System

Abstract

An object of a system according to an embodiment is to provide an optimal training plan based on an individual lifestyle and an exercise history and support continuous training.SOLUTION: A system includes an input part, an analysis part, a proposal part, a monitoring part, and an adjustment part. The input unit inputs lifestyle habit data and exercise history data. The analysis unit analyzes the information input by the input unit. The proposal unit proposes a training plan based on the result obtained by the analysis unit. The monitoring unit monitors a progress status of the training based on the plan proposed by the proposal unit. The adjustment unit adjusts the training plan based on the progress status obtained by the monitoring 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 has the problem that it is difficult to provide optimal training plans based on individual lifestyle habits and exercise history, and there is a lack of support for continuous training.

[0005] The system according to the embodiment aims to provide an optimal training plan based on an individual's lifestyle habits and exercise history, and to support continuous training. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The input unit inputs lifestyle habit data and exercise history data. The analysis unit analyzes the information input by the input unit. The proposal unit proposes a training plan based on the results obtained by the analysis unit. The monitoring unit monitors the training progress based on the plan proposed by the proposal unit. The adjustment unit adjusts the training plan based on the progress obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal training plan based on an individual's lifestyle habits and exercise history, and can support continuous training. [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 inputs a user's lifestyle and exercise history, analyzes the history using AI, proposes an optimal training plan, monitors the user's progress, and adjusts the plan as needed. The training support system inputs the user's lifestyle and exercise history, analyzes the history using AI, and proposes an optimal training plan. Furthermore, the system monitors the user's training progress and adjusts the plan as needed. For example, in a training support system, the user inputs their lifestyle and exercise history into an application. For example, they input information such as their diet, exercise frequency, and past training experience. This information is then input into AI. The AI ​​then analyzes the input information and proposes an optimal training plan for the user. The AI ​​determines what type of training is effective based on the user's lifestyle and exercise history. For example, it proposes a training plan tailored to the user's goals, such as strength training or aerobic exercise. Furthermore, the system monitors the user's training progress. When the user trains, the application records the user's progress, which is then analyzed by AI. For example, data such as the number of training sessions, duration, and calories burned is collected and analyzed by AI to evaluate the user's training effectiveness. If necessary, the training plan is adjusted. The AI ​​then reviews the training plan based on the user's progress and re-proposes an optimal plan. For example, the system makes adjustments to suit the user's condition, such as increasing the training load or changing the type of exercise. This allows the training support system to continuously support training that suits the user by proposing an optimal training plan based on the user's lifestyle and exercise history, monitoring progress, and adjusting the plan as needed. This allows the training support system to continuously support training that suits the user by proposing an optimal training plan based on the user's lifestyle and exercise history, monitoring progress, and adjusting the plan as needed. For example, the system can support sustainable training while resolving issues such as cost and location.

[0029] A training support system according to an embodiment includes an input unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The input unit inputs lifestyle habit data and exercise history data of a user. The lifestyle habit data includes, for example, but is not limited to, dietary details, sleep duration, and stress level. The exercise history data includes, for example, but is not limited to, exercise type, exercise duration, and exercise intensity. The analysis unit analyzes the information input by the input unit. The analysis is performed, for example, but is not limited to, using statistical analysis of data or a machine learning algorithm. The proposal unit proposes a training plan based on the results obtained by the analysis unit. The training plan includes, for example, but is not limited to, exercise type, frequency, and intensity. The monitoring unit monitors the training progress based on the plan proposed by the proposal unit. The monitoring is performed, for example, but is not limited to, based on the frequency of data collection and the type of sensor used. The adjustment unit adjusts the training plan based on the progress obtained by the monitoring unit. The adjustment is performed, for example, but is not limited to, based on the criteria for changing the training plan and the frequency of adjustment. As a result, the training support system according to the embodiment can continuously support training suited to the user by proposing an optimal training plan based on the user's lifestyle habits and exercise history, monitoring progress, and adjusting the plan as necessary.

[0030] The training support system includes a recording unit that records the training progress. The recording unit records the training progress. Recording includes, but is not limited to, for example, the data storage format and the recording frequency. For example, the recording unit records data such as the number of training sessions, the time, and calories burned. The recording unit can also visually display the training progress as a graph or chart. For example, the recording unit may display the training progress in chronological order, allowing the user to check their own progress. By recording the training progress, the user's training history can be managed in detail. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may input the training progress into AI, which may analyze and record the data.

[0031] The training support system includes an adjustment unit that makes adjustments according to the user's condition. The adjustment unit makes adjustments according to the user's condition. The user's condition includes, but is not limited to, health condition, fatigue level, motivation, etc. For example, the adjustment unit adjusts the training plan based on the user's health condition. The adjustment unit can also adjust the training load based on the user's fatigue level. Furthermore, the adjustment unit can change the type of training based on the user's motivation. This allows for more effective training by adjusting the training plan according to the user's condition. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's condition data into AI, which can analyze the data and propose an optimal training plan.

[0032] The analysis unit can analyze the user's lifestyle habits and exercise history and propose an optimal training plan. The analysis unit can analyze the user's lifestyle habits and exercise history and propose an optimal training plan. The optimal training plan may include, but is not limited to, individual goals, physical fitness level, and health status. For example, the analysis unit can determine what type of training is effective based on the user's lifestyle habits data. The analysis unit can also evaluate the effectiveness of past training based on the user's exercise history data and propose an optimal training plan. Furthermore, the analysis unit can customize a training plan based on the user's goals. In this way, an individually optimized training plan can be provided by analyzing the user's lifestyle habits and exercise history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's lifestyle habits data and exercise history data into AI, which can analyze the data and propose an optimal training plan.

[0033] The monitoring unit collects data on the number of training sessions, duration, and calories burned, and the AI ​​can analyze the data. The monitoring unit collects data on the number of training sessions, duration, and calories burned, and the AI ​​analyzes the data. The number of training sessions includes, for example, but is not limited to, the number of sessions per day or the number of sessions per week. The training duration includes, for example, but is not limited to, the duration of a single training session or the cumulative duration. The calories burned includes, for example, but is not limited to, the calculation formula and the type of sensor used. For example, the monitoring unit records the number of training sessions, and the AI ​​analyzes the data to evaluate the effectiveness of the training. The monitoring unit can also record the duration of training, and the AI ​​analyzes the data to evaluate the progress of the training. Furthermore, the monitoring unit can record the calories burned, and the AI ​​analyzes the data to evaluate the effectiveness of the training. In this way, detailed training data can be collected and analyzed by the AI, allowing the effectiveness of the training to be accurately evaluated. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input data on the number of training sessions, duration, and calories burned into the AI, which can then analyze the data and evaluate the effectiveness of the training.

[0034] The adjustment unit can increase the training load, change the type of exercise, or make adjustments to suit the user's condition. The adjustment unit can increase the training load, change the type of exercise, or make adjustments to suit the user's condition. Examples of training load include, but are not limited to, increasing weight and increasing the number of repetitions. Examples of types of exercise include, but are not limited to, aerobic exercise and strength training. For example, the adjustment unit can increase the training load based on the user's progress. The adjustment unit can also change the type of exercise based on the user's condition. Furthermore, the adjustment unit can adjust the training plan based on the user's goals. This allows for flexibly adjusting the training plan according to the user's condition, thereby providing effective training. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's condition data into AI, which can analyze the data and propose an optimal training plan.

[0035] The input unit can analyze the user's past input history and select the optimal input method. The input unit analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, input frequency and input content trends, but is not limited to these examples. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also customize an input form based on information previously input by the user. Furthermore, the input unit can analyze the user's past input history to determine input tendencies during specific time periods and suggest the optimal input timing. In this way, by analyzing the past input history, the optimal input method can be provided for the user. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data into AI, which analyzes the data and suggests the optimal input method.

[0036] The input unit can filter the information based on the user's current health condition and goals when inputting lifestyle habits and exercise history. The input unit can filter the information based on the user's current health condition and goals when inputting lifestyle habits and exercise history. Examples of filtering include, but are not limited to, health condition evaluation criteria and goal setting methods. For example, the input unit can filter the information to be input based on the user's current health condition, allowing only necessary information to be input. The input unit can also prioritize relevant information based on the user's goals. Furthermore, the input unit can customize the input form according to the user's health condition and goals to reduce the input burden. This reduces the input burden by allowing only necessary information to be input according to the user's health condition and goals. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's health condition data and goal data into AI, which can analyze the data and suggest an optimal input method.

[0037] The input unit can select the optimal input means according to the user's input method when inputting lifestyle habits and exercise history. The input unit can select the optimal input means according to the user's input method when inputting lifestyle habits and exercise history. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can preferentially provide voice input. Furthermore, if the user prefers text input, the input unit can preferentially provide text input. Furthermore, if the user prefers image input, the input unit can preferentially provide image input. This improves input convenience by providing an input means according to the user's preferences. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's input method data to AI, which can analyze the data and suggest the optimal input means.

