System

Abstract

An object of a system according to an embodiment is to analyze information related to sports of a user and provide an individually optimized training menu.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a creation unit, a guidance unit, and a purchase unit. The collection unit collects information related to a sport of a user. The analysis unit analyzes the information collected by the collection unit. The creation unit creates a training menu based on the analysis result obtained by the analysis unit. The guidance unit performs guidance based on the training menu created by the creation unit. The purchase unit supports purchase of the exercise-related goods.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been effective in utilizing information about a user's sport to provide an individually optimized training menu, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze information about a user's sport and provide an individually optimized training menu. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a creation unit, an instruction unit, and a purchase unit. The collection unit collects information related to the user's sport. The analysis unit analyzes the information collected by the collection unit. The creation unit creates a training menu based on the analysis results obtained by the analysis unit. The instruction unit provides instruction based on the training menu created by the creation unit. The purchase unit supports the purchase of exercise-related goods. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about a user's sport and provide an individually optimized training menu. [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 sports training support system according to an embodiment of the present invention collects and analyzes information about a user's sport, creates a training menu, provides instruction, and supports the purchase of sports-related goods. The sports training support system allows users to input information about their sport, and AI analyzes the information to create an appropriate training menu and provide instruction. It also supports the purchase of sports-related goods. For example, in a sports training support system, the user inputs their current abilities, goals, and challenges. The AI ​​then analyzes the input information and analyzes the user's current abilities and challenges. For example, it proposes strength training, endurance training, and practice menus for improving skills. Furthermore, the AI ​​provides instruction to the user based on the created training menu. For example, it provides training methods, points to note, and progress checks. The AI ​​also supports the purchase of sports-related goods. For example, it can recommend equipment, apparel, supplements, and other items necessary for training and complete the purchase process. This allows the sports training support system to provide training tailored to the user's abilities and challenges, helping them improve their sports. The system also supports the purchase of sports-related goods, creating a suitable training environment. This allows the sports training support system to collect and analyze information about the user's sports, create training menus, provide instruction, and support the purchase of sports-related goods. For example, users can receive training tailored to their own abilities and challenges, and aim to improve their sports. In addition, support for purchasing sports-related goods helps create a good training environment.

[0029] A sports training support system according to an embodiment includes a collection unit, an analysis unit, a creation unit, an instruction unit, and a purchase unit. The collection unit collects information about a user's sport. The information about the user's sport includes, but is not limited to, exercise history, health status, and goals. The collection unit collects the user's exercise data using, for example, a sensor. The collection unit can also collect the user's goals and challenges through a questionnaire. The collection unit can also collect the user's training history through an application. For example, the collection unit collects the user's exercise data in real time using a wearable device. The collection unit can also collect the user's goals and challenges through an online questionnaire. The collection unit can also collect the user's training history through a training app. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, but is not limited to, a data analysis method or algorithm. For example, the analysis unit analyzes the user's exercise data using a machine learning algorithm. The analysis unit can also evaluate the user's health condition using statistical analysis. The analysis unit can also analyze the user's training history using data mining technology. For example, the analysis unit can analyze the user's exercise data using a machine learning algorithm and evaluate the effectiveness of the training. The analysis unit can also evaluate the user's health condition using statistical analysis and suggest an appropriate training menu. The analysis unit can also analyze the user's training history using data mining technology and extract effective training methods. The creation unit creates a training menu based on the analysis results obtained by the analysis unit. The training menu is created based on, for example, the type, frequency, intensity, etc. of exercise, but is not limited to such examples. For example, the creation unit creates a strength training menu based on the user's goals. The creation unit can also create an endurance training menu based on the user's challenges. The creation unit can also create a practice menu for improving skills based on the user's training history.For example, the creation unit creates a strength training menu based on the user's goals and suggests specific exercise types and frequency. The creation unit can also create an endurance training menu based on the user's challenges and suggest appropriate exercise intensity and duration. The creation unit can also create a training menu for improving skills based on the user's training history and suggest effective training methods. The instruction unit provides instruction based on the training menu created by the creation unit. Instruction can be provided, for example, through online instruction, face-to-face instruction, feedback methods, etc., but is not limited to these examples. For example, the instruction unit provides the user with training methods and important points through online instruction. The instruction unit can also check the user's training progress through face-to-face instruction. The instruction unit can also evaluate the effectiveness of the user's training through feedback methods. For example, the instruction unit provides the user with training methods and important points through online instruction and provides specific advice. The instruction unit can also check the user's training progress and provide appropriate feedback through face-to-face instruction. The instruction unit can also evaluate the effectiveness of the user's training through feedback methods and suggest areas for improvement. The purchasing unit supports the purchase of exercise-related goods. The purchasing support is provided, for example, based on a product suggestion method and a purchasing procedure flow, but is not limited to such examples. For example, the purchasing department suggests equipment and apparel necessary for training. The purchasing department can also support the purchasing procedure for supplements. The purchasing department can also make suggestions to easily gather items necessary for training. For example, the purchasing department suggests equipment and apparel necessary for training and supports the selection of specific products. The purchasing department can also support the purchasing procedure for supplements and suggest the selection of appropriate products. The purchasing department can also make suggestions to easily gather items necessary for training and support the selection of products that meet the user's needs.As a result, the sports training support system according to the embodiment can collect and analyze information about the user's sport, create a training menu, provide instruction, and support the purchase of sports-related goods. For example, the user can receive training tailored to their own abilities and challenges, and aim to improve their sports. In addition, the purchase of sports-related goods is supported, so a training environment can be created.

[0030] The collection unit can collect information including the user's current sports level, goals, challenges, and past training history. For example, the collection unit collects the user's current sports level. For example, the collection unit collects the sports level by the user selecting one of beginner, intermediate, and advanced. The collection unit can also collect the user's goals. For example, the collection unit collects the goals by the user setting short-term and long-term goals. The collection unit can also collect the user's challenges. For example, the collection unit collects the challenges by the user identifying technical challenges, physical challenges, and mental challenges. The collection unit can also collect the user's past training history. For example, the collection unit collects the training history by recording the type, frequency, intensity, and duration of the user's past exercises. This allows for more accurate analysis and the creation of training menus by collecting detailed sports information about the user. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's input data into a generation AI and cause the generation AI to collect sports information.

[0031] The analysis unit can analyze the user's current abilities and challenges based on the collected information. The analysis unit, for example, analyzes the user's physical strength based on the collected information. For example, the analysis unit analyzes the user's exercise data and evaluates the user's physical strength level. The analysis unit can also analyze the user's technique based on the collected information. For example, the analysis unit analyzes the user's training history and evaluates the degree of improvement in technique. The analysis unit can also analyze the user's mental state based on the collected information. For example, the analysis unit analyzes the user's questionnaire data and evaluates the user's mental state. This makes it possible to accurately analyze the user's abilities and challenges and create an appropriate training menu. 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 collected data into a generation AI and have the generation AI analyze the user's abilities and challenges.

