System

Abstract

An object of the system according to the embodiment is to create a training plan according to an individual health condition and a goal, manage progress, and provide feedback and advice.SOLUTION: A system includes a reception unit, a generation unit, a management unit, and a provision unit. The reception unit inputs a health condition or a goal of a user. The generation unit generates a training plan based on the information input by the reception unit. The management unit manages the progress of the training based on the plan created by the generation unit. The providing unit provides feedback or advice based on the progress managed by the management 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 technology does not adequately create training plans or manage progress according to individual health conditions and goals, and there is room for improvement.

[0005] The system according to the embodiment aims to create a training plan according to an individual's health condition and goals, manage progress, and provide feedback and advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a management unit, and a provision unit. The reception unit inputs the user's health status or goals. The generation unit creates a training plan based on the information input by the reception unit. The management unit manages the training progress based on the plan created by the generation unit. The provision unit provides feedback or advice based on the progress managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can create a training plan based on an individual's health condition and goals, manage progress, and provide feedback and advice. [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 training support system according to an embodiment of the present invention provides an optimal training plan based on a user's health condition and goals, and provides progress management and feedback. The training support system inputs the user's health condition and goals, and AI analyzes the information to create a training plan, manages progress, and provides feedback and advice. For example, the user inputs information such as age, gender, weight, height, exercise experience, health condition, and fitness goals. The training support system then analyzes the input information using AI to create a training plan tailored to the user's needs. For example, a user who wants to lose weight may be suggested a plan that combines aerobic exercise and strength training. Furthermore, the training support system records the user's training progress and results, and the AI ​​analyzes the data. For example, data such as training frequency and intensity, weight change, and muscle mass increase may be recorded. Finally, the training support system provides feedback on the effectiveness of the training and areas for improvement based on the user's progress, and advice for achieving goals. This allows the user to train at their own pace and effectively achieve their goals. This allows the training support system to provide an optimal training plan based on the user's health condition and goals, and provide progress management and feedback. For example, the information written by the user can be analyzed quickly and accurately to provide an optimal training plan. In addition, the user can learn specific areas for improvement in their training, improving the effectiveness of their learning.

[0029] A training support system according to an embodiment includes a reception unit, a generation unit, a management unit, and a provision unit. The reception unit inputs a user's health condition or goals. Examples of the user's health condition include, but are not limited to, weight, blood pressure, and heart rate. The reception unit receives input information from the user, such as age, gender, weight, height, exercise experience, health condition, and fitness goals. The generation unit uses AI to create a training plan based on the information input by the reception unit. The generation unit suggests optimal training content, frequency, and intensity based on the user's health condition and goals. For example, the generation unit may suggest a plan combining aerobic exercise and strength training to a user who wants to lose weight. The generation unit may also suggest a plan focusing on strength training to a user who wants to build muscle. The generation unit may also suggest a plan focusing on endurance training to a user who wants to improve endurance. The management unit manages the progress of the training based on the plan created by the generation unit. For example, the management unit records the training status and results, and the AI ​​analyzes the data. For example, the management unit records data such as the frequency and intensity of training, changes in weight, and increases in muscle mass. The management unit can also grasp the progress of training and adjust the training plan as necessary. Furthermore, the management unit can monitor the implementation status and results of training in real time and immediately grasp the progress. The provision unit provides feedback and advice based on the progress managed by the management unit. For example, the provision unit provides feedback on the effectiveness of training and areas for improvement based on the user's progress. The provision unit can also provide advice toward achieving goals. For example, the provision unit provides specific advice such as increasing the frequency of training, reviewing dietary habits, and taking rest. As a result, the training support system according to the embodiment can provide an individually optimized training plan based on the user's health condition and goals, and perform progress management and feedback.

[0030] The generation unit can suggest appropriate training content, frequency, and intensity based on the user's health condition or goals. The generation unit can suggest optimal training content based on, for example, the user's physical fitness level or goals. For example, the generation unit can suggest a plan that combines aerobic exercise and strength training to a user who wants to lose weight. The generation unit can also suggest a plan that focuses on strength training to a user who wants to build muscle. The generation unit can also suggest a plan that focuses on endurance training to a user who wants to improve endurance. This makes it possible to provide an optimal training plan that matches the user's health condition and goals.

[0031] The management unit records the status or results of training, and AI can analyze the data. The management unit, for example, records the status of training. For example, the management unit records the frequency and intensity of training, the number of times it is performed, and the time. The management unit can also record the results of training. For example, the management unit records changes in weight, increases in muscle mass, improvements in endurance, and the like. The management unit can also analyze the recorded data using AI. For example, the management unit can analyze the effects of training using machine learning algorithms. The management unit can also evaluate the progress of training using data analysis methods. This allows for detailed management and analysis of training progress, making it possible to achieve effective training.

[0032] The providing unit can provide feedback on the effects or improvements of training based on the user's progress. The providing unit, for example, provides feedback on the effects of training based on the user's progress. For example, the providing unit provides feedback on effects such as weight loss, muscle strength increase, and improved endurance. The providing unit can also provide feedback on improvements to training based on the user's progress. For example, the providing unit provides feedback on improvements such as correcting form, adding exercises, or adjusting the frequency or intensity of training. This makes it possible to provide appropriate feedback according to the user's progress.

[0033] The providing unit can provide advice for achieving the goal. The providing unit provides, for example, advice for the user to achieve the goal. For example, the providing unit provides specific advice such as increasing the frequency of training, reviewing dietary content, and taking rest. The providing unit can also provide advice for achieving the goal based on the user's progress. For example, the providing unit provides advice for maximizing the effect of training. This makes it possible to provide specific advice for the user to achieve the goal.