[0038] When inputting lifestyle habits and exercise history, the input unit can prioritize inputting highly relevant information taking into account the user's geographical location information. When inputting lifestyle habits and exercise history, the input unit prioritizes inputting highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the input unit can prioritize inputting information related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting information related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting information related to the user's home. This improves input accuracy by prioritizing input of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to AI, which can analyze the data and suggest an optimal input method.

[0039] The input unit can analyze the user's social media activity and input related information when inputting lifestyle habits and exercise history. The input unit can analyze the user's social media activity and input related information when inputting lifestyle habits and exercise history. Social media activity includes, but is not limited to, analysis of posted content and evaluation of activity frequency. For example, the input unit can automatically input the exercise history shared by the user on social media. The input unit can also analyze the user's social media posts and input related information. Furthermore, the input unit can input related information based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information is automatically input and input efficiency is improved. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's social media activity data into AI, which can analyze the data and suggest an optimal input method.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting lifestyle habits and exercise history. The input unit customizes the input method by reflecting the user's past feedback when inputting lifestyle habits and exercise history. Past feedback includes, but is not limited to, the user's evaluations and records of areas for improvement. For example, the input unit customizes the input form based on feedback provided by the user in the past. The input unit can also improve the input method and make it easier to use based on the user's past feedback. Furthermore, the input unit can also optimize the input procedure by reflecting the user's feedback. In this way, the input method is optimized and easier to use by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data into AI, which can analyze the data and suggest the optimal input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle habits and exercise history during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle habits and exercise history during analysis. The level of detail of the analysis includes, but is not limited to, the granularity of the data and the depth of the analysis. For example, the analysis unit can perform a detailed analysis of important lifestyle habits and exercise history. The analysis unit can also perform a simplified analysis of less important lifestyle habits and exercise history. Furthermore, the analysis unit can determine the importance and adjust the level of detail of the analysis based on the user's goals. This improves the efficiency of the analysis by adjusting the level of detail of the analysis according to the importance of the lifestyle habits and exercise history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input lifestyle habit data and exercise history data into AI, which can analyze the data and suggest an optimal level of analysis detail.

[0042] The analysis unit can apply different analysis algorithms depending on the category of lifestyle habits and exercise history during analysis. The analysis unit can apply different analysis algorithms depending on the category of lifestyle habits and exercise history during analysis. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit can apply an analysis algorithm specialized for improving muscle strength to strength training. The analysis unit can also apply an analysis algorithm specialized for improving cardiopulmonary function to aerobic exercise. Furthermore, the analysis unit can apply an analysis algorithm specialized for nutritional balance to eating habits. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of lifestyle habits and exercise history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input lifestyle habit data and exercise history data into AI, which can analyze the data and propose an optimal analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past trends and success stories. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also analyze trends from the user's past analysis results and improve the accuracy of the analysis. Furthermore, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the current analysis result is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can analyze the data and propose an optimal analysis method.

[0044] The analysis unit can determine the analysis priority based on the time of submission of lifestyle habits and exercise history during analysis. The analysis unit can determine the analysis priority based on the time of submission of lifestyle habits and exercise history during analysis. The submission time includes, but is not limited to, the submission deadline and the submission frequency. For example, the analysis unit prioritizes analysis of recently submitted lifestyle habits and exercise history. The analysis unit can also lower the priority of information submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. This improves the efficiency of analysis by determining the analysis priority based on the time of submission. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the time of submission of lifestyle habit data and exercise history data into AI, which can analyze the data and propose an optimal analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of lifestyle habits and exercise history during analysis. The analysis unit can adjust the order of analysis based on the relevance of lifestyle habits and exercise history during analysis. Relevance includes, but is not limited to, data correlation and topic consistency. For example, the analysis unit prioritizes analysis of highly relevant lifestyle habits and exercise history. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of lifestyle habits and exercise history. This improves analysis efficiency by adjusting the order of analysis based on the relevance. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input relevance data between lifestyle habit data and exercise history data into AI, which can analyze the data and propose an optimal analysis order.

[0046] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. Technical terms include, but are not limited to, the user's level of knowledge and the difficulty level of the terms. For example, the analysis unit uses detailed technical terms when the user has technical knowledge. The analysis unit can also explain the analysis results in simple terms when the user does not have technical knowledge. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. This facilitates understanding of the analysis results by adjusting the way the analysis results are presented according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then analyze the data and suggest the optimal way to present the analysis.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the training plan when making the proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of the training plan when making the proposal. The level of detail of the proposal can include, but is not limited to, the depth of the proposal content and the granularity of the information. For example, the suggestion unit can make detailed suggestions for important training plans. The suggestion unit can also make simplified suggestions for less important training plans. Furthermore, the suggestion unit can determine the importance and adjust the level of detail of the proposal based on the user's goals. This improves the efficiency of the proposal by adjusting the level of detail of the proposal according to the importance of the training plan. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input training plan importance data into AI, which can analyze the data and suggest an optimal level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the training plan when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the category of the training plan when making a suggestion. Examples of suggestion algorithms include, but are not limited to, recommendation systems and optimization algorithms. For example, the suggestion unit can apply a suggestion algorithm specialized for improving muscle strength to strength training. The suggestion unit can also apply a suggestion algorithm specialized for improving cardiopulmonary function to aerobic exercise. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for nutritional balance to eating habits. In this way, the suggestion algorithm can be applied depending on the category of the training plan, thereby improving the accuracy of the suggestion. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input category data of the training plan into AI, which can analyze the data and suggest an optimal suggestion algorithm.

[0049] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, success stories and user feedback. For example, the suggestion unit corrects the current suggestion based on the user's past suggestion results. The suggestion unit can also analyze trends from the user's past suggestion results and improve the accuracy of the suggestion. Furthermore, the suggestion unit can optimize the suggestion algorithm by referring to the user's past suggestion results. This improves the accuracy of the current suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI, which can analyze the data and suggest an optimal suggestion method.

[0050] The suggestion unit can determine the priority of the proposals based on the submission time of the training plans when making the proposals. The suggestion unit can determine the priority of the proposals based on the submission time of the training plans when making the proposals. The submission time includes, but is not limited to, for example, a submission deadline and a submission frequency. For example, the suggestion unit can prioritize and propose training plans that were submitted recently. The suggestion unit can also lower the priority of training plans that were submitted earlier. Furthermore, the suggestion unit can adjust the proposal schedule based on the submission time. This improves the efficiency of the proposals by determining the priority of the proposals based on the submission time. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input data on the submission time of the training plans into AI, which can analyze the data and suggest an optimal proposal priority.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the training plans when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of the training plans when making suggestions. Relevance includes, but is not limited to, data correlation, topic consistency, and the like. For example, the suggestion unit can prioritize suggesting highly relevant training plans. The suggestion unit can also postpone suggesting less relevant training plans. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of the training plans. This improves the efficiency of suggestions by adjusting the order of suggestions based on relevance. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input relevance data of training plans into AI, which can analyze the data and suggest an optimal suggestion order.

[0052] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Technical terms include, but are not limited to, the user's level of knowledge and the difficulty level of the terms. For example, the suggestion unit may use detailed technical terms if the user has technical knowledge. The suggestion unit may also explain the proposal in simple terms if the user does not have technical knowledge. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This facilitates understanding of the proposal by adjusting the way the proposal is expressed according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into AI, which may analyze the data and suggest an optimal way to express the proposal.

[0053] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. The monitoring unit improves the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. Examples of interrelationships between trainings include, but are not limited to, exercise combinations and impact levels. For example, the monitoring unit performs monitoring by taking into account the interrelationships between strength training and aerobic exercise. The monitoring unit can also perform monitoring by taking into account the interrelationships between dietary habits and exercise habits. Furthermore, the monitoring unit can also perform monitoring by taking into account the interrelationships between rest and training. In this way, the accuracy of monitoring is improved by taking into account the interrelationships between trainings. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input training interrelationship data into AI, which can analyze the data and suggest an optimal monitoring method.

[0054] The monitoring unit can perform monitoring while taking into account attribute information of the trainee. The monitoring unit performs monitoring while taking into account attribute information of the trainee. Attribute information includes, for example, age, gender, and physical fitness level, but is not limited to these examples. For example, the monitoring unit performs monitoring while taking into account the age of the trainee. The monitoring unit can also perform monitoring while taking into account the gender of the trainee. Furthermore, the monitoring unit can perform monitoring while taking into account the health condition of the trainee. In this way, by taking into account the attribute information of the trainee, the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input attribute information data of the trainee into AI, which can analyze the data and propose an optimal monitoring method.