[0032] The creation unit can propose a training menu according to the user's goals and challenges. The creation unit, for example, proposes a training menu according to the user's goals. For example, if the user is aiming to improve muscle strength, the creation unit proposes a muscle strength training menu. The creation unit can also propose a training menu according to the user's challenges. For example, if the user wants to improve endurance, the creation unit proposes an endurance training menu. The creation unit can also propose a training menu based on the user's training history. For example, the creation unit proposes an effective training menu based on the results of the user's past training. This enables effective training by providing a training menu according to the user's goals and challenges. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input data on the user's goals and challenges into the generation AI and have the generation AI suggest a training menu.

[0033] The training unit can provide training methods, points to note, and check the user's progress. The training unit, for example, provides training methods. For example, the training unit instructs the user on correct form and movements. The training unit can also provide training points to note. For example, the training unit instructs the user on injury prevention methods and points to note when training. The training unit can also check the user's training progress. For example, the training unit periodically evaluates the user's training progress and provides feedback. This allows the user to check the training methods, points to note, and progress, thereby improving the user's training effectiveness. Some or all of the above-mentioned processing in the training unit may be performed using, or without, AI. For example, the training unit can input the user's training data into a generation AI and have the generation AI provide instructions on training methods and points to note.

[0034] The purchasing unit can suggest equipment, clothing, and supplements necessary for training and carry out the purchase procedure. The purchasing unit, for example, suggests equipment necessary for training. For example, the purchasing unit suggests dumbbells and barbells necessary for strength training to the user. The purchasing unit can also suggest clothing necessary for training. For example, the purchasing unit suggests appropriate training clothing and shoes to the user. The purchasing unit can also suggest supplements necessary for training. For example, the purchasing unit suggests protein and vitamins to the user. This makes it easy to obtain the items necessary for training. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's training data into a generation AI and have the generation AI suggest appropriate equipment, clothing, and supplements.

[0035] The collection unit can analyze the user's past training history and select the optimal collection method. The collection unit can, for example, analyze the user's past training history. For example, the collection unit can analyze the frequency and intensity of the user's past training. The collection unit can also analyze the user's favorite training menus in the past. For example, the collection unit can analyze the effects of the user's past training and extract effective training methods. The collection unit can also select the optimal collection method based on the user's past training history. For example, the collection unit can determine the appropriate collection timing based on the frequency and intensity of the user's past training. The collection unit can also prioritize collecting the user's favorite training menus in the past. The collection unit can also extract and collect effective training methods from the user's past training history. This enables effective information collection by selecting the optimal collection method based on the user's past training history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's training history data into a generation AI and cause the generation AI to select the optimal collection method.

[0036] When collecting sports information, the collection unit can filter the information based on the user's current lifestyle and areas of interest. The collection unit, for example, takes into account the user's current lifestyle. For example, if the user is currently busy, the collection unit collects effective training information in a short time. The collection unit can also take into account the user's areas of interest. For example, if the user is interested in a particular sport, the collection unit prioritizes collecting information related to that sport. The collection unit can also collect appropriate training information in accordance with the user's lifestyle. For example, if the user is currently busy, the collection unit collects effective training information in a short time, and if the user is interested in a particular sport, the collection unit prioritizes collecting information related to that sport. The collection unit can also collect appropriate training information in accordance with the user's lifestyle. This allows for the provision of more appropriate training information by collecting information according to the user's lifestyle and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's lifestyle data into a generation AI and have the generation AI perform filtering.

[0037] When collecting sports information, the collection unit can select the optimal collection means according to the user's input method. The collection unit selects the collection means according to, for example, the user's input method. For example, when the user uses voice input, the collection unit collects the sports information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect the sports information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect the sports information using image recognition technology. For example, when the user uses voice input, the collection unit collects the sports information using voice recognition technology, and when the user uses text input, the collection unit collects the sports information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect the sports information using image recognition technology. This enables efficient information collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs the user's input data into a generation AI and causes the generation AI to select the optimal collection means.

[0038] When collecting sports information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, considers the user's geographical location information. For example, the collection unit prioritizes collecting information about training that can be done near the user's current location. The collection unit can also collect information about sporting events and facilities specific to a region based on the user's geographical location. The collection unit can also collect training information suitable for weather and seasons based on the user's geographical location. For example, the collection unit prioritizes collecting information about training that can be done near the user's current location and collects information about sporting events and facilities specific to a region based on the user's geographical location. The collection unit can also collect training information suitable for weather and seasons based on the user's geographical location. In this way, by taking the user's geographical location information into account, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data to a generation AI and cause the generation AI to collect highly relevant information.