[0034] The generation unit can adjust the training plan based on the user's health condition and goals. The generation unit adjusts the training plan based on, for example, the user's health condition and goals. For example, the generation unit adjusts the content, frequency, and intensity of the training plan based on the user's physical fitness level and goals. The generation unit can also adjust the training plan based on the user's progress. For example, the generation unit adjusts the content, frequency, and intensity of the plan to maximize the effectiveness of the training. This allows the training plan to be flexibly adjusted according to the user's health condition and goals.

[0035] The reception unit can analyze the user's past health data and select the optimal input method. For example, the reception unit can analyze the user's past health data and select the optimal input method. For example, the reception unit can automatically display health data that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest input trends during specific time periods based on the user's past health data. This makes it possible to provide the optimal input method based on the user's past data.

[0036] The reception unit can filter the data based on the user's current living situation and areas of interest when inputting the health status and goals. For example, the reception unit can filter the data based on the user's current living situation and areas of interest when inputting the health status and goals. For example, the reception unit can prioritize input of related health data based on the user's current living situation. The reception unit can also prioritize input of related goals based on the user's areas of interest. The reception unit can also customize input items based on the user's living situation and areas of interest. This can prompt the user to make appropriate inputs according to their living situation and areas of interest.

[0037] The reception unit can select the optimum input means depending on the input method of the user when inputting the health condition or goals. For example, when inputting the health condition or goals, the reception unit selects the optimum input means depending on the input method of the user (voice, text, image, etc.). For example, if the user desires voice input, the reception unit can provide voice input preferentially. Also, if the user desires text input, the reception unit can provide text input preferentially. Also, if the user desires image input, the reception unit can provide image input preferentially. In this way, the optimum input means can be provided depending on the input method desired by the user.

[0038] The reception unit can prioritize input of highly relevant information in consideration of the user's geographical location information when inputting a health condition or a goal. For example, the reception unit prioritizes input of highly relevant information in consideration of the user's geographical location information when inputting a health condition or a goal. For example, when the user is in a specific area, the reception unit can prioritize input of health information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize input of health information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize input of health information related to the home. In this way, highly relevant information can be prioritized input based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and input related information when the user inputs their health condition or goals. For example, the reception unit can analyze the user's social media activity and input related information when the user inputs their health condition or goals. For example, the reception unit can automatically input health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related health information. The reception unit can also input related health information by referring to the activities of the user's friends on social media. This makes it possible to input related information based on the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting the health status or goals. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting the health status or goals. For example, the reception unit customizes the input method based on feedback provided by the user in the past. The reception unit can also suggest an optimal input method based on the user's past feedback. The reception unit can also improve the input procedure by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback.

[0041] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the user's health condition and the importance of the goal. For example, when generating a training plan, the generation unit adjusts the level of detail of the plan based on the user's health condition and the importance of the goal. For example, the generation unit provides a detailed training plan when the user's health condition is poor. The generation unit can also provide a detailed training plan when the user's goal is high. The generation unit can also adjust the level of detail of the training plan based on the user's health condition and the importance of the goal. This makes it possible to provide an optimal training plan according to the user's health condition and the importance of the goal.

[0042] The generation unit can apply different plan generation algorithms depending on the user category when generating a training plan. For example, the generation unit applies different plan generation algorithms depending on the user category when generating a training plan. For example, the generation unit applies a training plan generation algorithm for beginners. The generation unit can also apply a training plan generation algorithm for intermediate players. The generation unit can also apply a training plan generation algorithm for advanced players. This makes it possible to provide an optimal training plan depending on the user category.

[0043] When generating a training plan, the generation unit can improve the accuracy of the plan by referring to the user's past plan results. For example, when generating a training plan, the generation unit can improve the accuracy of the plan by referring to the user's past plan results. For example, the generation unit can propose an optimal plan based on the user's past training plan results. The generation unit can also extract effective training content from the user's past plan results. The generation unit can also analyze the user's past plan results and improve the accuracy of the plan. This makes it possible to provide an optimal training plan based on the user's past plan results.

[0044] When generating a training plan, the generation unit can determine the priority of the plan based on the time of submission by the user. For example, when generating a training plan, the generation unit determines the priority of the plan based on the time of submission by the user. For example, if the user submits early, the generation unit creates a training plan with priority. The generation unit can also quickly create a training plan if the user is approaching the submission deadline. The generation unit can also determine the priority of the plan based on the time of submission by the user. This makes it possible to provide an optimal training plan based on the time of submission by the user.

[0045] The generation unit can adjust the order of the plans based on the user's relevance when generating a training plan. For example, the generation unit adjusts the order of the plans based on the user's relevance when generating a training plan. For example, the generation unit prioritizes suggesting training content related to the user's health condition. The generation unit can also prioritize suggesting training content related to the user's goals. The generation unit can also adjust the order of the plans based on the user's past training history. This makes it possible to provide an optimal training plan based on the user's relevance.

[0046] The generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise when generating a training plan. For example, the generation unit adjusts the use of technical terminology in the plan according to the user's level of expertise when generating a training plan. For example, the generation unit can propose a training plan with less technical terminology for beginners. The generation unit can also propose a training plan with an appropriate amount of technical terminology for intermediate users. The generation unit can also propose a training plan with a heavy use of technical terminology for advanced users. This makes it possible to provide an optimal training plan according to the user's level of expertise.

[0047] The management unit can select the optimal management method by referring to the user's past training data when managing training progress. For example, the management unit selects the optimal management method by referring to the user's past training data when managing training progress. For example, the management unit proposes the optimal progress management method based on the user's past training data. The management unit can also extract an effective progress management method from the user's past training data. The management unit can also analyze the user's past training data and improve the progress management method. This makes it possible to provide the optimal progress management method based on the user's past training data.