[0055] The monitoring unit can weight the monitoring based on the frequency of training during monitoring. The monitoring unit weights the monitoring based on the frequency of training during monitoring. The frequency of training includes, but is not limited to, for example, the number of times per week or the number of times per month. For example, the monitoring unit performs detailed monitoring when the training is performed frequently. The monitoring unit can also perform brief monitoring when the training is performed infrequently. Furthermore, the monitoring unit can adjust the weighting of the monitoring based on the frequency of training. In this way, weighting the monitoring based on the frequency of training improves the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input training frequency data into AI, which can analyze the data and suggest an optimal monitoring method.

[0056] The monitoring unit can perform monitoring taking into account the geographical distribution of training. The monitoring unit performs monitoring taking into account the geographical distribution of training. Geographical distribution includes, but is not limited to, regional data and the use of location information, for example. For example, if training locations are different, the monitoring unit can perform monitoring according to each location. Furthermore, if training locations are concentrated in a specific region, the monitoring unit can perform monitoring specific to that region. Furthermore, if training locations are spread over a wide area, the monitoring unit can perform overall monitoring. In this way, by taking into account the geographical distribution of training, the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographical distribution data of training into AI, which can analyze the data and propose an optimal monitoring method.

[0057] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the training during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to literature related to the training during monitoring. Related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the monitoring unit sets monitoring criteria based on literature related to the training. The monitoring unit can also improve the accuracy of monitoring by referring to literature related to the training. Furthermore, the monitoring unit can optimize the monitoring method by referring to literature related to the training. In this way, the accuracy of monitoring is improved by referring to literature related to the training. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input literature data related to the training into AI, which can analyze the data and propose an optimal monitoring method.

[0058] The monitoring unit can perform monitoring taking into account the market value of the training. The monitoring unit performs monitoring taking into account the market value of the training. Market value includes, but is not limited to, economic value, demand assessment, etc. For example, the monitoring unit performs detailed monitoring for training with high market value. The monitoring unit can also perform brief monitoring for training with low market value. Furthermore, the monitoring unit can determine monitoring priorities based on the market value of the training. This allows the monitoring priorities to be determined by taking into account the market value of the training, thereby achieving efficient monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input market value data of the training into AI, which can analyze the data and propose an optimal monitoring method.

[0059] The adjustment unit can improve the accuracy of the adjustment by taking into account the interrelationships between the training plans during the adjustment. The adjustment unit improves the accuracy of the adjustment by taking into account the interrelationships between the training plans during the adjustment. Interrelationships include, but are not limited to, data correlations and influences. For example, the adjustment unit makes the adjustment by taking into account the interrelationships between strength training and aerobic exercise. The adjustment unit can also make the adjustment by taking into account the interrelationships between dietary habits and exercise habits. Furthermore, the adjustment unit can also make the adjustment by taking into account the interrelationships between rest and training. In this way, the accuracy of the adjustment is improved by taking into account the interrelationships between the training plans. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input interrelationship data between the training plans into AI, which can analyze the data and propose an optimal adjustment method.

[0060] The adjustment unit can make adjustments taking into account attribute information of the person implementing the training plan when making adjustments. The adjustment unit makes adjustments taking into account attribute information of the person implementing the training plan when making adjustments. Attribute information includes, but is not limited to, for example, age, gender, and physical fitness level. For example, the adjustment unit makes adjustments taking into account the age of the person implementing the training plan. The adjustment unit can also make adjustments taking into account the gender of the person implementing the training plan. Furthermore, the adjustment unit can make adjustments taking into account the health condition of the person implementing the training plan. In this way, the accuracy of the adjustments is improved by taking into account the attribute information of the person implementing the training plan. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input attribute information data of the person implementing the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0061] The adjustment unit can weight the adjustment based on the frequency of implementation of the training plan during adjustment. The adjustment unit weights the adjustment based on the frequency of implementation of the training plan during adjustment. The implementation frequency includes, but is not limited to, for example, the number of times per week or the number of times per month. For example, the adjustment unit performs detailed adjustment when the implementation frequency of the training plan is high. Furthermore, the adjustment unit can also perform simple adjustment when the implementation frequency of the training plan is low. Furthermore, the adjustment unit can adjust the weight of the adjustment based on the implementation frequency of the training plan. In this way, weighting the adjustment based on the implementation frequency of the training plan improves the accuracy of the adjustment. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the frequency of implementation of the training plan to AI, which can analyze the data and suggest an optimal adjustment method.

[0062] The adjustment unit can take into account the geographical distribution of the training plan when making the adjustment. The adjustment unit can take into account the geographical distribution of the training plan when making the adjustment. Geographical distribution includes, but is not limited to, the use of regional data and location information, for example. For example, if the training plan is implemented in different locations, the adjustment unit can make adjustments according to each location. Furthermore, if the training plan is implemented in a concentrated area, the adjustment unit can make adjustments specific to that area. Furthermore, if the training plan is implemented in a wide area, the adjustment unit can make overall adjustments. In this way, the accuracy of the adjustment is improved by taking into account the geographical distribution of the training plan. Some or all of the above-described processing by the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input geographical distribution data of the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0063] The adjustment unit can improve the accuracy of the adjustment by referring to literature related to the training plan during adjustment. The adjustment unit can improve the accuracy of the adjustment by referring to literature related to the training plan during adjustment. Related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the adjustment unit sets adjustment criteria based on literature related to the training plan. The adjustment unit can also improve the accuracy of the adjustment by referring to literature related to the training plan. Furthermore, the adjustment unit can also optimize the adjustment method by referring to literature related to the training plan. This improves the accuracy of the adjustment by referring to literature related to the training plan. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input literature data related to the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0064] The adjustment unit can make adjustments taking into account the market value of the training plan when making adjustments. The adjustment unit can make adjustments taking into account the market value of the training plan when making adjustments. Market value includes, but is not limited to, economic value, demand assessment, and the like. For example, the adjustment unit can make detailed adjustments to training plans with high market value. The adjustment unit can also make simple adjustments to training plans with low market value. Furthermore, the adjustment unit can determine the priority of adjustments based on the market value of the training plan. This allows the adjustment priority to be determined by taking into account the market value of the training plan, thereby achieving efficient adjustments. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input market value data of the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0065] The recording unit can improve the accuracy of recording by taking into account the interrelationships of training data during recording. The recording unit improves the accuracy of recording by taking into account the interrelationships of training data during recording. Interrelationships include, but are not limited to, data correlation and influence. For example, the recording unit performs recording by taking into account the interrelationships between strength training and aerobic exercise. The recording unit can also perform recording by taking into account the interrelationships between dietary habits and exercise habits. Furthermore, the recording unit can perform recording by taking into account the interrelationships between rest and training. In this way, the accuracy of recording is improved by taking into account the interrelationships of training data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input interrelationship data of training data into AI, which can analyze the data and suggest an optimal recording method.

[0066] The recording unit can record the data while taking into consideration attribute information of the person who has performed the training data. The recording unit can record the data while taking into consideration attribute information of the person who has performed the training data. Attribute information includes, but is not limited to, age, gender, and physical fitness level, for example. For example, the recording unit can record the data while taking into consideration the age of the person who has performed the training data. The recording unit can also record the data while taking into consideration the gender of the person who has performed the training data. Furthermore, the recording unit can record the data while taking into consideration the health condition of the person who has performed the training data. This improves the accuracy of recording by taking into consideration the attribute information of the person who has performed the training data. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input attribute information data of the person who has performed the training data into AI, which can analyze the data and suggest an optimal recording method.

[0067] The recording unit may record the training data while taking into consideration the geographical distribution of the training data. The recording unit may record the training data while taking into consideration the geographical distribution of the training data. Examples of geographical distribution include, but are not limited to, regional data and location information. For example, if the training data is performed in different locations, the recording unit may record the data according to each location. Furthermore, if the training data is performed in a specific region, the recording unit may perform region-specific recording. Furthermore, if the training data is performed in a wide range of locations, the recording unit may perform overall recording. This improves the accuracy of recording by taking into consideration the geographical distribution of the training data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit may input geographical distribution data of the training data into AI, which may analyze the data and suggest an optimal recording method.

[0068] The recording unit can improve the accuracy of the recording by referring to literature related to the training data during recording. The recording unit can improve the accuracy of the recording by referring to literature related to the training data during recording. Related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the recording unit sets recording criteria based on literature related to the training data. The recording unit can also improve the accuracy of the recording by referring to literature related to the training data. Furthermore, the recording unit can also optimize the recording method by referring to literature related to the training data. In this way, the accuracy of the recording is improved by referring to literature related to the training data. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input literature data related to the training data into AI, which can analyze the data and suggest an optimal recording method.