[0039] When collecting sports information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit collects information on athletes and trainers the user follows on social media. The collection unit can also analyze the user's social media posts to collect related training information. The collection unit can also collect related sports information by referring to the activities of the user's friends on social media. For example, the collection unit collects information on athletes and trainers the user follows on social media, analyzes the user's social media posts, and collects related training information. The collection unit can also collect related sports information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more relevant information can be collected. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0040] When collecting sports information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, reflects the user's past feedback. For example, the collection unit prioritizes collecting training information that the user has previously rated highly. The collection unit can also collect information by excluding training information that the user has previously rated poorly. The collection unit can also optimize the collection method based on the user's past feedback. For example, the collection unit prioritizes collecting training information that the user has previously rated highly and excluding training information that the user has previously rated poorly. The collection unit can also optimize the collection method based on the user's past feedback. This enables more effective information collection by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data to a generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the sports information during analysis. The analysis unit, for example, evaluates the importance of the sports information. For example, the analysis unit evaluates the importance of the sports information based on the user's goals. The analysis unit can also evaluate the importance of the sports information based on the effectiveness of training. For example, the analysis unit evaluates sports information that is directly related to the user's goals as being high in importance. The analysis unit can also evaluate general sports information as being low in importance. The analysis unit can also adjust the level of detail of the analysis based on the importance of the sports information. For example, the analysis unit can perform a detailed analysis of important sports information. The analysis unit can also perform a simplified analysis of general sports information. The analysis unit can also perform a particularly detailed analysis of sports information that is directly related to the user's goals. This enables more effective feedback by performing an analysis according to the importance of the sports information. 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 sports information data to a generation AI and cause the generation AI to evaluate the importance and adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the sport category during analysis. The analysis unit, for example, classifies sport categories. For example, the analysis unit classifies sports into categories such as ball games, track and field, and swimming. The analysis unit can also apply different analysis algorithms depending on the sport category. 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 endurance to endurance training. The analysis unit can also apply an algorithm specialized for technical analysis to practice for improving technique. This enables more appropriate feedback by performing analysis according to the sport category. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sport category data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit references the user's past training data. The analysis unit can also reference the user's past analysis reports. For example, the analysis unit corrects the current analysis results based on the user's past training data. The analysis unit can also extract effective training methods based on the user's past analysis reports and reflect them in the current analysis. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described 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 data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of the sports information. The analysis unit, for example, evaluates the timing of submission of the sports information. For example, the analysis unit evaluates the timing of submission of the sports information based on a submission deadline specified by the user. The analysis unit can also evaluate the timing of submission of the sports information based on the user's training schedule. For example, the analysis unit prioritizes analysis of the most recent sports information. The analysis unit can also determine the priority of analysis based on a submission deadline specified by the user. The analysis unit can also adjust the priority of analysis to match the user's training schedule. This enables more effective feedback by determining the priority of analysis based on the timing of submission of the sports information. Some or all of the above-described 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 data on the timing of submission of the sports information to a generation AI and have the generation AI determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the sports information during analysis. The analysis unit, for example, evaluates the relevance of the sports information. For example, the analysis unit evaluates the relevance of the sports information based on the user's goal. The analysis unit can also evaluate the relevance of the sports information based on the user's current training status. For example, the analysis unit evaluates sports information that is directly related to the user's goal as highly relevant. The analysis unit can also evaluate sports information related to the user's current training status as highly relevant. The analysis unit can also evaluate the relevance of the sports information based on the user's interests and concerns. For example, the analysis unit prioritizes analyzing sports information that is directly related to the user's goal. The analysis unit can also prioritize analyzing sports information related to the user's current training status. The analysis unit can also prioritize analyzing sports information that is highly relevant based on the user's interests and concerns. This enables more effective feedback by adjusting the order of analysis based on the relevance of the sports information. 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 relevant data of sports information to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise by having the user select one of beginner, intermediate, and advanced. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide easy-to-understand analysis results by avoiding technical terms. If the user is an intermediate user, the analysis unit can provide analysis results by using technical terms appropriately. If the user is an advanced user, the analysis unit can provide detailed analysis results by using a lot of technical terms. This enables more appropriate feedback by providing analysis results according to the user's level of expertise. Some or all of the above-described processing in 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 level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] When creating a training menu, the creation unit can adjust the level of menu detail based on the user's goals. The creation unit, for example, evaluates the user's goals. For example, the creation unit evaluates the goals by having the user set one of short-term, long-term, and medium-term goals. The creation unit can also adjust the level of menu detail based on the user's goals. For example, the creation unit can provide a specific and detailed training menu if the user's goal is short-term. The creation unit can also provide a training menu including an overall plan if the user's goal is long-term. The creation unit can also provide a balanced training menu if the user's goal is medium-term. This enables more effective training by providing a training menu based on the user's goals. Some or all of the above-described processing in the creation unit may be performed using AI, for example, or may be performed without using AI. For example, the creation unit can input the user's goal data into a generation AI and cause the generation AI to adjust the level of menu detail.

[0048] When creating a training menu, the creation unit can apply different menu creation algorithms depending on the sport category. The creation unit, for example, classifies sport categories. For example, the creation unit classifies sports into categories such as ball games, track and field, and swimming. The creation unit can also apply different menu creation algorithms depending on the sport category. For example, the creation unit can apply a menu creation algorithm specialized for improving muscle strength to strength training. The creation unit can also apply a menu creation algorithm specialized for improving endurance to endurance training. The creation unit can also apply a menu creation algorithm specialized for improving skill to practice for improving skill. This enables more effective training by providing a training menu according to the sport category. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input sport category data into the generation AI and cause the generation AI to apply the menu creation algorithm.

[0049] When creating a training menu, the creation unit can improve the accuracy of the menu by referring to the user's past training results. The creation unit, for example, refers to the user's past training results. For example, the creation unit references the user's past training data. The creation unit can also reference the user's past training reports. For example, the creation unit creates an effective training menu based on the user's past training data. The creation unit can also extract areas for improvement based on the user's past training reports and reflect them in the menu. The creation unit can also adjust the difficulty of the training menu based on the user's past training results. In this way, by referring to the user's past training results, a more effective training menu can be provided. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's past training data into the generation AI and cause the generation AI to improve the accuracy of the menu.

[0050] When creating a training menu, the creation unit can determine the priority of the menu based on the user's training history. The creation unit, for example, evaluates the user's training history. For example, the creation unit evaluates the effectiveness of the user's past training. The creation unit can also determine the priority of the menu based on the user's training history. For example, the creation unit determines the training menu to be prioritized based on the effectiveness of the user's past training. The creation unit can also prioritize effective training menus based on the user's training history. The creation unit can also optimize the order of the training menus based on the user's training history. This enables more effective training by providing priorities based on the user's training history. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's training history data into a generation AI and have the generation AI determine the priority of the menus.

[0051] When creating a training menu, the creation unit can customize the menu by reflecting user feedback. The creation unit, for example, reflects user feedback. For example, the creation unit may prioritize providing training menus that the user has previously rated highly. The creation unit may also exclude training menus that the user has previously rated poorly. The creation unit may also optimize the menu based on user feedback. For example, the creation unit may prioritize providing training menus that the user has previously rated highly and exclude training menus that the user has previously rated poorly. The creation unit may also optimize the menu based on user feedback. In this way, a more effective training menu can be provided by reflecting user feedback. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit may input user feedback data into a generation AI and cause the generation AI to customize the menu.

[0052] When creating a training menu, the creation unit can adjust the time allocation of the menu according to the user's lifestyle. The creation unit, for example, evaluates the user's lifestyle. For example, the creation unit evaluates the user's work schedule and family situation. The creation unit can also adjust the time allocation of the menu according to the user's lifestyle. For example, the creation unit provides a short and effective training menu when the user is busy. The creation unit can also provide a detailed training menu when the user has time. The creation unit can also optimize the time allocation of the training menu to match the user's lifestyle. This enables more effective training by providing a training menu that matches the user's lifestyle. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's lifestyle data into the generation AI and cause the generation AI to adjust the time allocation of the menu.

[0053] When providing instruction, the instruction unit can select the optimal instruction method by referring to the user's past training history. The instruction unit, for example, refers to the user's past training history. For example, the instruction unit can refer to the user's past training data. The instruction unit can also refer to the user's past training reports. For example, the instruction unit selects an effective instruction method based on the user's past training data. The instruction unit can also extract areas for improvement based on the user's past training reports and reflect them in the instruction. The instruction unit can also optimize the instruction method based on the user's past training history. This enables more effective training by providing the optimal instruction method based on the user's past training history. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's training history data into a generation AI and have the generation AI select the optimal instruction method.

[0054] During training, the training unit can customize the training method based on the user's current living situation. The training unit, for example, evaluates the user's current living situation. For example, the training unit evaluates the user's work schedule and family situation. The training unit can also customize the training method based on the user's lifestyle. For example, if the user is busy, the training unit can provide a short and effective training method. If the user has time, the training unit can provide a detailed training method. The training unit can also optimize the training method to match the user's lifestyle. This enables more effective training by providing a training method that suits the user's lifestyle. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the user's living situation data into the generation AI and have the generation AI customize the training method.