[0048] The management unit can customize the management means based on the user's current living situation when managing training progress. For example, the management unit customizes the management means based on the user's current living situation when managing training progress. For example, the management unit customizes the progress management means based on the user's current living situation. The management unit can also adjust the frequency of progress management based on the user's living situation. The management unit can also change the method of progress management based on the user's living situation. This makes it possible to provide the optimal progress management means based on the user's living situation.

[0049] The management unit can improve the management method by reflecting user feedback when managing training progress. For example, the management unit improves the management method by reflecting user feedback when managing training progress. For example, the management unit improves the progress management method based on user feedback. The management unit can also extract effective progress management methods from user feedback. The management unit can also analyze user feedback and optimize the progress management method. This makes it possible to provide an optimal progress management method based on user feedback.

[0050] The management unit can select the optimal management method in consideration of the user's geographical location information when managing training progress. For example, the management unit selects the optimal management method in consideration of the user's geographical location information when managing training progress. For example, if the user is in a specific area, the management unit can suggest a progress management method related to that area. Furthermore, if the user is traveling, the management unit can also suggest a progress management method related to the user's travel destination. Furthermore, if the user is at home, the management unit can also suggest a progress management method related to the user's home. This makes it possible to provide the optimal progress management method based on the user's geographical location information.

[0051] The management unit can analyze the user's social media activities and suggest management methods when managing training progress. For example, the management unit can analyze the user's social media activities and suggest management methods when managing training progress. For example, the management unit can suggest management methods based on the training progress shared by the user on social media. The management unit can also analyze the content of the user's social media posts and suggest related progress management methods. The management unit can also suggest related progress management methods by referring to the activities of the user's friends on social media. This makes it possible to provide the optimal progress management methods based on the user's social media activities.

[0052] The management unit can customize the management method by reflecting the user's past feedback when managing training progress. For example, the management unit customizes the management method by reflecting the user's past feedback when managing training progress. For example, the management unit customizes the progress management method based on the user's past feedback. The management unit can also propose an optimal progress management method based on the user's past feedback. The management unit can also improve the progress management means by reflecting the user's past feedback. This makes it possible to provide an optimal progress management method based on the user's past feedback.

[0053] The providing unit can adjust the level of detail of the provided content based on the user's health condition or the importance of the goal when providing feedback or advice. For example, the providing unit adjusts the level of detail of the provided content based on the user's health condition or the importance of the goal when providing feedback or advice. For example, the providing unit provides detailed feedback or advice when the user's health condition is poor. Furthermore, the providing unit can also provide detailed feedback or advice when the user's goal is high. Furthermore, the providing unit can adjust the level of detail of the feedback or advice based on the user's health condition or the importance of the goal. This makes it possible to provide optimal feedback or advice according to the user's health condition or the importance of the goal.

[0054] The providing unit can apply different providing algorithms depending on the user category when providing feedback or advice. For example, the providing unit applies different providing algorithms depending on the user category when providing feedback or advice. For example, the providing unit applies a feedback or advice providing algorithm for beginners. The providing unit can also apply a feedback or advice providing algorithm for intermediate users. The providing unit can also apply a feedback or advice providing algorithm for advanced users. This makes it possible to provide optimal feedback and advice depending on the user category.

[0055] The providing unit can improve the accuracy of the provided content by referring to the user's past feedback results when providing feedback or advice. For example, the providing unit improves the accuracy of the provided content by referring to the user's past feedback results when providing feedback or advice. For example, the providing unit provides optimal feedback or advice based on the user's past feedback results. The providing unit can also extract effective feedback or advice from the user's past feedback results. The providing unit can also analyze the user's past feedback results and improve the accuracy of the provided content. This makes it possible to provide optimal feedback or advice based on the user's past feedback results.

[0056] The providing unit can determine the priority of the content to be provided based on the time of submission by the user when providing feedback or advice. For example, the providing unit determines the priority of the content to be provided based on the time of submission by the user when providing feedback or advice. For example, if the user submits early, the providing unit can provide feedback or advice preferentially. Furthermore, if the user is approaching the submission deadline, the providing unit can also provide feedback or advice quickly. Furthermore, the providing unit can determine the priority of the content to be provided based on the time of submission by the user. This makes it possible to provide optimal feedback or advice based on the time of submission by the user.

[0057] The providing unit can adjust the order of the provided content based on the relevance of the user when providing feedback or advice. For example, the providing unit adjusts the order of the provided content based on the relevance of the user when providing feedback or advice. For example, the providing unit prioritizes providing feedback or advice related to the user's health status. The providing unit can also prioritize providing feedback or advice related to the user's goals. The providing unit can also adjust the order of the provided content based on the user's past feedback history. This makes it possible to provide optimal feedback or advice based on the relevance of the user.

[0058] The providing unit can adjust the use of technical terminology in the provided content according to the user's level of expertise when providing feedback or advice. For example, the providing unit adjusts the use of technical terminology in the provided content according to the user's level of expertise when providing feedback or advice. For example, the providing unit can provide feedback or advice with less technical terminology for beginners. The providing unit can also provide feedback or advice with an appropriate amount of technical terminology for intermediate users. The providing unit can also provide feedback or advice with a heavy use of technical terminology for advanced users. This makes it possible to provide optimal feedback and advice according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The generation unit can also take the user's dietary data into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the dietary details and calorie intake entered by the user and reflect them in the training plan. The generation unit can also check whether the user is consuming specific nutrients and suggest nutritional supplements as needed. Furthermore, the generation unit can adjust the timing and content of training based on the user's eating patterns. This allows the user to optimize the balance between their diet and training and achieve effective training.