[0069] The recording unit can record the training data while taking into account its market value. The recording unit can record the training data while taking into account its market value. Market value includes, but is not limited to, economic value, demand assessment, and the like. For example, the recording unit can perform detailed recording for training data with high market value. The recording unit can also perform concise recording for training data with low market value. Furthermore, the recording unit can determine the priority of recording based on the market value of the training data. This allows the recording priority to be determined by taking into account the market value of the training data, thereby achieving efficient recording. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can input market value data of the training data into AI, which can analyze the data and propose an optimal recording method.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The training support system can analyze the nutritional balance of the user's diet in addition to the user's lifestyle habits and exercise history, and reflect this in the training plan. For example, the analysis unit can analyze the user's diet and, if necessary nutrients are lacking, suggest dietary improvements. The suggestion unit can adjust the training plan based on the user's nutritional balance and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's diet and adjust the training plan according to changes in nutritional balance. This makes it possible to provide a training plan that takes the user's nutritional balance into consideration, thereby supporting more effective training.

[0072] The training support system can analyze a user's sleep patterns based on the user's lifestyle habits and exercise history and reflect the results in a training plan. For example, the analysis unit can analyze the user's sleep data and, if the quality of sleep is declining, make suggestions for improving sleep. The suggestion unit can also adjust the time and intensity of training based on the user's sleep patterns to suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's sleep data and adjust the training plan according to changes in sleep quality. This allows for more effective training support by providing a training plan that takes the user's sleep patterns into account.

[0073] The training support system can analyze a user's stress level based on the user's lifestyle habits and exercise history and reflect the results in a training plan. For example, the analysis unit can analyze the user's stress data and, if stress is high, suggest relaxing training. The suggestion unit can adjust the intensity and type of training based on the user's stress level and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's stress data and adjust the training plan according to changes in the stress level. This allows for more effective training support by providing a training plan that takes the user's stress level into consideration.

[0074] The training support system can analyze the user's social activities based on the user's lifestyle habits and exercise history and reflect them in the training plan. For example, the analysis unit can analyze the user's social activity data and suggest social training (such as group training or partner training). The suggestion unit can adjust the type and time of training based on the user's social activity and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's social activity data and adjust the training plan according to changes in social activity. This can support more effective training by providing a training plan that takes the user's social activity into consideration.

[0075] The training support system can analyze a user's hobbies and interests based on the user's lifestyle habits and exercise history and reflect them in a training plan. For example, the analysis unit can analyze the user's hobby and interest data and suggest training that the user can enjoy (such as dancing or sports). The suggestion unit can adjust the type and time of training based on the user's hobbies and interests to suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's hobby and interest data and adjust the training plan according to changes in hobbies and interests. This makes it possible to support more effective training by providing a training plan that takes the user's hobbies and interests into consideration.

[0076] The training support system can analyze a user's health risks based on the user's lifestyle habits and exercise history and reflect them in a training plan. For example, the analysis unit can analyze the user's health data and, if the health risk is high, suggest training to reduce the risk. The suggestion unit can adjust the intensity and type of training based on the user's health risk and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's health data and adjust the training plan according to changes in health risk. This makes it possible to support more effective training by providing a training plan that takes the user's health risk into consideration.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The input unit inputs the user's lifestyle data and exercise history data. The lifestyle data includes, for example, dietary habits, sleep duration, stress level, etc. The exercise history data includes, for example, type of exercise, exercise duration, exercise intensity, etc. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm. Step 3: The suggestion unit proposes a training plan based on the results obtained by the analysis unit. The training plan includes, for example, the type, frequency, and intensity of exercise. Step 4: The monitoring unit monitors the progress of the training based on the plan proposed by the proposing unit, for example, based on the frequency of data collection and the type of sensor used. Step 5: The adjustment unit adjusts the training plan based on the progress obtained by the monitoring unit. The adjustment is performed based on, for example, the criteria for changing the training plan and the frequency of adjustment.

[0079] (Example 2) A training support system according to an embodiment of the present invention inputs a user's lifestyle and exercise history, analyzes the history using AI, proposes an optimal training plan, monitors the user's progress, and adjusts the plan as needed. The training support system inputs the user's lifestyle and exercise history, analyzes the history using AI, and proposes an optimal training plan. Furthermore, the system monitors the user's training progress and adjusts the plan as needed. For example, in a training support system, the user inputs their lifestyle and exercise history into an application. For example, they input information such as their diet, exercise frequency, and past training experience. This information is then input into AI. The AI ​​then analyzes the input information and proposes an optimal training plan for the user. The AI ​​determines what type of training is effective based on the user's lifestyle and exercise history. For example, it proposes a training plan tailored to the user's goals, such as strength training or aerobic exercise. Furthermore, the system monitors the user's training progress. When the user trains, the application records the user's progress, which is then analyzed by AI. For example, data such as the number of training sessions, duration, and calories burned is collected and analyzed by AI to evaluate the user's training effectiveness. If necessary, the training plan is adjusted. The AI ​​then reviews the training plan based on the user's progress and re-proposes an optimal plan. For example, the system makes adjustments to suit the user's condition, such as increasing the training load or changing the type of exercise. This allows the training support system to continuously support training that suits the user by proposing an optimal training plan based on the user's lifestyle and exercise history, monitoring progress, and adjusting the plan as needed. This allows the training support system to continuously support training that suits the user by proposing an optimal training plan based on the user's lifestyle and exercise history, monitoring progress, and adjusting the plan as needed. For example, the system can support sustainable training while resolving issues such as cost and location.

[0080] A training support system according to an embodiment includes an input unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The input unit inputs lifestyle habit data and exercise history data of a user. The lifestyle habit data includes, for example, but is not limited to, dietary details, sleep duration, and stress level. The exercise history data includes, for example, but is not limited to, exercise type, exercise duration, and exercise intensity. The analysis unit analyzes the information input by the input unit. The analysis is performed, for example, but is not limited to, using statistical analysis of data or a machine learning algorithm. The proposal unit proposes a training plan based on the results obtained by the analysis unit. The training plan includes, for example, but is not limited to, exercise type, frequency, and intensity. The monitoring unit monitors the training progress based on the plan proposed by the proposal unit. The monitoring is performed, for example, but is not limited to, based on the frequency of data collection and the type of sensor used. The adjustment unit adjusts the training plan based on the progress obtained by the monitoring unit. The adjustment is performed, for example, but is not limited to, based on the criteria for changing the training plan and the frequency of adjustment. As a result, the training support system according to the embodiment can continuously support training suited to the user by proposing an optimal training plan based on the user's lifestyle habits and exercise history, monitoring progress, and adjusting the plan as necessary.

[0081] The training support system includes a recording unit that records the training progress. The recording unit records the training progress. Recording includes, but is not limited to, for example, the data storage format and the recording frequency. For example, the recording unit records data such as the number of training sessions, the time, and calories burned. The recording unit can also visually display the training progress as a graph or chart. For example, the recording unit may display the training progress in chronological order, allowing the user to check their own progress. By recording the training progress, the user's training history can be managed in detail. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit may input the training progress into AI, which may analyze and record the data.

[0082] The training support system includes an adjustment unit that makes adjustments according to the user's condition. The adjustment unit makes adjustments according to the user's condition. The user's condition includes, but is not limited to, health condition, fatigue level, motivation, etc. For example, the adjustment unit adjusts the training plan based on the user's health condition. The adjustment unit can also adjust the training load based on the user's fatigue level. Furthermore, the adjustment unit can change the type of training based on the user's motivation. This allows for more effective training by adjusting the training plan according to the user's condition. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's condition data into AI, which can analyze the data and propose an optimal training plan.

[0083] The analysis unit can analyze the user's lifestyle habits and exercise history and propose an optimal training plan. The analysis unit can analyze the user's lifestyle habits and exercise history and propose an optimal training plan. The optimal training plan may include, but is not limited to, individual goals, physical fitness level, and health status. For example, the analysis unit can determine what type of training is effective based on the user's lifestyle habits data. The analysis unit can also evaluate the effectiveness of past training based on the user's exercise history data and propose an optimal training plan. Furthermore, the analysis unit can customize a training plan based on the user's goals. In this way, an individually optimized training plan can be provided by analyzing the user's lifestyle habits and exercise history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's lifestyle habits data and exercise history data into AI, which can analyze the data and propose an optimal training plan.

[0084] The monitoring unit collects data on the number of training sessions, duration, and calories burned, and the AI ​​can analyze the data. The monitoring unit collects data on the number of training sessions, duration, and calories burned, and the AI ​​analyzes the data. The number of training sessions includes, for example, but is not limited to, the number of sessions per day or the number of sessions per week. The training duration includes, for example, but is not limited to, the duration of a single training session or the cumulative duration. The calories burned includes, for example, but is not limited to, the calculation formula and the type of sensor used. For example, the monitoring unit records the number of training sessions, and the AI ​​analyzes the data to evaluate the effectiveness of the training. The monitoring unit can also record the duration of training, and the AI ​​analyzes the data to evaluate the progress of the training. Furthermore, the monitoring unit can record the calories burned, and the AI ​​analyzes the data to evaluate the effectiveness of the training. In this way, detailed training data can be collected and analyzed by the AI, allowing the effectiveness of the training to be accurately evaluated. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input data on the number of training sessions, duration, and calories burned into the AI, which can then analyze the data and evaluate the effectiveness of the training.