[0055] The teaching unit can improve the teaching method by reflecting user feedback during teaching. The teaching unit, for example, reflects user feedback. For example, the teaching unit may prioritize providing teaching methods that users have previously rated highly. The teaching unit may also exclude teaching methods that users have previously rated poorly. The teaching unit may also optimize the teaching method based on user feedback. For example, the teaching unit may prioritize providing teaching methods that users have previously rated highly and exclude teaching methods that users have previously rated poorly. The teaching unit may also optimize the teaching method based on user feedback. In this way, by reflecting user feedback, a more effective teaching method can be provided. Some or all of the above-described processing in the teaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the teaching unit may input user feedback data into a generation AI and cause the generation AI to improve the teaching method.

[0056] When providing instruction, the instruction unit can select the optimal instruction method by taking into account the user's geographical location information. The instruction unit, for example, considers the user's geographical location information. For example, the instruction unit may prioritize providing instruction methods that can be performed near the user's current location. The instruction unit may also provide information on sporting events and facilities specific to the region based on the user's geographical location. The instruction unit may also provide instruction methods suitable for the weather and season based on the user's geographical location. For example, the instruction unit may prioritize providing instruction methods that can be performed near the user's current location and provide information on sporting events and facilities specific to the region based on the user's geographical location. The instruction unit may also provide instruction methods suitable for the weather and season based on the user's geographical location. In this way, by taking the user's geographical location information into account, more effective instruction methods can be provided. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may input the user's geographical location data into a generation AI and cause the generation AI to select the optimal instruction method.

[0057] When providing instruction, the instruction unit can analyze the user's social media activity and suggest instruction methods. The instruction unit, for example, analyzes the user's social media activity. For example, the instruction unit suggests instruction methods from athletes or trainers the user follows on social media. The instruction unit can also analyze the user's social media posts and suggest related instruction methods. The instruction unit can also suggest related instruction methods by referring to the activities of the user's friends on social media. For example, the instruction unit suggests instruction methods from athletes or trainers the user follows on social media, analyzes the user's social media posts, and suggests related instruction methods. The instruction unit can also suggest related instruction methods by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant instruction methods can be provided. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's social media data into a generation AI and have the generation AI suggest instruction methods.

[0058] When providing instruction, the instruction unit can customize the instruction method by reflecting the user's past feedback. The instruction unit, for example, reflects the user's past feedback. For example, the instruction unit may prioritize and provide instruction methods that the user has previously rated highly. The instruction unit may also exclude instruction methods that the user has previously rated poorly. The instruction unit may also optimize the instruction method based on the user's past feedback. For example, the instruction unit may prioritize and provide instruction methods that the user has previously rated highly, and exclude instruction methods that the user has previously rated poorly. The instruction unit may also optimize the instruction method based on the user's past feedback. In this way, by reflecting the user's past feedback, a more effective instruction method can be provided. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may input the user's feedback data into a generation AI and cause the generation AI to customize the instruction method.

[0059] When making a purchase suggestion, the purchasing unit can provide optimal suggestions by referring to the user's past purchase history. The purchasing unit, for example, refers to the user's past purchase history. For example, the purchasing unit refers to products the user has purchased in the past. The purchasing unit can also refer to the user's past purchase frequency. For example, the purchasing unit can suggest related products based on the user's past purchase history. The purchasing unit can also suggest products that match the user's preferences and needs based on the user's past purchase history. The purchasing unit can also make suggestions that take into account the user's purchase frequency and timing based on the user's past purchase history. This enables more effective purchasing support by providing optimal suggestions based on the user's past purchase history. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's purchase history data into a generation AI and have the generation AI provide optimal suggestions.

[0060] When making a purchase suggestion, the purchasing unit can customize the suggestion content based on the user's current training status. The purchasing unit, for example, evaluates the user's current training status. For example, the purchasing unit evaluates the type, frequency, and intensity of the user's exercise. The purchasing unit can also customize the suggestion content according to the user's training progress. For example, the purchasing unit can recommend necessary equipment and apparel based on the user's current training status. The purchasing unit can also recommend appropriate supplements according to the user's training progress. The purchasing unit can also recommend effective training goods tailored to the user's training goals. This enables more effective purchasing support by providing suggestions based on the user's training status. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without AI. For example, the purchasing unit can input the user's training status data into a generation AI and have the generation AI customize the suggestion content.

[0061] The purchasing unit can reflect user feedback when making purchase suggestions to improve the suggestion method. The purchasing unit, for example, reflects user feedback. For example, the purchasing unit prioritizes suggesting products that the user has previously rated highly. The purchasing unit can also exclude products that the user has previously rated poorly. The purchasing unit can also optimize the suggestion method based on user feedback. For example, the purchasing unit prioritizes suggesting products that the user has previously rated highly and excludes products that the user has previously rated poorly. The purchasing unit can also optimize the suggestion method based on user feedback. In this way, more effective purchase suggestions can be provided by reflecting user feedback. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input user feedback data into a generation AI and cause the generation AI to improve the suggestion method.

[0062] When making purchase suggestions, the purchasing unit can provide optimal suggestions by taking into account the user's geographical location information. The purchasing unit, for example, considers the user's geographical location information. For example, the purchasing unit prioritizes suggesting products that can be purchased near the user's current location. The purchasing unit can also suggest products and services that are unique to the region based on the user's geographical location. The purchasing unit can also suggest products that are suitable for the weather or season based on the user's geographical location. For example, the purchasing unit prioritizes suggesting products that can be purchased near the user's current location and suggests products and services that are unique to the region based on the user's geographical location. The purchasing unit can also suggest products that are suitable for the weather or season based on the user's geographical location. This makes it possible to provide more effective purchase suggestions by taking into account the user's geographical location information. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's geographical location data into a generation AI and cause the generation AI to provide optimal suggestions.

[0063] When making a purchase suggestion, the purchasing unit can analyze the user's social media activity and suggest related products. The purchasing unit, for example, analyzes the user's social media activity. For example, the purchasing unit suggests information about brands and products that the user follows on social media. The purchasing unit can also analyze the user's social media posts and suggest related products. The purchasing unit can also suggest related products by referring to the activities of the user's friends on social media. For example, the purchasing unit suggests information about brands and products that the user follows on social media, analyzes the user's social media posts, and suggests related products. The purchasing unit can also suggest related products by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant products can be suggested. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit may input the user's social media data into a generation AI and have the generation AI suggest related products.