[0061] The management unit can also take the user's sleep data into consideration when analyzing the user's training data. For example, the management unit can record the user's sleep duration and sleep quality and analyze the impact on the effectiveness of training. The management unit can also adjust the intensity of training if the user is not getting enough sleep. Furthermore, the management unit can suggest the optimal time for training based on the user's sleep data. This allows the user to optimize the balance between sleep and training and achieve effective training.

[0062] The management unit can also take the user's heart rate data into consideration when analyzing the user's training data. For example, the management unit can record the user's heart rate and analyze its impact on the effectiveness of training. The management unit can also adjust the intensity of training if the user's heart rate exceeds a certain range. Furthermore, the management unit can suggest the optimal time for training based on the user's heart rate data. This allows for an optimized balance between the user's heart rate and training, resulting in effective training.

[0063] The generation unit can also take the user's activity level into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the amount of activity the user engages in daily and reflect this in the training plan. If the user has a sedentary lifestyle, the generation unit can also suggest training to increase the amount of activity. Furthermore, the generation unit can adjust the intensity and frequency of training based on the user's activity level. This allows for an optimized balance between the user's activity level and training, resulting in effective training.

[0064] The management unit can also take the user's water intake into consideration when analyzing the user's training data. For example, the management unit can record the user's water intake and analyze its impact on the effectiveness of training. The management unit can also provide hydration advice if the user is not consuming enough water. Furthermore, the management unit can suggest the optimal time of day for training based on the user's water intake. This allows the user to optimize the balance between water intake and training and achieve effective training.

[0065] The generation unit can also take the user's geographical location information into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the climate and topography of the area where the user lives and reflect this in the training plan. If the user is traveling, the generation unit can also suggest training that is suitable for the environment of the travel destination. Furthermore, the generation unit can also suggest the optimal location and time for training based on the user's geographical location information. This optimizes the balance between the user's geographical location information and training, allowing for effective training.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs the user's health condition or goals. The user's health condition may include, but is not limited to, weight, blood pressure, heart rate, etc. The reception unit receives the user's input information such as age, gender, weight, height, exercise experience, health condition, and fitness goals. Step 2: The generation unit uses AI to create a training plan based on the information entered by the reception unit. The generation unit suggests optimal training content, frequency, and intensity based on the user's health condition and goals. For example, the generation unit suggests a plan that combines aerobic exercise and strength training for a user who wants to lose weight, and a plan that focuses on strength training for a user who wants to build muscle. Furthermore, the generation unit can suggest a plan that focuses on endurance training for a user who wants to improve their endurance. Step 3: The management unit manages the training progress based on the plan created by the generation unit. The management unit records the training implementation status and results, and the AI ​​analyzes the data. For example, it records data such as training frequency and intensity, weight changes, and muscle mass gain. The management unit can also grasp the training progress and adjust the training plan as necessary. Furthermore, the management unit can monitor the training implementation status and results in real time, allowing it to immediately grasp the progress. Step 4: The providing unit provides feedback and advice based on the progress managed by the management unit. The providing unit provides feedback on the effectiveness of training and areas for improvement based on the user's progress. The providing unit also provides advice to help achieve goals. For example, it provides specific advice such as increasing the frequency of training, reviewing dietary habits, and taking rest.

[0068] (Example 2) A training support system according to an embodiment of the present invention provides an optimal training plan based on a user's health condition and goals, and provides progress management and feedback. The training support system inputs the user's health condition and goals, and AI analyzes the information to create a training plan, manages progress, and provides feedback and advice. For example, the user inputs information such as age, gender, weight, height, exercise experience, health condition, and fitness goals. The training support system then analyzes the input information using AI to create a training plan tailored to the user's needs. For example, a user who wants to lose weight may be suggested a plan that combines aerobic exercise and strength training. Furthermore, the training support system records the user's training progress and results, and the AI ​​analyzes the data. For example, data such as training frequency and intensity, weight change, and muscle mass increase may be recorded. Finally, the training support system provides feedback on the effectiveness of the training and areas for improvement based on the user's progress, and advice for achieving goals. This allows the user to train at their own pace and effectively achieve their goals. This allows the training support system to provide an optimal training plan based on the user's health condition and goals, and provide progress management and feedback. For example, the information written by the user can be analyzed quickly and accurately to provide an optimal training plan. In addition, the user can learn specific areas for improvement in their training, improving the effectiveness of their learning.

[0069] A training support system according to an embodiment includes a reception unit, a generation unit, a management unit, and a provision unit. The reception unit inputs a user's health condition or goals. Examples of the user's health condition include, but are not limited to, weight, blood pressure, and heart rate. The reception unit receives input information from the user, such as age, gender, weight, height, exercise experience, health condition, and fitness goals. The generation unit uses AI to create a training plan based on the information input by the reception unit. The generation unit suggests optimal training content, frequency, and intensity based on the user's health condition and goals. For example, the generation unit may suggest a plan combining aerobic exercise and strength training to a user who wants to lose weight. The generation unit may also suggest a plan focusing on strength training to a user who wants to build muscle. The generation unit may also suggest a plan focusing on endurance training to a user who wants to improve endurance. The management unit manages the progress of the training based on the plan created by the generation unit. For example, the management unit records the training status and results, and the AI ​​analyzes the data. For example, the management unit records data such as the frequency and intensity of training, changes in weight, and increases in muscle mass. The management unit can also grasp the progress of training and adjust the training plan as necessary. Furthermore, the management unit can monitor the implementation status and results of training in real time and immediately grasp the progress. The provision unit provides feedback and advice based on the progress managed by the management unit. For example, the provision unit provides feedback on the effectiveness of training and areas for improvement based on the user's progress. The provision unit can also provide advice toward achieving goals. For example, the provision unit provides specific advice such as increasing the frequency of training, reviewing dietary habits, and taking rest. As a result, the training support system according to the embodiment can provide an individually optimized training plan based on the user's health condition and goals, and perform progress management and feedback.