[0085] The adjustment unit can increase the training load, change the type of exercise, or make adjustments to suit the user's condition. The adjustment unit can increase the training load, change the type of exercise, or make adjustments to suit the user's condition. Examples of training load include, but are not limited to, increasing weight and increasing the number of repetitions. Examples of types of exercise include, but are not limited to, aerobic exercise and strength training. For example, the adjustment unit can increase the training load based on the user's progress. The adjustment unit can also change the type of exercise based on the user's condition. Furthermore, the adjustment unit can adjust the training plan based on the user's goals. This allows for flexibly adjusting the training plan according to the user's condition, thereby providing effective training. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's condition data into AI, which can analyze the data and propose an optimal training plan.

[0086] The input unit can estimate the user's emotions and adjust the timing of inputting lifestyle habits and exercise history based on the estimated user emotions. The input unit can estimate the user's emotions and adjust the timing of inputting lifestyle habits and exercise history based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the input unit can prompt the user to input lifestyle habits and exercise history during a time when the user can relax. The input unit can also prompt the user to input more detailed information when the user is relaxed. Furthermore, if the user is in a hurry, the input unit can provide a simplified input form to enable quick input. This reduces stress and improves input efficiency by adjusting the input timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the input unit can be performed using, for example, AI or without AI. For example, the input unit can input the user's emotional data into the AI, which can then analyze the data and suggest the optimal input timing.

[0087] The input unit can analyze the user's past input history and select the optimal input method. The input unit analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, input frequency and input content trends, but is not limited to these examples. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also customize an input form based on information previously input by the user. Furthermore, the input unit can analyze the user's past input history to determine input tendencies during specific time periods and suggest the optimal input timing. In this way, by analyzing the past input history, the optimal input method can be provided for the user. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data into AI, which analyzes the data and suggests the optimal input method.

[0088] The input unit can filter the information based on the user's current health condition and goals when inputting lifestyle habits and exercise history. The input unit can filter the information based on the user's current health condition and goals when inputting lifestyle habits and exercise history. Examples of filtering include, but are not limited to, health condition evaluation criteria and goal setting methods. For example, the input unit can filter the information to be input based on the user's current health condition, allowing only necessary information to be input. The input unit can also prioritize relevant information based on the user's goals. Furthermore, the input unit can customize the input form according to the user's health condition and goals to reduce the input burden. This reduces the input burden by allowing only necessary information to be input according to the user's health condition and goals. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's health condition data and goal data into AI, which can analyze the data and suggest an optimal input method.

[0089] The input unit can select the optimal input means according to the user's input method when inputting lifestyle habits and exercise history. The input unit can select the optimal input means according to the user's input method when inputting lifestyle habits and exercise history. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can preferentially provide voice input. Furthermore, if the user prefers text input, the input unit can preferentially provide text input. Furthermore, if the user prefers image input, the input unit can preferentially provide image input. This improves input convenience by providing an input means according to the user's preferences. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's input method data to AI, which can analyze the data and suggest the optimal input means.

[0090] The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. Information priority can include, but is not limited to, an importance evaluation criterion and an emotional impact. For example, the input unit can prioritize input of only important information when the user is stressed. The input unit can also prioritize input of detailed information when the user is relaxed. Furthermore, the input unit can prioritize input of simplified information when the user is in a hurry. This improves input efficiency by determining the priority of information to be input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the input unit can be performed using, for example, an AI. For example, the input unit can input the user's emotion data into an AI, which can analyze the data and suggest an optimal input method.

[0091] When inputting lifestyle habits and exercise history, the input unit can prioritize inputting highly relevant information taking into account the user's geographical location information. When inputting lifestyle habits and exercise history, the input unit prioritizes inputting highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the input unit can prioritize inputting information related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting information related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting information related to the user's home. This improves input accuracy by prioritizing input of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to AI, which can analyze the data and suggest an optimal input method.

[0092] The input unit can analyze the user's social media activity and input related information when inputting lifestyle habits and exercise history. The input unit can analyze the user's social media activity and input related information when inputting lifestyle habits and exercise history. Social media activity includes, but is not limited to, analysis of posted content and evaluation of activity frequency. For example, the input unit can automatically input the exercise history shared by the user on social media. The input unit can also analyze the user's social media posts and input related information. Furthermore, the input unit can input related information based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information is automatically input and input efficiency is improved. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's social media activity data into AI, which can analyze the data and suggest an optimal input method.

[0093] The input unit can customize the input method by reflecting the user's past feedback when inputting lifestyle habits and exercise history. The input unit customizes the input method by reflecting the user's past feedback when inputting lifestyle habits and exercise history. Past feedback includes, but is not limited to, the user's evaluations and records of areas for improvement. For example, the input unit customizes the input form based on feedback provided by the user in the past. The input unit can also improve the input method and make it easier to use based on the user's past feedback. Furthermore, the input unit can also optimize the input procedure by reflecting the user's feedback. In this way, the input method is optimized and easier to use by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data into AI, which can analyze the data and suggest the optimal input method.

[0094] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The presentation method of the analysis includes, but is not limited to, graph display, text display, etc. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. This adjusts the presentation method of the analysis according to the user's emotion, facilitating understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input a user's emotional data into AI, which can then analyze the data and suggest the optimal analytical and expression method.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle habits and exercise history during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle habits and exercise history during analysis. The level of detail of the analysis includes, but is not limited to, the granularity of the data and the depth of the analysis. For example, the analysis unit can perform a detailed analysis of important lifestyle habits and exercise history. The analysis unit can also perform a simplified analysis of less important lifestyle habits and exercise history. Furthermore, the analysis unit can determine the importance and adjust the level of detail of the analysis based on the user's goals. This improves the efficiency of the analysis by adjusting the level of detail of the analysis according to the importance of the lifestyle habits and exercise history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input lifestyle habit data and exercise history data into AI, which can analyze the data and suggest an optimal level of analysis detail.

[0096] The analysis unit can apply different analysis algorithms depending on the category of lifestyle habits and exercise history during analysis. The analysis unit can apply different analysis algorithms depending on the category of lifestyle habits and exercise history during analysis. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit can apply an analysis algorithm specialized for improving muscle strength to strength training. The analysis unit can also apply an analysis algorithm specialized for improving cardiopulmonary function to aerobic exercise. Furthermore, the analysis unit can apply an analysis algorithm specialized for nutritional balance to eating habits. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of lifestyle habits and exercise history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input lifestyle habit data and exercise history data into AI, which can analyze the data and propose an optimal analysis algorithm.

[0097] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past trends and success stories. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also analyze trends from the user's past analysis results and improve the accuracy of the analysis. Furthermore, the analysis unit can optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the current analysis result is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can analyze the data and propose an optimal analysis method.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Examples of the length of the analysis include, but are not limited to, the analysis time and the amount of data. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. This adjusts the length of the analysis according to the user's emotions, facilitating understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into an AI, which then analyzes the data and proposes an optimal analysis method.

[0099] The analysis unit can determine the analysis priority based on the time of submission of lifestyle habits and exercise history during analysis. The analysis unit can determine the analysis priority based on the time of submission of lifestyle habits and exercise history during analysis. The submission time includes, but is not limited to, the submission deadline and the submission frequency. For example, the analysis unit prioritizes analysis of recently submitted lifestyle habits and exercise history. The analysis unit can also lower the priority of information submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. This improves the efficiency of analysis by determining the analysis priority based on the time of submission. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the time of submission of lifestyle habit data and exercise history data into AI, which can analyze the data and propose an optimal analysis priority.

[0100] The analysis unit can adjust the order of analysis based on the relevance of lifestyle habits and exercise history during analysis. The analysis unit can adjust the order of analysis based on the relevance of lifestyle habits and exercise history during analysis. Relevance includes, but is not limited to, data correlation and topic consistency. For example, the analysis unit prioritizes analysis of highly relevant lifestyle habits and exercise history. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of lifestyle habits and exercise history. This improves analysis efficiency by adjusting the order of analysis based on the relevance. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input relevance data between lifestyle habit data and exercise history data into AI, which can analyze the data and propose an optimal analysis order.

[0101] The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. The analysis unit can adjust the use of technical terms during analysis according to the user's level of expertise. Technical terms include, but are not limited to, the user's level of knowledge and the difficulty level of the terms. For example, the analysis unit uses detailed technical terms when the user has technical knowledge. The analysis unit can also explain the analysis results in simple terms when the user does not have technical knowledge. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. This facilitates understanding of the analysis results by adjusting the way the analysis results are presented according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then analyze the data and suggest the optimal way to present the analysis.