[0064] When making a purchase suggestion, the purchasing unit can customize the suggestion method by reflecting the user's past feedback. The purchasing unit, for example, reflects the user's past feedback. For example, the purchasing unit may prioritize suggesting products that the user has previously rated highly. The purchasing unit may also exclude products that the user has previously rated poorly. The purchasing unit may also optimize the suggestion method based on the user's past feedback. For example, the purchasing unit may prioritize suggesting products that the user has previously rated highly and exclude products that the user has previously rated poorly. The purchasing unit may also optimize the suggestion method based on the user's past feedback. This allows for more effective purchase suggestions to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit may input user feedback data into a generation AI and have the generation AI customize the suggestion method.

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

[0066] When collecting information about the user's sports, the collection unit can also collect the user's diet and nutritional intake status. For example, the collection unit evaluates nutritional balance by having the user input the details of their daily meals. The collection unit can also record the types and amounts of supplements the user takes. Furthermore, the collection unit can suggest meal menus suitable for training based on the user's dietary history. This makes it possible to create training menus that take into account the user's diet and nutritional intake status.

[0067] The analysis unit can analyze the user's sleep patterns based on the collected information. For example, the analysis unit can evaluate the user's sleep duration and quality. The analysis unit can also suggest optimal sleep duration to maximize the effectiveness of training based on the user's sleep data. Furthermore, the analysis unit can correlate and analyze the user's sleep patterns with the results of training and suggest an effective training schedule. This makes it possible to create a training menu that takes the user's sleep patterns into consideration.

[0068] The instructor can monitor the user's training progress in real time and adjust the training menu as needed. For example, the instructor can collect the user's exercise data in real time and evaluate the user's progress. The instructor can also adjust the user's training menu in real time to provide effective training. Furthermore, the instructor can provide feedback according to the user's training progress. This makes it possible to monitor the user's training progress in real time and provide effective training.

[0069] The purchasing department can suggest equipment and apparel necessary for training based on the user's training progress. For example, the purchasing department can evaluate the user's training progress and suggest the necessary equipment and apparel. The purchasing department can also suggest appropriate supplements based on the user's training progress. Furthermore, the purchasing department can suggest effective training goods that match the user's training goals. This allows for more effective purchasing support by providing suggestions based on the user's training progress.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The collection unit collects information about the user's sport. The information about the user's sport includes, for example, exercise history, health status, and goals. The collection unit collects the user's exercise data using sensors and collects the user's goals and challenges through a questionnaire. The collection unit can also collect the user's training history through an application. For example, the collection unit can collect the user's exercise data in real time using a wearable device and collect the user's goals and challenges through an online questionnaire. The collection unit can also collect the user's training history through a training app. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data analysis techniques and algorithms. For example, machine learning algorithms can be used to analyze the user's exercise data, and statistical analysis can be used to evaluate the user's health condition. Data mining techniques can also be used to analyze the user's training history. This allows the effectiveness of training to be evaluated and an appropriate training menu to be proposed. Step 3: The creation unit creates a training menu based on the analysis results obtained by the analysis unit. The training menu is created based on the type, frequency, intensity, etc. of exercise. For example, a strength training menu is created based on the user's goals, and an endurance training menu is created based on the user's challenges. It is also possible to create a training menu for improving skills based on the user's training history. Step 4: The instructor provides instruction based on the training menu created by the creator. The instruction is provided through online instruction, face-to-face instruction, feedback methods, etc. For example, the instructor can instruct the user on training methods and important points through online instruction, and check the user's training progress through face-to-face instruction. The instructor can also use feedback methods to evaluate the effectiveness of the training and suggest areas for improvement. Step 5: The purchasing department provides support for purchasing exercise-related goods. Purchasing support is based on product recommendation methods and the flow of the purchasing process. For example, they recommend the equipment and clothing needed for training and support the purchasing process for supplements. They can also make suggestions to easily gather the items needed for training and support the selection of products that meet the user's needs.

[0072] (Example 2) A sports training support system according to an embodiment of the present invention collects and analyzes information about a user's sport, creates a training menu, provides instruction, and supports the purchase of sports-related goods. The sports training support system allows users to input information about their sport, and AI analyzes the information to create an appropriate training menu and provide instruction. It also supports the purchase of sports-related goods. For example, in a sports training support system, the user inputs their current abilities, goals, and challenges. The AI ​​then analyzes the input information and analyzes the user's current abilities and challenges. For example, it proposes strength training, endurance training, and practice menus for improving skills. Furthermore, the AI ​​provides instruction to the user based on the created training menu. For example, it provides training methods, points to note, and progress checks. The AI ​​also supports the purchase of sports-related goods. For example, it can recommend equipment, apparel, supplements, and other items necessary for training and complete the purchase process. This allows the sports training support system to provide training tailored to the user's abilities and challenges, helping them improve their sports. The system also supports the purchase of sports-related goods, creating a suitable training environment. This allows the sports training support system to collect and analyze information about the user's sports, create training menus, provide instruction, and support the purchase of sports-related goods. For example, users can receive training tailored to their own abilities and challenges, and aim to improve their sports. In addition, support for purchasing sports-related goods helps create a good training environment.

[0073] A sports training support system according to an embodiment includes a collection unit, an analysis unit, a creation unit, an instruction unit, and a purchase unit. The collection unit collects information about a user's sport. The information about the user's sport includes, but is not limited to, exercise history, health status, and goals. The collection unit collects the user's exercise data using, for example, a sensor. The collection unit can also collect the user's goals and challenges through a questionnaire. The collection unit can also collect the user's training history through an application. For example, the collection unit collects the user's exercise data in real time using a wearable device. The collection unit can also collect the user's goals and challenges through an online questionnaire. The collection unit can also collect the user's training history through a training app. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, but is not limited to, a data analysis method or algorithm. For example, the analysis unit analyzes the user's exercise data using a machine learning algorithm. The analysis unit can also evaluate the user's health condition using statistical analysis. The analysis unit can also analyze the user's training history using data mining technology. For example, the analysis unit can analyze the user's exercise data using a machine learning algorithm and evaluate the effectiveness of the training. The analysis unit can also evaluate the user's health condition using statistical analysis and suggest an appropriate training menu. The analysis unit can also analyze the user's training history using data mining technology and extract effective training methods. The creation unit creates a training menu based on the analysis results obtained by the analysis unit. The training menu is created based on, for example, the type, frequency, intensity, etc. of exercise, but is not limited to such examples. For example, the creation unit creates a strength training menu based on the user's goals. The creation unit can also create an endurance training menu based on the user's challenges. The creation unit can also create a practice menu for improving skills based on the user's training history.For example, the creation unit creates a strength training menu based on the user's goals and suggests specific exercise types and frequency. The creation unit can also create an endurance training menu based on the user's challenges and suggest appropriate exercise intensity and duration. The creation unit can also create a training menu for improving skills based on the user's training history and suggest effective training methods. The instruction unit provides instruction based on the training menu created by the creation unit. Instruction can be provided, for example, through online instruction, face-to-face instruction, feedback methods, etc., but is not limited to these examples. For example, the instruction unit provides the user with training methods and important points through online instruction. The instruction unit can also check the user's training progress through face-to-face instruction. The instruction unit can also evaluate the effectiveness of the user's training through feedback methods. For example, the instruction unit provides the user with training methods and important points through online instruction and provides specific advice. The instruction unit can also check the user's training progress and provide appropriate feedback through face-to-face instruction. The instruction unit can also evaluate the effectiveness of the user's training through feedback methods and suggest areas for improvement. The purchasing unit supports the purchase of exercise-related goods. The purchasing support is provided, for example, based on a product suggestion method and a purchasing procedure flow, but is not limited to such examples. For example, the purchasing department suggests equipment and apparel necessary for training. The purchasing department can also support the purchasing procedure for supplements. The purchasing department can also make suggestions to easily gather items necessary for training. For example, the purchasing department suggests equipment and apparel necessary for training and supports the selection of specific products. The purchasing department can also support the purchasing procedure for supplements and suggest the selection of appropriate products. The purchasing department can also make suggestions to easily gather items necessary for training and support the selection of products that meet the user's needs.As a result, the sports training support system according to the embodiment can collect and analyze information about the user's sport, create a training menu, provide instruction, and support the purchase of sports-related goods. For example, the user can receive training tailored to their own abilities and challenges, and aim to improve their sports. In addition, the purchase of sports-related goods is supported, so a training environment can be created.