[0070] The generation unit can suggest appropriate training content, frequency, and intensity based on the user's health condition or goals. The generation unit can suggest optimal training content based on, for example, the user's physical fitness level or goals. For example, the generation unit can suggest a plan that combines aerobic exercise and strength training to a user who wants to lose weight. The generation unit can also suggest a plan that focuses on strength training to a user who wants to build muscle. The generation unit can also suggest a plan that focuses on endurance training to a user who wants to improve endurance. This makes it possible to provide an optimal training plan that matches the user's health condition and goals.

[0071] The management unit records the status or results of training, and AI can analyze the data. The management unit, for example, records the status of training. For example, the management unit records the frequency and intensity of training, the number of times it is performed, and the time. The management unit can also record the results of training. For example, the management unit records changes in weight, increases in muscle mass, improvements in endurance, and the like. The management unit can also analyze the recorded data using AI. For example, the management unit can analyze the effects of training using machine learning algorithms. The management unit can also evaluate the progress of training using data analysis methods. This allows for detailed management and analysis of training progress, making it possible to achieve effective training.

[0072] The providing unit can provide feedback on the effects or improvements of training based on the user's progress. The providing unit, for example, provides feedback on the effects of training based on the user's progress. For example, the providing unit provides feedback on effects such as weight loss, muscle strength increase, and improved endurance. The providing unit can also provide feedback on improvements to training based on the user's progress. For example, the providing unit provides feedback on improvements such as correcting form, adding exercises, or adjusting the frequency or intensity of training. This makes it possible to provide appropriate feedback according to the user's progress.

[0073] The providing unit can provide advice for achieving the goal. The providing unit provides, for example, advice for the user to achieve the goal. For example, the providing unit provides specific advice such as increasing the frequency of training, reviewing dietary content, and taking rest. The providing unit can also provide advice for achieving the goal based on the user's progress. For example, the providing unit provides advice for maximizing the effect of training. This makes it possible to provide specific advice for the user to achieve the goal.

[0074] The generation unit can adjust the training plan based on the user's health condition and goals. The generation unit adjusts the training plan based on, for example, the user's health condition and goals. For example, the generation unit adjusts the content, frequency, and intensity of the training plan based on the user's physical fitness level and goals. The generation unit can also adjust the training plan based on the user's progress. For example, the generation unit adjusts the content, frequency, and intensity of the plan to maximize the effectiveness of the training. This allows the training plan to be flexibly adjusted according to the user's health condition and goals.

[0075] The reception unit can estimate the user's emotions and adjust the timing of inputting the health status and goals based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of inputting the health status and goals based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit may prompt the user to input the health status and goals during a time when the user can relax. Furthermore, if the user is relaxed, the reception unit may prompt the user to input detailed health status and goals. Furthermore, if the user is in a hurry, the reception unit may provide a simplified input form to allow the user to complete the input quickly. This allows the user to be prompted to input the health status and goals at the optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past health data and select the optimal input method. For example, the reception unit can analyze the user's past health data and select the optimal input method. For example, the reception unit can automatically display health data that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest input trends during specific time periods based on the user's past health data. This makes it possible to provide the optimal input method based on the user's past data.

[0077] The reception unit can filter the data based on the user's current living situation and areas of interest when inputting the health status and goals. For example, the reception unit can filter the data based on the user's current living situation and areas of interest when inputting the health status and goals. For example, the reception unit can prioritize input of related health data based on the user's current living situation. The reception unit can also prioritize input of related goals based on the user's areas of interest. The reception unit can also customize input items based on the user's living situation and areas of interest. This can prompt the user to make appropriate inputs according to their living situation and areas of interest.

[0078] The reception unit can select the optimum input means depending on the input method of the user when inputting the health condition or goals. For example, when inputting the health condition or goals, the reception unit selects the optimum input means depending on the input method of the user (voice, text, image, etc.). For example, if the user desires voice input, the reception unit can provide voice input preferentially. Also, if the user desires text input, the reception unit can provide text input preferentially. Also, if the user desires image input, the reception unit can provide image input preferentially. In this way, the optimum input means can be provided depending on the input method desired by the user.

[0079] The reception unit can estimate the user's emotions and determine the priority of the health information to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the health information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize input of stress-related health information. Also, if the user is relaxed, the reception unit can prioritize input of detailed health information. Also, if the user is in a hurry, the reception unit can prioritize input of important health information. In this way, the health information to be input with priority can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The reception unit can prioritize input of highly relevant information in consideration of the user's geographical location information when inputting a health condition or a goal. For example, the reception unit prioritizes input of highly relevant information in consideration of the user's geographical location information when inputting a health condition or a goal. For example, when the user is in a specific area, the reception unit can prioritize input of health information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize input of health information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize input of health information related to the home. In this way, highly relevant information can be prioritized input based on the user's geographical location information.

[0081] The reception unit can analyze the user's social media activity and input related information when the user inputs their health condition or goals. For example, the reception unit can analyze the user's social media activity and input related information when the user inputs their health condition or goals. For example, the reception unit can automatically input health information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related health information. The reception unit can also input related health information by referring to the activities of the user's friends on social media. This makes it possible to input related information based on the user's social media activity.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting the health status or goals. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting the health status or goals. For example, the reception unit customizes the input method based on feedback provided by the user in the past. The reception unit can also suggest an optimal input method based on the user's past feedback. The reception unit can also improve the input procedure by reflecting the user's past feedback. This makes it possible to provide an optimal input method based on the user's past feedback.

