on the user's emotion. For example, if the user is relaxed, the suggestion unit provides a detailed suggested revision. If the user is in a hurry, the suggestion unit provides a concise suggested revision. If the user is excited, the suggestion unit provides a suggested revision with a visually stimulating effect. In this way, by adjusting the length of the suggested revision according to the user's emotion, it is possible to provide a suggested revision that is appropriate for the user.

[0109] When proposing modifications, the suggestion unit can determine the priority of the suggestions based on the timing of the exercise. The suggestion unit, for example, builds a database for recording the timing of the user's exercise. For example, the suggestion unit records and analyzes the date and season of the user's exercise. The suggestion unit also determines the priority of the suggestions based on the timing of the exercise. For example, the suggestion unit may give priority to suggesting modifications to an exercise that will be performed soon. The suggestion unit may also postpone suggesting modifications to an exercise that is further away. Furthermore, the priority of the suggestions can be adjusted depending on the timing of the exercise. In this way, by determining the priority of the suggestions based on the timing of the exercise, modification suggestions can be provided at an appropriate time for the user.

[0110] The suggestion unit can adjust the order of suggestions based on the relevance of the exercises when suggesting modifications. The suggestion unit, for example, builds a database for evaluating the relevance of the exercises. For example, the suggestion unit records and analyzes the type, purpose, and effect of the exercises. The suggestion unit also adjusts the order of suggestions based on the relevance of the exercises. For example, the suggestion unit prioritizes modification suggestions for highly relevant exercises. Also, the suggestion unit can postpone modification suggestions for less relevant exercises. Furthermore, the order of suggestions can be adjusted based on the relevance of the exercises. In this way, by adjusting the order of suggestions based on the relevance of the exercises, it is possible to provide modification suggestions that are effective for the user.

[0111] When proposing a correction, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, builds a database for evaluating the user's level of expertise. For example, the suggestion unit records and analyzes the user's knowledge and years of experience regarding exercise. The suggestion unit also adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, for beginners, the suggestion unit can make correction suggestions using easy-to-understand language while avoiding technical terminology. For intermediate users, the suggestion unit can also make correction suggestions using a moderate amount of technical terminology. Furthermore, for advanced users, the suggestion unit can also make correction suggestions using a lot of technical terminology. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide correction suggestions that are easy for the user to understand.

[0112] The management unit can estimate the user's emotions and adjust the progress management method based on the estimated user's emotions. The management unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the management unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The management unit can also estimate the user's emotions using voice analysis technology. For example, the management unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the management unit adjusts the progress management method based on the user's emotions. For example, if the user is relaxed, detailed progress management is performed. If the user is in a hurry, simple progress management is performed. If the user is excited, progress management is performed with visually stimulating effects. In this way, by adjusting the progress management method according to the user's emotions, it is possible to provide appropriate progress management for the user.

[0113] The management unit can analyze the user's past training history and select the optimal management method when managing progress. The management unit, for example, builds a database for recording the user's past training history. For example, the management unit records and analyzes the user's past training content, frequency, and achievement level. The management unit also selects the optimal management method based on the user's past training history. For example, the management unit suggests the optimal progress management method based on the user's past training history. The management unit can also analyze the user's past training data to improve the accuracy of progress management. Furthermore, the user's past training history can be used to select the progress management method. In this way, the optimal progress management method can be selected by analyzing the user's past training history, and the user can be supported in achieving their goals.

[0114] The management unit can customize the management means based on the user's current living situation when managing progress. The management unit, for example, builds a database for evaluating the user's current living situation. For example, the management unit records and analyzes the user's work schedule, home environment, and lifestyle habits. The management unit also customizes the management means based on the user's current living situation. For example, the management unit proposes an optimal progress management method taking into consideration the user's current living situation (work, home, etc.). The progress management method can also be customized based on the user's lifestyle habits (sleep time, meal contents, etc.). Furthermore, the progress management means can be adjusted according to the user's living situation. In this way, by customizing the management means based on the user's current living situation, it is possible to provide progress management that is appropriate for the user.