[0074] The collection unit can collect information including the user's current sports level, goals, challenges, and past training history. For example, the collection unit collects the user's current sports level. For example, the collection unit collects the sports level by the user selecting one of beginner, intermediate, and advanced. The collection unit can also collect the user's goals. For example, the collection unit collects the goals by the user setting short-term and long-term goals. The collection unit can also collect the user's challenges. For example, the collection unit collects the challenges by the user identifying technical challenges, physical challenges, and mental challenges. The collection unit can also collect the user's past training history. For example, the collection unit collects the training history by recording the type, frequency, intensity, and duration of the user's past exercises. This allows for more accurate analysis and the creation of training menus by collecting detailed sports information about the user. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's input data into a generation AI and cause the generation AI to collect sports information.

[0075] The analysis unit can analyze the user's current abilities and challenges based on the collected information. The analysis unit, for example, analyzes the user's physical strength based on the collected information. For example, the analysis unit analyzes the user's exercise data and evaluates the user's physical strength level. The analysis unit can also analyze the user's technique based on the collected information. For example, the analysis unit analyzes the user's training history and evaluates the degree of improvement in technique. The analysis unit can also analyze the user's mental state based on the collected information. For example, the analysis unit analyzes the user's questionnaire data and evaluates the user's mental state. This makes it possible to accurately analyze the user's abilities and challenges and create an appropriate training menu. 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 collected data into a generation AI and have the generation AI analyze the user's abilities and challenges.

[0076] The creation unit can propose a training menu according to the user's goals and challenges. The creation unit, for example, proposes a training menu according to the user's goals. For example, if the user is aiming to improve muscle strength, the creation unit proposes a muscle strength training menu. The creation unit can also propose a training menu according to the user's challenges. For example, if the user wants to improve endurance, the creation unit proposes an endurance training menu. The creation unit can also propose a training menu based on the user's training history. For example, the creation unit proposes an effective training menu based on the results of the user's past training. This enables effective training by providing a training menu according to the user's goals and challenges. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input data on the user's goals and challenges into the generation AI and have the generation AI suggest a training menu.

[0077] The training unit can provide training methods, points to note, and check the user's progress. The training unit, for example, provides training methods. For example, the training unit instructs the user on correct form and movements. The training unit can also provide training points to note. For example, the training unit instructs the user on injury prevention methods and points to note when training. The training unit can also check the user's training progress. For example, the training unit periodically evaluates the user's training progress and provides feedback. This allows the user to check the training methods, points to note, and progress, thereby improving the user's training effectiveness. Some or all of the above-mentioned processing in the training unit may be performed using, or without, AI. For example, the training unit can input the user's training data into a generation AI and have the generation AI provide instructions on training methods and points to note.

[0078] The purchasing unit can suggest equipment, clothing, and supplements necessary for training and carry out the purchase procedure. The purchasing unit, for example, suggests equipment necessary for training. For example, the purchasing unit suggests dumbbells and barbells necessary for strength training to the user. The purchasing unit can also suggest clothing necessary for training. For example, the purchasing unit suggests appropriate training clothing and shoes to the user. The purchasing unit can also suggest supplements necessary for training. For example, the purchasing unit suggests protein and vitamins to the user. This makes it easy to obtain the items necessary for training. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's training data into a generation AI and have the generation AI suggest appropriate equipment, clothing, and supplements.

[0079] The collection unit can estimate the user's emotions and adjust the timing of collecting sports information based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The collection unit can also adjust the timing of collecting sports information based on the user's emotions. For example, if the user is feeling stressed, the collection unit can collect sports information during a time when the user can relax. If the user is highly motivated, the collection unit can collect sports information immediately so that the user can start training. If the user is tired, the collection unit can collect sports information after a break. This allows the sports information to be collected at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0080] The collection unit can analyze the user's past training history and select the optimal collection method. The collection unit can, for example, analyze the user's past training history. For example, the collection unit can analyze the frequency and intensity of the user's past training. The collection unit can also analyze the user's favorite training menus in the past. For example, the collection unit can analyze the effects of the user's past training and extract effective training methods. The collection unit can also select the optimal collection method based on the user's past training history. For example, the collection unit can determine the appropriate collection timing based on the frequency and intensity of the user's past training. The collection unit can also prioritize collecting the user's favorite training menus in the past. The collection unit can also extract and collect effective training methods from the user's past training history. This enables effective information collection by selecting the optimal collection method based on the user's past training history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's training history data into a generation AI and cause the generation AI to select the optimal collection method.

[0081] When collecting sports information, the collection unit can filter the information based on the user's current lifestyle and areas of interest. The collection unit, for example, takes into account the user's current lifestyle. For example, if the user is currently busy, the collection unit collects effective training information in a short time. The collection unit can also take into account the user's areas of interest. For example, if the user is interested in a particular sport, the collection unit prioritizes collecting information related to that sport. The collection unit can also collect appropriate training information in accordance with the user's lifestyle. For example, if the user is currently busy, the collection unit collects effective training information in a short time, and if the user is interested in a particular sport, the collection unit prioritizes collecting information related to that sport. The collection unit can also collect appropriate training information in accordance with the user's lifestyle. This allows for the provision of more appropriate training information by collecting information according to the user's lifestyle and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's lifestyle data into a generation AI and have the generation AI perform filtering.