[0083] The generation unit can estimate the user's emotions and adjust the presentation method of the training plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the presentation method of the training plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can suggest a training plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also suggest a short and effective training plan. If the user is excited, the generation unit can also suggest a visually stimulating training plan. This makes it possible to provide an optimal presentation method of the training plan according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the user's health condition and the importance of the goal. For example, when generating a training plan, the generation unit adjusts the level of detail of the plan based on the user's health condition and the importance of the goal. For example, the generation unit provides a detailed training plan when the user's health condition is poor. The generation unit can also provide a detailed training plan when the user's goal is high. The generation unit can also adjust the level of detail of the training plan based on the user's health condition and the importance of the goal. This makes it possible to provide an optimal training plan according to the user's health condition and the importance of the goal.

[0085] The generation unit can apply different plan generation algorithms depending on the user category when generating a training plan. For example, the generation unit applies different plan generation algorithms depending on the user category when generating a training plan. For example, the generation unit applies a training plan generation algorithm for beginners. The generation unit can also apply a training plan generation algorithm for intermediate players. The generation unit can also apply a training plan generation algorithm for advanced players. This makes it possible to provide an optimal training plan depending on the user category.

[0086] When generating a training plan, the generation unit can improve the accuracy of the plan by referring to the user's past plan results. For example, when generating a training plan, the generation unit can improve the accuracy of the plan by referring to the user's past plan results. For example, the generation unit can propose an optimal plan based on the user's past training plan results. The generation unit can also extract effective training content from the user's past plan results. The generation unit can also analyze the user's past plan results and improve the accuracy of the plan. This makes it possible to provide an optimal training plan based on the user's past plan results.

[0087] The generation unit can estimate the user's emotions and adjust the length of the training plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the training plan based on the estimated user emotions. For example, the generation unit can suggest a longer training plan when the user is relaxed. The generation unit can also suggest a short and effective training plan when the user is in a hurry. The generation unit can also suggest a visually stimulating training plan when the user is excited. This makes it possible to provide an optimal training plan length depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] When generating a training plan, the generation unit can determine the priority of the plan based on the time of submission by the user. For example, when generating a training plan, the generation unit determines the priority of the plan based on the time of submission by the user. For example, if the user submits early, the generation unit creates a training plan with priority. The generation unit can also quickly create a training plan if the user is approaching the submission deadline. The generation unit can also determine the priority of the plan based on the time of submission by the user. This makes it possible to provide an optimal training plan based on the time of submission by the user.

[0089] The generation unit can adjust the order of the plans based on the user's relevance when generating a training plan. For example, the generation unit adjusts the order of the plans based on the user's relevance when generating a training plan. For example, the generation unit prioritizes suggesting training content related to the user's health condition. The generation unit can also prioritize suggesting training content related to the user's goals. The generation unit can also adjust the order of the plans based on the user's past training history. This makes it possible to provide an optimal training plan based on the user's relevance.

[0090] The generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise when generating a training plan. For example, the generation unit adjusts the use of technical terminology in the plan according to the user's level of expertise when generating a training plan. For example, the generation unit can propose a training plan with less technical terminology for beginners. The generation unit can also propose a training plan with an appropriate amount of technical terminology for intermediate users. The generation unit can also propose a training plan with a heavy use of technical terminology for advanced users. This makes it possible to provide an optimal training plan according to the user's level of expertise.

[0091] The management unit can estimate the user's emotions and adjust the training progress management method based on the estimated user emotions. The management unit, for example, estimates the user's emotions and adjusts the training progress management method based on the estimated user emotions. For example, the management unit simplifies progress management when the user is feeling stressed. The management unit can also perform detailed progress management when the user is relaxed. The management unit can also perform quick progress management when the user is in a hurry. This makes it possible to provide an optimal training progress management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The management unit can select the optimal management method by referring to the user's past training data when managing training progress. For example, the management unit selects the optimal management method by referring to the user's past training data when managing training progress. For example, the management unit proposes the optimal progress management method based on the user's past training data. The management unit can also extract an effective progress management method from the user's past training data. The management unit can also analyze the user's past training data and improve the progress management method. This makes it possible to provide the optimal progress management method based on the user's past training data.

[0093] The management unit can customize the management means based on the user's current living situation when managing training progress. For example, the management unit customizes the management means based on the user's current living situation when managing training progress. For example, the management unit customizes the progress management means based on the user's current living situation. The management unit can also adjust the frequency of progress management based on the user's living situation. The management unit can also change the method of progress management based on the user's living situation. This makes it possible to provide the optimal progress management means based on the user's living situation.

[0094] The management unit can improve the management method by reflecting user feedback when managing training progress. For example, the management unit improves the management method by reflecting user feedback when managing training progress. For example, the management unit improves the progress management method based on user feedback. The management unit can also extract effective progress management methods from user feedback. The management unit can also analyze user feedback and optimize the progress management method. This makes it possible to provide an optimal progress management method based on user feedback.

[0095] The management unit can estimate the user's emotions and prioritize training progress based on the estimated user emotions. The management unit, for example, estimates the user's emotions and prioritizes training progress based on the estimated user emotions. For example, if the user is feeling stressed, the management unit prioritizes progress related to stress reduction. Also, if the user is relaxed, the management unit can prioritize detailed progress. Also, if the user is in a hurry, the management unit can prioritize important progress. This makes it possible to provide optimal training progress priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The management unit can select the optimal management method in consideration of the user's geographical location information when managing training progress. For example, the management unit selects the optimal management method in consideration of the user's geographical location information when managing training progress. For example, if the user is in a specific area, the management unit can suggest a progress management method related to that area. Furthermore, if the user is traveling, the management unit can also suggest a progress management method related to the user's travel destination. Furthermore, if the user is at home, the management unit can also suggest a progress management method related to the user's home. This makes it possible to provide the optimal progress management method based on the user's geographical location information.