[0102] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. Examples of ways to present the suggestion include, but are not limited to, text suggestions and visual suggestions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. Furthermore, the suggestion unit can provide suggestions that focus on the main points when the user is in a hurry. This adjusts the way the suggestion is presented based on the user's emotion, thereby improving the likelihood of the suggestion being accepted. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can analyze the data and propose an optimal way to present the suggestion.

[0103] The suggestion unit can adjust the level of detail of the proposal based on the importance of the training plan when making the proposal. The suggestion unit can adjust the level of detail of the proposal based on the importance of the training plan when making the proposal. The level of detail of the proposal can include, but is not limited to, the depth of the proposal content and the granularity of the information. For example, the suggestion unit can make detailed suggestions for important training plans. The suggestion unit can also make simplified suggestions for less important training plans. Furthermore, the suggestion unit can determine the importance and adjust the level of detail of the proposal based on the user's goals. This improves the efficiency of the proposal by adjusting the level of detail of the proposal according to the importance of the training plan. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input training plan importance data into AI, which can analyze the data and suggest an optimal level of detail of the proposal.

[0104] The suggestion unit can apply different suggestion algorithms depending on the category of the training plan when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the category of the training plan when making a suggestion. Examples of suggestion algorithms include, but are not limited to, recommendation systems and optimization algorithms. For example, the suggestion unit can apply a suggestion algorithm specialized for improving muscle strength to strength training. The suggestion unit can also apply a suggestion algorithm specialized for improving cardiopulmonary function to aerobic exercise. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for nutritional balance to eating habits. In this way, the suggestion algorithm can be applied depending on the category of the training plan, thereby improving the accuracy of the suggestion. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input category data of the training plan into AI, which can analyze the data and suggest an optimal suggestion algorithm.

[0105] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. Past suggestion results include, but are not limited to, success stories and user feedback. For example, the suggestion unit corrects the current suggestion based on the user's past suggestion results. The suggestion unit can also analyze trends from the user's past suggestion results and improve the accuracy of the suggestion. Furthermore, the suggestion unit can optimize the suggestion algorithm by referring to the user's past suggestion results. This improves the accuracy of the current suggestion by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI, which can analyze the data and suggest an optimal suggestion method.

[0106] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The length of the suggestion can include, but is not limited to, the amount of suggestion content and the duration of the suggestion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. Furthermore, the suggestion unit can provide suggestions that focus on the main points when the user is in a hurry. This adjusts the length of the suggestion according to the user's emotion, thereby improving the acceptability of the suggestion. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can analyze the data and suggest an optimal suggestion method.

[0107] The suggestion unit can determine the priority of the proposals based on the submission time of the training plans when making the proposals. The suggestion unit can determine the priority of the proposals based on the submission time of the training plans when making the proposals. The submission time includes, but is not limited to, for example, a submission deadline and a submission frequency. For example, the suggestion unit can prioritize and propose training plans that were submitted recently. The suggestion unit can also lower the priority of training plans that were submitted earlier. Furthermore, the suggestion unit can adjust the proposal schedule based on the submission time. This improves the efficiency of the proposals by determining the priority of the proposals based on the submission time. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input data on the submission time of the training plans into AI, which can analyze the data and suggest an optimal proposal priority.

[0108] The suggestion unit can adjust the order of suggestions based on the relevance of the training plans when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of the training plans when making suggestions. Relevance includes, but is not limited to, data correlation, topic consistency, and the like. For example, the suggestion unit can prioritize suggesting highly relevant training plans. The suggestion unit can also postpone suggesting less relevant training plans. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of the training plans. This improves the efficiency of suggestions by adjusting the order of suggestions based on relevance. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input relevance data of training plans into AI, which can analyze the data and suggest an optimal suggestion order.

[0109] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. Technical terms include, but are not limited to, the user's level of knowledge and the difficulty level of the terms. For example, the suggestion unit may use detailed technical terms if the user has technical knowledge. The suggestion unit may also explain the proposal in simple terms if the user does not have technical knowledge. Furthermore, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This facilitates understanding of the proposal by adjusting the way the proposal is expressed according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the user's level of expertise data into AI, which may analyze the data and suggest an optimal way to express the proposal.

[0110] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user emotions. The monitoring criteria include, but are not limited to, data collection frequency and evaluation criteria. For example, the monitoring unit can perform detailed monitoring when the user is relaxed. The monitoring unit can also perform brief monitoring when the user is stressed. Furthermore, the monitoring unit can perform monitoring that focuses on the main points when the user is in a hurry. This improves the accuracy of monitoring by adjusting the monitoring criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into an AI, which can analyze the data and propose optimal monitoring criteria.

[0111] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. The monitoring unit improves the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. Examples of interrelationships between trainings include, but are not limited to, exercise combinations and impact levels. For example, the monitoring unit performs monitoring by taking into account the interrelationships between strength training and aerobic exercise. The monitoring unit can also perform monitoring by taking into account the interrelationships between dietary habits and exercise habits. Furthermore, the monitoring unit can also perform monitoring by taking into account the interrelationships between rest and training. In this way, the accuracy of monitoring is improved by taking into account the interrelationships between trainings. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input training interrelationship data into AI, which can analyze the data and suggest an optimal monitoring method.

[0112] The monitoring unit can perform monitoring while taking into account attribute information of the trainee. The monitoring unit performs monitoring while taking into account attribute information of the trainee. Attribute information includes, for example, age, gender, and physical fitness level, but is not limited to these examples. For example, the monitoring unit performs monitoring while taking into account the age of the trainee. The monitoring unit can also perform monitoring while taking into account the gender of the trainee. Furthermore, the monitoring unit can perform monitoring while taking into account the health condition of the trainee. In this way, by taking into account the attribute information of the trainee, the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input attribute information data of the trainee into AI, which can analyze the data and propose an optimal monitoring method.

[0113] The monitoring unit can weight the monitoring based on the frequency of training during monitoring. The monitoring unit weights the monitoring based on the frequency of training during monitoring. The frequency of training includes, but is not limited to, for example, the number of times per week or the number of times per month. For example, the monitoring unit performs detailed monitoring when the training is performed frequently. The monitoring unit can also perform brief monitoring when the training is performed infrequently. Furthermore, the monitoring unit can adjust the weighting of the monitoring based on the frequency of training. In this way, weighting the monitoring based on the frequency of training improves the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input training frequency data into AI, which can analyze the data and suggest an optimal monitoring method.

[0114] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user emotions. The display order can include, but is not limited to, evaluation criteria for importance and the impact of emotions. For example, if the user is relaxed, the monitoring unit can prioritize displaying detailed monitoring results. Furthermore, if the user is stressed, the monitoring unit can prioritize displaying concise monitoring results. Furthermore, if the user is in a hurry, the monitoring unit can prioritize displaying monitoring results that highlight the main points. This adjusts the display order of the monitoring results according to the user's emotions, facilitating understanding of the results. Emotion estimation is achieved 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. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotional data into AI, which can then analyze the data and suggest the optimal display order for the monitoring results.

[0115] The monitoring unit can perform monitoring taking into account the geographical distribution of training. The monitoring unit performs monitoring taking into account the geographical distribution of training. Geographical distribution includes, but is not limited to, regional data and the use of location information, for example. For example, if training locations are different, the monitoring unit can perform monitoring according to each location. Furthermore, if training locations are concentrated in a specific region, the monitoring unit can perform monitoring specific to that region. Furthermore, if training locations are spread over a wide area, the monitoring unit can perform overall monitoring. In this way, by taking into account the geographical distribution of training, the accuracy of monitoring is improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographical distribution data of training into AI, which can analyze the data and propose an optimal monitoring method.

[0116] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the training during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to literature related to the training during monitoring. Related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the monitoring unit sets monitoring criteria based on literature related to the training. The monitoring unit can also improve the accuracy of monitoring by referring to literature related to the training. Furthermore, the monitoring unit can optimize the monitoring method by referring to literature related to the training. In this way, the accuracy of monitoring is improved by referring to literature related to the training. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or without AI. For example, the monitoring unit can input literature data related to the training into AI, which can analyze the data and propose an optimal monitoring method.

[0117] The monitoring unit can perform monitoring taking into account the market value of the training. The monitoring unit performs monitoring taking into account the market value of the training. Market value includes, but is not limited to, economic value, demand assessment, etc. For example, the monitoring unit performs detailed monitoring for training with high market value. The monitoring unit can also perform brief monitoring for training with low market value. Furthermore, the monitoring unit can determine monitoring priorities based on the market value of the training. This allows the monitoring priorities to be determined by taking into account the market value of the training, thereby achieving efficient monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input market value data of the training into AI, which can analyze the data and propose an optimal monitoring method.