[0082] When collecting sports information, the collection unit can select the optimal collection means according to the user's input method. The collection unit selects the collection means according to, for example, the user's input method. For example, when the user uses voice input, the collection unit collects the sports information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect the sports information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect the sports information using image recognition technology. For example, when the user uses voice input, the collection unit collects the sports information using voice recognition technology, and when the user uses text input, the collection unit collects the sports information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect the sports information using image recognition technology. This enables efficient information collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs the user's input data into a generation AI and causes the generation AI to select the optimal collection means.

[0083] The collection unit can estimate the user's emotions and determine the priority of sports information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The collection unit can also determine the priority of sports information to be collected based on the user's emotions. For example, if the user is low in motivation, the collection unit can prioritize collecting easy training information that provides a sense of accomplishment. If the user is excited, the collection unit can prioritize collecting challenging training information. If the user is relaxed, the collection unit can prioritize collecting training information that has a relaxing effect. This enables more effective information collection by determining the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0084] When collecting sports information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, considers the user's geographical location information. For example, the collection unit prioritizes collecting information about training that can be done near the user's current location. The collection unit can also collect information about sporting events and facilities specific to a region based on the user's geographical location. The collection unit can also collect training information suitable for weather and seasons based on the user's geographical location. For example, the collection unit prioritizes collecting information about training that can be done near the user's current location and collects information about sporting events and facilities specific to a region based on the user's geographical location. The collection unit can also collect training information suitable for weather and seasons based on the user's geographical location. In this way, by taking the user's geographical location information into account, more relevant information can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data to a generation AI and cause the generation AI to collect highly relevant information.

[0085] When collecting sports information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit collects information on athletes and trainers the user follows on social media. The collection unit can also analyze the user's social media posts to collect related training information. The collection unit can also collect related sports information by referring to the activities of the user's friends on social media. For example, the collection unit collects information on athletes and trainers the user follows on social media, analyzes the user's social media posts, and collects related training information. The collection unit can also collect related sports information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more relevant information can be collected. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0086] When collecting sports information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, reflects the user's past feedback. For example, the collection unit prioritizes collecting training information that the user has previously rated highly. The collection unit can also collect information by excluding training information that the user has previously rated poorly. The collection unit can also optimize the collection method based on the user's past feedback. For example, the collection unit prioritizes collecting training information that the user has previously rated highly and excluding training information that the user has previously rated poorly. The collection unit can also optimize the collection method based on the user's past feedback. This enables more effective information collection by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data to a generation AI and cause the generation AI to customize the collection method.

[0087] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The analysis unit can also adjust the way the analysis is presented based on the user's emotions. For example, the analysis unit can provide simple, highly visible analysis results when the user is nervous. The analysis unit can also provide detailed analysis results when the user is relaxed. The analysis unit can also provide analysis results that focus on the main points when the user is in a hurry. This enables more appropriate feedback by providing analysis results that correspond to the user's emotions. 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the sports information during analysis. The analysis unit, for example, evaluates the importance of the sports information. For example, the analysis unit evaluates the importance of the sports information based on the user's goals. The analysis unit can also evaluate the importance of the sports information based on the effectiveness of training. For example, the analysis unit evaluates sports information that is directly related to the user's goals as being high in importance. The analysis unit can also evaluate general sports information as being low in importance. The analysis unit can also adjust the level of detail of the analysis based on the importance of the sports information. For example, the analysis unit can perform a detailed analysis of important sports information. The analysis unit can also perform a simplified analysis of general sports information. The analysis unit can also perform a particularly detailed analysis of sports information that is directly related to the user's goals. This enables more effective feedback by performing an analysis according to the importance of the sports information. 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 sports information data to a generation AI and cause the generation AI to evaluate the importance and adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the sport category during analysis. The analysis unit, for example, classifies sport categories. For example, the analysis unit classifies sports into categories such as ball games, track and field, and swimming. The analysis unit can also apply different analysis algorithms depending on the sport category. 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 endurance to endurance training. The analysis unit can also apply an algorithm specialized for technical analysis to practice for improving technique. This enables more appropriate feedback by performing analysis according to the sport category. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sport category data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit references the user's past training data. The analysis unit can also reference the user's past analysis reports. For example, the analysis unit corrects the current analysis results based on the user's past training data. The analysis unit can also extract effective training methods based on the user's past analysis reports and reflect them in the current analysis. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described 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 data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can estimate the user's emotion by analyzing the tone and speed of the user's voice. The analysis unit can also adjust the length of the analysis based on the user's emotion. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide an analysis result with visually stimulating effects if the user is excited. This allows for more appropriate feedback by adjusting the length of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of the sports information. The analysis unit, for example, evaluates the timing of submission of the sports information. For example, the analysis unit evaluates the timing of submission of the sports information based on a submission deadline specified by the user. The analysis unit can also evaluate the timing of submission of the sports information based on the user's training schedule. For example, the analysis unit prioritizes analysis of the most recent sports information. The analysis unit can also determine the priority of analysis based on a submission deadline specified by the user. The analysis unit can also adjust the priority of analysis to match the user's training schedule. This enables more effective feedback by determining the priority of analysis based on the timing of submission of the sports information. Some or all of the above-described 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 data on the timing of submission of the sports information to a generation AI and have the generation AI determine the priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the sports information during analysis. The analysis unit, for example, evaluates the relevance of the sports information. For example, the analysis unit evaluates the relevance of the sports information based on the user's goal. The analysis unit can also evaluate the relevance of the sports information based on the user's current training status. For example, the analysis unit evaluates sports information that is directly related to the user's goal as highly relevant. The analysis unit can also evaluate sports information related to the user's current training status as highly relevant. The analysis unit can also evaluate the relevance of the sports information based on the user's interests and concerns. For example, the analysis unit prioritizes analyzing sports information that is directly related to the user's goal. The analysis unit can also prioritize analyzing sports information related to the user's current training status. The analysis unit can also prioritize analyzing sports information that is highly relevant based on the user's interests and concerns. This enables more effective feedback by adjusting the order of analysis based on the relevance of the sports information. 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 relevant data of sports information to the generation AI and cause the generation AI to adjust the order of analysis.

[0094] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise by having the user select one of beginner, intermediate, and advanced. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide easy-to-understand analysis results by avoiding technical terms. If the user is an intermediate user, the analysis unit can provide analysis results by using technical terms appropriately. If the user is an advanced user, the analysis unit can provide detailed analysis results by using a lot of technical terms. This enables more appropriate feedback by providing analysis results according to the user's level of expertise. Some or all of the above-described processing in 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 level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0095] The creation unit can estimate the user's emotions and adjust the presentation method of the training menu based on the estimated user's emotions. The creation unit, for example, estimates the user's emotions. For example, the creation unit can estimate the user's emotions using facial expression recognition technology. The creation unit can also estimate the user's emotions using voice analysis technology. For example, the creation unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The creation unit can also adjust the presentation method of the training menu based on the user's emotions. For example, the creation unit can provide a simple and highly visible training menu when the user is nervous. The creation unit can also provide a detailed training menu when the user is relaxed. The creation unit can also provide a training menu that focuses on the main points when the user is in a hurry. This enables more effective training by providing a presentation method of the training menu that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0096] When creating a training menu, the creation unit can adjust the level of menu detail based on the user's goals. The creation unit, for example, evaluates the user's goals. For example, the creation unit evaluates the goals by having the user set one of short-term, long-term, and medium-term goals. The creation unit can also adjust the level of menu detail based on the user's goals. For example, the creation unit can provide a specific and detailed training menu if the user's goal is short-term. The creation unit can also provide a training menu including an overall plan if the user's goal is long-term. The creation unit can also provide a balanced training menu if the user's goal is medium-term. This enables more effective training by providing a training menu based on the user's goals. Some or all of the above-described processing in the creation unit may be performed using AI, for example, or may be performed without using AI. For example, the creation unit can input the user's goal data into a generation AI and cause the generation AI to adjust the level of menu detail.