[0097] The management unit can analyze the user's social media activities and suggest management methods when managing training progress. For example, the management unit can analyze the user's social media activities and suggest management methods when managing training progress. For example, the management unit can suggest management methods based on the training progress shared by the user on social media. The management unit can also analyze the content of the user's social media posts and suggest related progress management methods. The management unit can also suggest related progress management methods by referring to the activities of the user's friends on social media. This makes it possible to provide the optimal progress management methods based on the user's social media activities.

[0098] The management unit can customize the management method by reflecting the user's past feedback when managing training progress. For example, the management unit customizes the management method by reflecting the user's past feedback when managing training progress. For example, the management unit customizes the progress management method based on the user's past feedback. The management unit can also propose an optimal progress management method based on the user's past feedback. The management unit can also improve the progress management means by reflecting the user's past feedback. This makes it possible to provide an optimal progress management method based on the user's past feedback.

[0099] The providing unit can estimate the user's emotions and adjust the manner in which feedback and advice are expressed based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the manner in which feedback and advice are expressed based on the estimated user's emotions. For example, the providing unit can provide detailed feedback and advice when the user is relaxed. Furthermore, the providing unit can provide concise feedback and advice when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating feedback and advice when the user is excited. This makes it possible to provide the optimal manner in which feedback and advice are expressed depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The providing unit can adjust the level of detail of the provided content based on the user's health condition or the importance of the goal when providing feedback or advice. For example, the providing unit adjusts the level of detail of the provided content based on the user's health condition or the importance of the goal when providing feedback or advice. For example, the providing unit provides detailed feedback or advice when the user's health condition is poor. Furthermore, the providing unit can also provide detailed feedback or advice when the user's goal is high. Furthermore, the providing unit can adjust the level of detail of the feedback or advice based on the user's health condition or the importance of the goal. This makes it possible to provide optimal feedback or advice according to the user's health condition or the importance of the goal.

[0101] The providing unit can apply different providing algorithms depending on the user category when providing feedback or advice. For example, the providing unit applies different providing algorithms depending on the user category when providing feedback or advice. For example, the providing unit applies a feedback or advice providing algorithm for beginners. The providing unit can also apply a feedback or advice providing algorithm for intermediate users. The providing unit can also apply a feedback or advice providing algorithm for advanced users. This makes it possible to provide optimal feedback and advice depending on the user category.

[0102] The providing unit can improve the accuracy of the provided content by referring to the user's past feedback results when providing feedback or advice. For example, the providing unit improves the accuracy of the provided content by referring to the user's past feedback results when providing feedback or advice. For example, the providing unit provides optimal feedback or advice based on the user's past feedback results. The providing unit can also extract effective feedback or advice from the user's past feedback results. The providing unit can also analyze the user's past feedback results and improve the accuracy of the provided content. This makes it possible to provide optimal feedback or advice based on the user's past feedback results.

[0103] The providing unit can estimate the user's emotion and adjust the length of the feedback or advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the feedback or advice based on the estimated user's emotion. For example, the providing unit can provide longer feedback or advice when the user is relaxed. Furthermore, the providing unit can provide effective feedback or advice in a short time when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating feedback or advice when the user is excited. This makes it possible to provide the optimal length of feedback or advice according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The providing unit can determine the priority of the content to be provided based on the time of submission by the user when providing feedback or advice. For example, the providing unit determines the priority of the content to be provided based on the time of submission by the user when providing feedback or advice. For example, if the user submits early, the providing unit can provide feedback or advice preferentially. Furthermore, if the user is approaching the submission deadline, the providing unit can also provide feedback or advice quickly. Furthermore, the providing unit can determine the priority of the content to be provided based on the time of submission by the user. This makes it possible to provide optimal feedback or advice based on the time of submission by the user.

[0105] The providing unit can adjust the order of the provided content based on the relevance of the user when providing feedback or advice. For example, the providing unit adjusts the order of the provided content based on the relevance of the user when providing feedback or advice. For example, the providing unit prioritizes providing feedback or advice related to the user's health status. The providing unit can also prioritize providing feedback or advice related to the user's goals. The providing unit can also adjust the order of the provided content based on the user's past feedback history. This makes it possible to provide optimal feedback or advice based on the relevance of the user.

[0106] The providing unit can adjust the use of technical terminology in the provided content according to the user's level of expertise when providing feedback or advice. For example, the providing unit adjusts the use of technical terminology in the provided content according to the user's level of expertise when providing feedback or advice. For example, the providing unit can provide feedback or advice with less technical terminology for beginners. The providing unit can also provide feedback or advice with an appropriate amount of technical terminology for intermediate users. The providing unit can also provide feedback or advice with a heavy use of technical terminology for advanced users. This makes it possible to provide optimal feedback and advice according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, management unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's health condition and goals using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training plan based on the input information using AI. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of training. The provision unit can provide feedback and advice using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, management unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's health condition and goals using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training plan based on the input information using AI. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of training. The provision unit can provide feedback and advice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, management unit, and provision 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 can input the user's health condition and goals using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training plan based on the input information using AI. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of training. The provision unit can provide feedback and advice using the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, management unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's health condition and goals using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training plan based on the input information using AI. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the progress of training. The provision unit can provide feedback and advice using the speaker 240 of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The generation unit can also take the user's dietary data into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the dietary details and calorie intake entered by the user and reflect them in the training plan. The generation unit can also check whether the user is consuming specific nutrients and suggest nutritional supplements as needed. Furthermore, the generation unit can adjust the timing and content of training based on the user's eating patterns. This allows the user to optimize the balance between their diet and training and achieve effective training.