[0115] The management unit can improve the management method by reflecting user feedback during progress management. For example, the management unit builds a database for collecting user feedback. For example, the management unit records and analyzes user feedback. The management unit also improves the management method based on user feedback. For example, the management unit improves the progress management method based on user feedback. The management unit can also analyze past user feedback and propose an optimal progress management method. Furthermore, the user feedback can be utilized to improve the accuracy of progress management. In this way, the management method can be improved by reflecting user feedback, and more effective progress management can be provided.

[0116] The management unit can estimate the user's emotions and determine the priority of progress management based on the estimated user's emotions. The management unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the management unit analyzes the user's facial expressions captured by a camera to estimate the emotions. The management unit can also estimate the user's emotions using voice analysis technology. For example, the management unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the management unit determines the priority of progress management based on the user's emotions. For example, if the user is relaxed, detailed progress management is prioritized. If the user is in a hurry, simple progress management is prioritized. If the user is excited, progress management with visually stimulating effects is prioritized. In this way, by determining the priority of progress management based on the user's emotions, it is possible to provide appropriate progress management for the user.

[0117] The management unit can select the optimal management method by taking into account the user's geographical location information when managing progress. The management unit, for example, has a GPS function for acquiring the user's geographical location information. For example, the management unit acquires the user's current location and takes it into account when managing progress. The management unit also selects the optimal management method based on the user's geographical location information. For example, if the user is near a gym, priority can be given to progress management at the gym. Also, if the user is near a park, priority can be given to outdoor progress management. Furthermore, if the user is at home, priority can be given to progress management at home. In this way, by taking into account the user's geographical location information, the optimal progress management method can be selected and the user can be supported in achieving their goals.

[0118] The management unit can analyze the user's social media activity and suggest management methods when managing progress. The management unit, for example, builds a database for analyzing the user's social media activity. For example, the management unit records and analyzes the content of the user's social media posts and the frequency of activity. The management unit also suggests optimal progress management methods based on the user's social media activity. For example, the management unit can suggest optimal progress management methods based on the user's social media activity. The management unit can also analyze the content of the user's social media posts and customize the progress management method. Furthermore, the management unit can also suggest progress management methods based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest optimal progress management methods and support the user in achieving their goals.

[0119] The management unit can customize the management method by reflecting the user's past feedback when managing progress. For example, the management unit builds a database for collecting the user's past feedback. For example, the management unit records and analyzes the user's past feedback. The management unit also customizes the management method based on the user's past feedback. For example, the management unit proposes an optimal progress management method based on the user's past feedback. The management unit can also analyze the user's past feedback and improve the progress management method. Furthermore, the user's past feedback can be utilized to improve the accuracy of progress management. In this way, the management method can be customized by reflecting the user's past feedback, and more effective progress management can be provided. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, analysis unit, suggestion unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting the user's training objectives. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a personalized exercise menu based on the user's physical strength and exercise experience. The analysis unit monitors the user's exercise form in real time using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing device 12. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and provides suggested corrections and meal plans based on the analysis results. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and manages the user's training progress and provides advice for achieving goals. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, analysis unit, suggestion unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's training objectives. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a personalized exercise menu based on the user's physical strength and exercise experience. The analysis unit monitors the user's exercise form in real time using the camera 42 and sensors of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides correction suggestions and meal plans based on the analysis results. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's training progress and provides advice for achieving goals. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, analysis unit, suggestion unit, and management unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for inputting the user's training objectives. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a personalized exercise menu based on the user's physical strength and exercise experience. The analysis unit monitors the user's exercise form in real time using the camera 42 and sensors of the headset-type terminal 314 and analyzes it using the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides correction suggestions and meal plans based on the analysis results. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's training progress and provides advice for achieving goals. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, generation unit, analysis unit, suggestion unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting the user's training objectives. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a personalized exercise menu based on the user's physical strength and exercise experience. The analysis unit monitors the user's exercise form in real time using the camera 42 and sensors of the robot 414 and analyzes it using the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides correction suggestions and meal plans based on the analysis results. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's training progress and provides advice for achieving goals.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] When the user inputs a training goal, the reception unit can suggest the most suitable training goal based on the user's past training data. For example, it can prioritize and display training goals that the user has frequently selected in the past. It can also analyze the user's past training data and suggest training goals according to the season or time of day. It can also suggest achievable goals by taking into account the user's past training results. This makes it possible to support the input of more personalized training goals by utilizing the user's past training data.