[0118] The adjustment unit can estimate the user's emotions and determine the priority of the training plans to be adjusted based on the estimated user emotions. The adjustment unit estimates the user's emotions and determines the priority of the training plans to be adjusted based on the estimated user emotions. The priority can include, but is not limited to, evaluation criteria for importance and the impact of emotions. For example, when the user is relaxed, the adjustment unit can prioritize adjusting a detailed training plan. Furthermore, when the user is stressed, the adjustment unit can prioritize adjusting a concise training plan. Furthermore, when the user is in a hurry, the adjustment unit can prioritize adjusting a training plan that focuses on the main points. In this way, by determining the priority of the training plans according to the user's emotions, effective adjustment of the training plan is achieved. 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. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, an AI, or without using an AI. For example, the adjustment unit can input the user's emotional data into the AI, which can then analyze the data and suggest optimal training plan priorities.

[0119] The adjustment unit can improve the accuracy of the adjustment by taking into account the interrelationships between the training plans during the adjustment. The adjustment unit improves the accuracy of the adjustment by taking into account the interrelationships between the training plans during the adjustment. Interrelationships include, but are not limited to, data correlations and influences. For example, the adjustment unit makes the adjustment by taking into account the interrelationships between strength training and aerobic exercise. The adjustment unit can also make the adjustment by taking into account the interrelationships between dietary habits and exercise habits. Furthermore, the adjustment unit can also make the adjustment by taking into account the interrelationships between rest and training. In this way, the accuracy of the adjustment is improved by taking into account the interrelationships between the training plans. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input interrelationship data between the training plans into AI, which can analyze the data and propose an optimal adjustment method.

[0120] The adjustment unit can make adjustments taking into account attribute information of the person implementing the training plan when making adjustments. The adjustment unit makes adjustments taking into account attribute information of the person implementing the training plan when making adjustments. Attribute information includes, but is not limited to, for example, age, gender, and physical fitness level. For example, the adjustment unit makes adjustments taking into account the age of the person implementing the training plan. The adjustment unit can also make adjustments taking into account the gender of the person implementing the training plan. Furthermore, the adjustment unit can make adjustments taking into account the health condition of the person implementing the training plan. In this way, the accuracy of the adjustments is improved by taking into account the attribute information of the person implementing the training plan. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input attribute information data of the person implementing the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0121] The adjustment unit can weight the adjustment based on the frequency of implementation of the training plan during adjustment. The adjustment unit weights the adjustment based on the frequency of implementation of the training plan during adjustment. The implementation frequency includes, but is not limited to, for example, the number of times per week or the number of times per month. For example, the adjustment unit performs detailed adjustment when the implementation frequency of the training plan is high. Furthermore, the adjustment unit can also perform simple adjustment when the implementation frequency of the training plan is low. Furthermore, the adjustment unit can adjust the weight of the adjustment based on the implementation frequency of the training plan. In this way, weighting the adjustment based on the implementation frequency of the training plan improves the accuracy of the adjustment. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the frequency of implementation of the training plan to AI, which can analyze the data and suggest an optimal adjustment method.

[0122] The adjustment unit can estimate the user's emotions and adjust the display method of the training plan based on the estimated user's emotions. The adjustment unit estimates the user's emotions and adjusts the display method of the training plan based on the estimated user's emotions. Display methods include, but are not limited to, text display and visual display. For example, the adjustment unit can display a detailed training plan when the user is relaxed. Furthermore, the adjustment unit can display a concise training plan when the user is stressed. Furthermore, the adjustment unit can display a training plan that focuses on the main points when the user is in a hurry. This promotes understanding of the training plan by adjusting the display method of the training plan according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI or without an AI. For example, the adjustment unit can input the user's emotional data into the AI, which can then analyze the data and suggest the optimal display method.

[0123] The adjustment unit can take into account the geographical distribution of the training plan when making the adjustment. The adjustment unit can take into account the geographical distribution of the training plan when making the adjustment. Geographical distribution includes, but is not limited to, the use of regional data and location information, for example. For example, if the training plan is implemented in different locations, the adjustment unit can make adjustments according to each location. Furthermore, if the training plan is implemented in a concentrated area, the adjustment unit can make adjustments specific to that area. Furthermore, if the training plan is implemented in a wide area, the adjustment unit can make overall adjustments. In this way, the accuracy of the adjustment is improved by taking into account the geographical distribution of the training plan. Some or all of the above-described processing by the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input geographical distribution data of the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0124] The adjustment unit can improve the accuracy of the adjustment by referring to literature related to the training plan during adjustment. The adjustment unit can improve the accuracy of the adjustment by referring to literature related to the training plan during adjustment. Related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the adjustment unit sets adjustment criteria based on literature related to the training plan. The adjustment unit can also improve the accuracy of the adjustment by referring to literature related to the training plan. Furthermore, the adjustment unit can also optimize the adjustment method by referring to literature related to the training plan. This improves the accuracy of the adjustment by referring to literature related to the training plan. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input literature data related to the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0125] The adjustment unit can make adjustments taking into account the market value of the training plan when making adjustments. The adjustment unit can make adjustments taking into account the market value of the training plan when making adjustments. Market value includes, but is not limited to, economic value, demand assessment, and the like. For example, the adjustment unit can make detailed adjustments to training plans with high market value. The adjustment unit can also make simple adjustments to training plans with low market value. Furthermore, the adjustment unit can determine the priority of adjustments based on the market value of the training plan. This allows the adjustment priority to be determined by taking into account the market value of the training plan, thereby achieving efficient adjustments. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input market value data of the training plan into AI, which can analyze the data and propose an optimal adjustment method.

[0126] The recording unit can estimate the user's emotions and determine the priority of the training data to be recorded based on the estimated user emotions. The recording unit can estimate the user's emotions and determine the priority of the training data to be recorded based on the estimated user emotions. Priorities include, but are not limited to, evaluation criteria for importance and the impact of emotions. For example, when the user is relaxed, the recording unit can prioritize recording detailed training data. Furthermore, when the user is stressed, the recording unit can prioritize recording concise training data. Furthermore, when the user is in a hurry, the recording unit can prioritize recording training data that focuses on the main points. This enables effective data recording by determining the priority of the training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or without using an AI. For example, the recording unit can input the user's emotional data into AI, which can then analyze the data and suggest the optimal recording method.

[0127] The recording unit can improve the accuracy of recording by taking into account the interrelationships of training data during recording. The recording unit improves the accuracy of recording by taking into account the interrelationships of training data during recording. Interrelationships include, but are not limited to, data correlation and influence. For example, the recording unit performs recording by taking into account the interrelationships between strength training and aerobic exercise. The recording unit can also perform recording by taking into account the interrelationships between dietary habits and exercise habits. Furthermore, the recording unit can perform recording by taking into account the interrelationships between rest and training. In this way, the accuracy of recording is improved by taking into account the interrelationships of training data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input interrelationship data of training data into AI, which can analyze the data and suggest an optimal recording method.

[0128] The recording unit can record the data while taking into consideration attribute information of the person who has performed the training data. The recording unit can record the data while taking into consideration attribute information of the person who has performed the training data. Attribute information includes, but is not limited to, age, gender, and physical fitness level, for example. For example, the recording unit can record the data while taking into consideration the age of the person who has performed the training data. The recording unit can also record the data while taking into consideration the gender of the person who has performed the training data. Furthermore, the recording unit can record the data while taking into consideration the health condition of the person who has performed the training data. This improves the accuracy of recording by taking into consideration the attribute information of the person who has performed the training data. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input attribute information data of the person who has performed the training data into AI, which can analyze the data and suggest an optimal recording method.

[0129] The recording unit can estimate the user's emotions and adjust the display method of the recorded training data based on the estimated user emotions. The recording unit can estimate the user's emotions and adjust the display method of the recorded training data based on the estimated user emotions. Display methods include, but are not limited to, text display and visual display. For example, the recording unit can display detailed training data when the user is relaxed. The recording unit can also display concise training data when the user is stressed. Furthermore, the recording unit can display training data that focuses on the main points when the user is in a hurry. This facilitates understanding of the data by adjusting the display method of the training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI. For example, the recording unit can input the user's emotion data into an AI, which can analyze the data and suggest an optimal display method.

[0130] The recording unit may record the training data while taking into consideration the geographical distribution of the training data. The recording unit may record the training data while taking into consideration the geographical distribution of the training data. Examples of geographical distribution include, but are not limited to, regional data and location information. For example, if the training data is performed in different locations, the recording unit may record the data according to each location. Furthermore, if the training data is performed in a specific region, the recording unit may perform region-specific recording. Furthermore, if the training data is performed in a wide range of locations, the recording unit may perform overall recording. This improves the accuracy of recording by taking into consideration the geographical distribution of the training data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without AI. For example, the recording unit may input geographical distribution data of the training data into AI, which may analyze the data and suggest an optimal recording method.