[0109] The management unit can also take the user's sleep data into consideration when analyzing the user's training data. For example, the management unit can record the user's sleep duration and sleep quality and analyze the impact on the effectiveness of training. The management unit can also adjust the intensity of training if the user is not getting enough sleep. Furthermore, the management unit can suggest the optimal time for training based on the user's sleep data. This allows the user to optimize the balance between sleep and training and achieve effective training.

[0110] The providing unit may also introduce a reward system to increase the user's motivation when providing feedback on the effectiveness of training and areas for improvement based on the user's progress. For example, the providing unit may award badges or points when the user achieves a goal. The providing unit may also provide benefits or discount coupons when the user earns a certain number of points. Furthermore, the providing unit may also provide messages or words of encouragement to increase the user's motivation according to the user's progress. This allows the user to maintain their motivation and continue effective training.

[0111] The generation unit can also take the user's stress level into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the stress level entered by the user and reflect it in the training plan. Furthermore, if the user is feeling high stress, the generation unit can also suggest training that has a relaxing effect. Furthermore, the generation unit can adjust the intensity and frequency of training based on the user's stress level. This allows the user to optimize the balance between stress and training and achieve effective training.

[0112] The management unit can also take the user's heart rate data into consideration when analyzing the user's training data. For example, the management unit can record the user's heart rate and analyze its impact on the effectiveness of training. The management unit can also adjust the intensity of training if the user's heart rate exceeds a certain range. Furthermore, the management unit can suggest the optimal time for training based on the user's heart rate data. This allows for an optimized balance between the user's heart rate and training, resulting in effective training.

[0113] When providing feedback on the effectiveness of training and areas for improvement based on the user's progress, the providing unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling down, the providing unit can provide encouraging words or positive feedback. Also, if the user is excited, the providing unit can provide challenging feedback aimed at the next goal. Furthermore, if the user is relaxed, the providing unit can provide detailed feedback or advice. This makes it possible to provide appropriate feedback according to the user's emotions and support effective training.

[0114] The generation unit can also take the user's activity level into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the amount of activity the user engages in daily and reflect this in the training plan. If the user has a sedentary lifestyle, the generation unit can also suggest training to increase the amount of activity. Furthermore, the generation unit can adjust the intensity and frequency of training based on the user's activity level. This allows for an optimized balance between the user's activity level and training, resulting in effective training.

[0115] The management unit can also take the user's water intake into consideration when analyzing the user's training data. For example, the management unit can record the user's water intake and analyze its impact on the effectiveness of training. The management unit can also provide hydration advice if the user is not consuming enough water. Furthermore, the management unit can suggest the optimal time of day for training based on the user's water intake. This allows the user to optimize the balance between water intake and training and achieve effective training.

[0116] When providing feedback on the effects of training and areas for improvement based on the user's progress, the providing unit can estimate the user's emotions and adjust the timing of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide feedback at a time when the user is able to relax. Furthermore, if the user is relaxed, the providing unit can provide detailed feedback. Furthermore, if the user is in a hurry, the providing unit can provide brief feedback. This makes it possible to provide feedback at an appropriate time according to the user's emotions and support effective training.

[0117] The generation unit can also take the user's geographical location information into consideration when creating a training plan based on the user's health condition and goals. For example, the generation unit can analyze the climate and topography of the area where the user lives and reflect this in the training plan. If the user is traveling, the generation unit can also suggest training that is suitable for the environment of the travel destination. Furthermore, the generation unit can also suggest the optimal location and time for training based on the user's geographical location information. This optimizes the balance between the user's geographical location information and training, allowing for effective training.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs the user's health condition or goals. The user's health condition may include, but is not limited to, weight, blood pressure, heart rate, etc. The reception unit receives the user's input information such as age, gender, weight, height, exercise experience, health condition, and fitness goals. Step 2: The generation unit uses AI to create a training plan based on the information entered by the reception unit. The generation unit suggests optimal training content, frequency, and intensity based on the user's health condition and goals. For example, the generation unit suggests a plan that combines aerobic exercise and strength training for a user who wants to lose weight, and a plan that focuses on strength training for a user who wants to build muscle. Furthermore, the generation unit can suggest a plan that focuses on endurance training for a user who wants to improve their endurance. Step 3: The management unit manages the training progress based on the plan created by the generation unit. The management unit records the training implementation status and results, and the AI ​​analyzes the data. For example, it records data such as training frequency and intensity, weight changes, and muscle mass gain. The management unit can also grasp the training progress and adjust the training plan as necessary. Furthermore, the management unit can monitor the training implementation status and results in real time, allowing it to immediately grasp the progress. Step 4: The providing unit provides feedback and advice based on the progress managed by the management unit. The providing unit provides feedback on the effectiveness of training and areas for improvement based on the user's progress. The providing unit also provides advice to help achieve goals. For example, it provides specific advice such as increasing the frequency of training, reviewing dietary habits, and taking rest.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 a user's health status or goals; a generation unit that generates a training plan based on the information input by the reception unit; a management unit that manages the progress of training based on the plan created by the generation unit; a providing unit that provides feedback or advice based on the progress managed by the management unit. A system characterized by:

2. The generation unit Suggest appropriate training content, frequency, and intensity based on the user's health condition or goals 2. The system of claim 1.

3. The management unit Training status and results are recorded, and AI analyzes the data.

2. The system of claim 1.

4. The providing unit Provide feedback on training effectiveness or areas for improvement based on user progress 2. The system of claim 1.

5. The providing unit Providing advice to help you achieve your goals 2. The system of claim 1.

6. The generation unit Tailor your training plan based on your fitness and goals 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of inputting health status and goals based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past health data and select the optimal input method 2. The system of claim 1.

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