[0122] The generation unit can dynamically adjust the difficulty level of the exercise menu according to the user's training goal. For example, if the user's goal is to increase muscle strength, the generation unit can suggest an exercise menu that gradually increases the load. Also, if the user's goal is to lose weight, the generation unit can suggest an exercise menu that emphasizes calorie consumption. Furthermore, the generation unit can adjust the difficulty level of the exercise menu in real time based on user feedback. This makes it possible to provide the optimal exercise menu according to the user's training goal.

[0123] When analyzing a user's exercise form, the analysis unit can improve the accuracy of the analysis based on the user's body type and muscle strength. For example, it can propose an optimal exercise form taking into account the user's height, weight, and muscle mass. It can also suggest exercise form modifications based on the user's muscle strength data. Furthermore, it can adjust the analysis criteria for the exercise form according to the user's body type and muscle strength. This makes it possible to provide a highly accurate analysis of the exercise form based on the user's individual body type and muscle strength.

[0124] When analyzing the user's dietary history and nutritional balance, the suggestion unit can propose an optimal meal plan taking into account the user's dietary preferences and allergy information. For example, if the user is allergic to a specific ingredient, the suggestion unit can propose a meal plan that avoids that ingredient. It can also propose a meal plan that prioritizes the user's favorite ingredients. Furthermore, it can propose a meal plan that takes nutritional balance into consideration based on the user's dietary preferences and allergy information. This makes it possible to provide an optimal meal plan based on the user's individual dietary preferences and allergy information.

[0125] When managing a user's training progress, the management unit can adjust the progress management method taking into account the user's lifestyle and schedule. For example, when the user is busy, it can suggest a short and effective training menu. It can also adjust the training time according to the user's lifestyle (for example, whether they are a night owl or a morning person). It can also adjust the frequency and intensity of training based on the user's schedule. This makes it possible to provide flexible progress management that suits the user's lifestyle and schedule.

[0126] The reception unit can estimate the user's emotions and adjust the training goal input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface is provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options are provided to suggest a customizable input method. Furthermore, if the user is in a hurry, voice input is prioritized to enable the user to quickly input the training goal. In this way, by adjusting the training goal input method according to the user's emotions, it is possible to reduce the user's stress and improve input efficiency.

[0127] The generation unit can estimate the user's emotions and adjust the presentation method of the exercise menu based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates an exercise menu that progresses at a leisurely pace. If the user is in a hurry, the generation unit generates an exercise menu that is effective in a short amount of time. Furthermore, if the user is excited, the generation unit generates an exercise menu that adds visually stimulating effects. In this way, the user's motivation can be increased by adjusting the presentation method of the exercise menu according to the user's emotions.

[0128] The analysis unit can estimate the user's emotions and adjust the analysis criteria for the exercise form based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis criteria can be applied. If the user is in a hurry, simplified analysis criteria can be applied. If the user is excited, analysis criteria with visually stimulating effects can be applied. In this way, by adjusting the analysis criteria for the exercise form according to the user's emotions, it is possible to provide an analysis that is appropriate for the user.