[0131] The recording unit can improve the accuracy of the recording by referring to literature related to the training data during recording. The recording unit can improve the accuracy of the recording by referring to literature related to the training data during recording. Related literature includes, but is not limited to, literature selection criteria and reference methods. For example, the recording unit sets recording criteria based on literature related to the training data. The recording unit can also improve the accuracy of the recording by referring to literature related to the training data. Furthermore, the recording unit can also optimize the recording method by referring to literature related to the training data. In this way, the accuracy of the recording is improved by referring to literature related to the training data. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input literature data related to the training data into AI, which can analyze the data and suggest an optimal recording method.

[0132] The recording unit can record the training data while taking into account its market value. The recording unit can record the training data while taking into account its market value. Market value includes, but is not limited to, economic value, demand assessment, and the like. For example, the recording unit can perform detailed recording for training data with high market value. The recording unit can also perform concise recording for training data with low market value. Furthermore, the recording unit can determine the priority of recording based on the market value of the training data. This allows the recording priority to be determined by taking into account the market value of the training data, thereby achieving efficient recording. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or without AI. For example, the recording unit can input market value data of the training data into AI, which can analyze the data and propose an optimal recording method. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, proposal unit, monitoring unit, adjustment unit, and recording unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14 and inputs the user's lifestyle habit data and exercise history data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a training plan based on the analysis results. The monitoring unit monitors the training progress using the camera 42 and sensors of the smart device 14. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the training plan based on the progress. The recording unit records the training progress in the storage 50 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, proposal unit, monitoring unit, adjustment unit, and recording unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214 and inputs the user's lifestyle habit data and exercise history data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a training plan based on the analysis results. The monitoring unit monitors the training progress using the camera 42 and sensors of the smart glasses 214. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the training plan based on the progress. The recording unit records the training progress in the storage 50 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, analysis unit, proposal unit, monitoring unit, adjustment unit, and recording unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and inputs the user's lifestyle habit data and exercise history data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a training plan based on the analysis results. The monitoring unit monitors the training progress using the camera 42 and sensors of the headset-type terminal 314. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the training plan based on the progress. The recording unit records the training progress in the storage 50 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, proposal unit, monitoring unit, adjustment unit, and recording unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and inputs the user's lifestyle habit data and exercise history data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a training plan based on the analysis results. The monitoring unit monitors the training progress using the camera 42 and sensors of the robot 414. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the training plan based on the progress. The recording unit records the training progress in the storage 50 of the robot 414.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The training support system can analyze the nutritional balance of the user's diet in addition to the user's lifestyle habits and exercise history, and reflect this in the training plan. For example, the analysis unit can analyze the user's diet and, if necessary nutrients are lacking, suggest dietary improvements. The suggestion unit can adjust the training plan based on the user's nutritional balance and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's diet and adjust the training plan according to changes in nutritional balance. This makes it possible to provide a training plan that takes the user's nutritional balance into consideration, thereby supporting more effective training.

[0135] The training support system can analyze a user's sleep patterns based on the user's lifestyle habits and exercise history and reflect the results in a training plan. For example, the analysis unit can analyze the user's sleep data and, if the quality of sleep is declining, make suggestions for improving sleep. The suggestion unit can also adjust the time and intensity of training based on the user's sleep patterns to suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's sleep data and adjust the training plan according to changes in sleep quality. This allows for more effective training support by providing a training plan that takes the user's sleep patterns into account.

[0136] The training support system can estimate a user's emotions and provide feedback to improve training motivation based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a relaxing training program. The monitoring unit can record the emotions felt by the user during training, and the analysis unit can provide advice to improve motivation based on that data. Furthermore, the adjustment unit can adjust the training plan according to the user's emotions and support the user so that they can continue training in an enjoyable manner. In this way, providing feedback based on the user's emotions can improve training motivation.

[0137] The training support system can analyze a user's stress level based on the user's lifestyle habits and exercise history and reflect the results in a training plan. For example, the analysis unit can analyze the user's stress data and, if stress is high, suggest relaxing training. The suggestion unit can adjust the intensity and type of training based on the user's stress level and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's stress data and adjust the training plan according to changes in the stress level. This allows for more effective training support by providing a training plan that takes the user's stress level into consideration.

[0138] The training support system can estimate the user's emotions and evaluate the training progress based on the estimated emotions. For example, the monitoring unit can record the emotions felt by the user during training, and the analysis unit can evaluate the effectiveness of the training based on that data. The suggestion unit can provide feedback on the training progress based on the user's emotions and support the user so that they can feel a sense of accomplishment. Furthermore, the adjustment unit can adjust the training plan according to the user's emotions and support the user so that they can continue training in an enjoyable manner. In this way, the effectiveness of training can be improved by evaluating the progress based on the user's emotions.

[0139] The training support system can analyze the user's social activities based on the user's lifestyle habits and exercise history and reflect them in the training plan. For example, the analysis unit can analyze the user's social activity data and suggest social training (such as group training or partner training). The suggestion unit can adjust the type and time of training based on the user's social activity and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's social activity data and adjust the training plan according to changes in social activity. This can support more effective training by providing a training plan that takes the user's social activity into consideration.

[0140] The training support system can estimate the user's emotions and customize training feedback based on the estimated emotions. For example, the suggestion unit can provide detailed feedback when the user is relaxed. The monitoring unit can provide concise feedback when the user is feeling stressed. Furthermore, the adjustment unit can provide feedback that focuses on the main points when the user is in a hurry. In this way, the effectiveness of training can be improved by providing feedback according to the user's emotions.

[0141] The training support system can analyze a user's hobbies and interests based on the user's lifestyle habits and exercise history and reflect them in a training plan. For example, the analysis unit can analyze the user's hobby and interest data and suggest training that the user can enjoy (such as dancing or sports). The suggestion unit can adjust the type and time of training based on the user's hobbies and interests to suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's hobby and interest data and adjust the training plan according to changes in hobbies and interests. This makes it possible to support more effective training by providing a training plan that takes the user's hobbies and interests into consideration.

[0142] The training support system can estimate a user's emotions and support the setting of training goals based on the estimated emotions. For example, the suggestion unit can set a long-term goal if the user is relaxed. The monitoring unit can set a short-term goal if the user is feeling stressed. Furthermore, the adjustment unit can set a specific goal if the user is in a hurry. In this way, the effectiveness of training can be improved by setting goals according to the user's emotions.

[0143] The training support system can analyze a user's health risks based on the user's lifestyle habits and exercise history and reflect them in a training plan. For example, the analysis unit can analyze the user's health data and, if the health risk is high, suggest training to reduce the risk. The suggestion unit can adjust the intensity and type of training based on the user's health risk and suggest effective training. Furthermore, the monitoring unit can continuously monitor the user's health data and adjust the training plan according to changes in health risk. This makes it possible to support more effective training by providing a training plan that takes the user's health risk into consideration.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The input unit inputs the user's lifestyle data and exercise history data. The lifestyle data includes, for example, dietary habits, sleep duration, stress level, etc. The exercise history data includes, for example, type of exercise, exercise duration, exercise intensity, etc. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm. Step 3: The suggestion unit proposes a training plan based on the results obtained by the analysis unit. The training plan includes, for example, the type, frequency, and intensity of exercise. Step 4: The monitoring unit monitors the progress of the training based on the plan proposed by the proposing unit, for example, based on the frequency of data collection and the type of sensor used. Step 5: The adjustment unit adjusts the training plan based on the progress obtained by the monitoring unit. The adjustment is performed based on, for example, the criteria for changing the training plan and the frequency of adjustment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0216] 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.

[0217] [Explanation of symbols]

[0218] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting lifestyle habit data and exercise history data; an analysis unit that analyzes the information input by the input unit; a suggestion unit that proposes a training plan based on the results obtained by the analysis unit; a monitoring unit that monitors the progress of training based on the plan proposed by the proposal unit; an adjustment unit that adjusts the training plan based on the progress obtained by the monitoring unit; A system characterized by:

2. Equipped with a recording section to record training progress 2. The system of claim 1.

3. Equipped with an adjustment unit that adjusts to suit the user's condition 2. The system of claim 1.

4. The analysis unit Analyzes the user's lifestyle and exercise history to propose optimal training plans 2. The system of claim 1.

5. The monitoring unit Data on the number of training sessions, duration, and calories burned is collected and analyzed by AI.

2. The system of claim 1.

6. The adjustment unit Increase training intensity, change exercise type, and adjust to suit your condition 2. The system of claim 1.

7. The input unit Estimates the user's emotions and adjusts the timing of inputting lifestyle habits and exercise history based on the estimated user emotions.

2. The system of claim 1.

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

9. The input unit When entering lifestyle and exercise history, filtering is performed based on the user's current health status and goals.

2. The system of claim 1.

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