[0129] The suggestion unit can estimate the user's emotions and adjust the way in which revision suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, detailed revision suggestions are provided. If the user is in a hurry, concise revision suggestions are provided. Furthermore, if the user is excited, revision suggestions are provided that include visually stimulating effects. In this way, by adjusting the way in which revision suggestions are expressed according to the user's emotions, revision suggestions that are easy for the user to understand can be provided.

[0130] The management unit can estimate the user's emotions and adjust the progress management method based on the estimated user emotions. For example, if the user is relaxed, detailed progress management is performed. If the user is in a hurry, simple progress management is performed. Furthermore, if the user is excited, progress management is performed with visually stimulating effects. In this way, by adjusting the progress management method according to the user's emotions, it is possible to provide progress management that is appropriate for the user.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit inputs the user's training goal. Examples of the user's training goal include, but are not limited to, health, weight loss, and muscle building. The reception unit provides, for example, an interface for the user to select the training goal. The reception unit can also support voice input or text input for inputting the user's training goal. Step 2: The generation unit generates a personalized exercise menu based on the purpose input by the reception unit. The generation unit suggests an optimal exercise menu based on, for example, the user's physical strength and exercise experience. For example, the generation unit suggests a menu starting with light exercise for beginners and more advanced exercise for experienced users. The generation unit can also simulate the body shape after achieving the goal based on the user's current body shape and present it as an image. For example, the generation unit uses an algorithm that takes the user's current body shape data as input and simulates the body shape after achieving the goal to generate an image. Step 3: The analysis unit analyzes the user's exercise form based on the exercise menu generated by the generation unit. The analysis unit monitors the user's exercise form in real time using, for example, a camera or sensor, and provides advice on maintaining correct form. For example, the analysis unit suggests corrections if the knee position is incorrect during a squat. The analysis unit can also analyze the user's exercise form in detail and suggest appropriate corrections. Step 4: The suggestion unit suggests corrections based on the exercise form analyzed by the analysis unit. For example, if the knee position is incorrect during squats, the suggestion unit suggests corrections. The suggestion unit can also analyze the user's diet history and nutritional balance to suggest an optimal meal plan. For example, the suggestion unit inputs the user's diet history data and uses an algorithm that analyzes nutritional balance to suggest an optimal meal plan. Step 5: The management unit manages progress based on the meal plan proposed by the proposal unit. The management unit, for example, manages the user's training progress and provides advice for achieving goals. For example, the management unit inputs the user's training data and uses an algorithm for managing progress to provide advice for achieving goals.

[0133] 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.

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

[0135] 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.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0140] The 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.

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

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

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

[0144] Fig. 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.

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

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

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0151] 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0156] The 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.

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

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

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

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the user's training objectives; a generation unit that generates a personalized exercise menu based on the purpose input by the reception unit; an analysis unit that analyzes the user's exercise form based on the exercise menu generated by the generation unit; a suggestion unit that suggests corrections based on the exercise form analyzed by the analysis unit; a suggestion unit that proposes a meal plan for achieving the goal based on the modification proposed by the suggestion unit; a management unit based on the meal plan proposed by the proposal unit; A system characterized by:

2. The management unit Track your training progress and provide advice to help you reach your goals 2. The system of claim 1.

3. The generation unit Based on the user's current body shape, a simulation of what the body shape will be like after achieving the goal is displayed as an image.

2. The system of claim 1.

4. The analysis unit Monitor the user's exercise form in real time using a camera or sensor 2. The system of claim 1.

5. The proposal unit Analyzes the user's dietary history and nutritional balance to suggest optimal meal plans 2. The system of claim 1.

6. The proposal unit Suggests corrections for improper knee position during squats 2. The system of claim 1.

7. The reception unit Estimating a user's emotion and adjusting an input method for training purposes based on the estimated user's emotion 2. The system of claim 1.

8. The reception unit Analyzes the user's past training history and suggests the best way to input goals 2. The system of claim 1.

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