[0097] When creating a training menu, the creation unit can apply different menu creation algorithms depending on the sport category. The creation unit, for example, classifies sport categories. For example, the creation unit classifies sports into categories such as ball games, track and field, and swimming. The creation unit can also apply different menu creation algorithms depending on the sport category. For example, the creation unit can apply a menu creation algorithm specialized for improving muscle strength to strength training. The creation unit can also apply a menu creation algorithm specialized for improving endurance to endurance training. The creation unit can also apply a menu creation algorithm specialized for improving skill to practice for improving skill. This enables more effective training by providing a training menu according to the sport category. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input sport category data into the generation AI and cause the generation AI to apply the menu creation algorithm.

[0098] When creating a training menu, the creation unit can improve the accuracy of the menu by referring to the user's past training results. The creation unit, for example, refers to the user's past training results. For example, the creation unit references the user's past training data. The creation unit can also reference the user's past training reports. For example, the creation unit creates an effective training menu based on the user's past training data. The creation unit can also extract areas for improvement based on the user's past training reports and reflect them in the menu. The creation unit can also adjust the difficulty of the training menu based on the user's past training results. In this way, by referring to the user's past training results, a more effective training menu can be provided. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's past training data into the generation AI and cause the generation AI to improve the accuracy of the menu.

[0099] The creation unit can estimate the user's emotions and adjust the length of the training menu based on the estimated user's emotions. The creation unit, for example, estimates the user's emotions. For example, the creation unit can estimate the user's emotions using facial expression recognition technology. The creation unit can also estimate the user's emotions using voice analysis technology. For example, the creation unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The creation unit can also adjust the length of the training menu based on the user's emotions. For example, the creation unit can provide a short and effective training menu if the user is in a hurry. The creation unit can also provide a detailed training menu if the user is relaxed. The creation unit can also provide a training menu with visually stimulating effects if the user is excited. This enables more effective training by providing a training menu length that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0100] When creating a training menu, the creation unit can determine the priority of the menu based on the user's training history. The creation unit, for example, evaluates the user's training history. For example, the creation unit evaluates the effectiveness of the user's past training. The creation unit can also determine the priority of the menu based on the user's training history. For example, the creation unit determines the training menu to be prioritized based on the effectiveness of the user's past training. The creation unit can also prioritize effective training menus based on the user's training history. The creation unit can also optimize the order of the training menus based on the user's training history. This enables more effective training by providing priorities based on the user's training history. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's training history data into a generation AI and have the generation AI determine the priority of the menus.

[0101] When creating a training menu, the creation unit can customize the menu by reflecting user feedback. The creation unit, for example, reflects user feedback. For example, the creation unit may prioritize providing training menus that the user has previously rated highly. The creation unit may also exclude training menus that the user has previously rated poorly. The creation unit may also optimize the menu based on user feedback. For example, the creation unit may prioritize providing training menus that the user has previously rated highly and exclude training menus that the user has previously rated poorly. The creation unit may also optimize the menu based on user feedback. In this way, a more effective training menu can be provided by reflecting user feedback. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit may input user feedback data into a generation AI and cause the generation AI to customize the menu.

[0102] When creating a training menu, the creation unit can adjust the time allocation of the menu according to the user's lifestyle. The creation unit, for example, evaluates the user's lifestyle. For example, the creation unit evaluates the user's work schedule and family situation. The creation unit can also adjust the time allocation of the menu according to the user's lifestyle. For example, the creation unit provides a short and effective training menu when the user is busy. The creation unit can also provide a detailed training menu when the user has time. The creation unit can also optimize the time allocation of the training menu to match the user's lifestyle. This enables more effective training by providing a training menu that matches the user's lifestyle. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's lifestyle data into the generation AI and cause the generation AI to adjust the time allocation of the menu.

[0103] The teaching unit can estimate the user's emotions and adjust the teaching method based on the estimated user's emotions. The teaching unit, for example, estimates the user's emotions. For example, the teaching unit can estimate the user's emotions using facial expression recognition technology. The teaching unit can also estimate the user's emotions using voice analysis technology. For example, the teaching unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The teaching unit can also adjust the teaching method based on the user's emotions. For example, if the user is nervous, the teaching unit can provide a teaching method that helps the user relax. If the user is relaxed, the teaching unit can provide a detailed teaching method. If the user is in a hurry, the teaching unit can provide a teaching method that focuses on the key points. This enables more effective training by providing a teaching method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instructor may be performed using AI, or may be performed without AI. For example, the instructor may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0104] When providing instruction, the instruction unit can select the optimal instruction method by referring to the user's past training history. The instruction unit, for example, refers to the user's past training history. For example, the instruction unit can refer to the user's past training data. The instruction unit can also refer to the user's past training reports. For example, the instruction unit selects an effective instruction method based on the user's past training data. The instruction unit can also extract areas for improvement based on the user's past training reports and reflect them in the instruction. The instruction unit can also optimize the instruction method based on the user's past training history. This enables more effective training by providing the optimal instruction method based on the user's past training history. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's training history data into a generation AI and have the generation AI select the optimal instruction method.

[0105] During training, the training unit can customize the training method based on the user's current living situation. The training unit, for example, evaluates the user's current living situation. For example, the training unit evaluates the user's work schedule and family situation. The training unit can also customize the training method based on the user's lifestyle. For example, if the user is busy, the training unit can provide a short and effective training method. If the user has time, the training unit can provide a detailed training method. The training unit can also optimize the training method to match the user's lifestyle. This enables more effective training by providing a training method that suits the user's lifestyle. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the user's living situation data into the generation AI and have the generation AI customize the training method.

[0106] The teaching unit can improve the teaching method by reflecting user feedback during teaching. The teaching unit, for example, reflects user feedback. For example, the teaching unit may prioritize providing teaching methods that users have previously rated highly. The teaching unit may also exclude teaching methods that users have previously rated poorly. The teaching unit may also optimize the teaching method based on user feedback. For example, the teaching unit may prioritize providing teaching methods that users have previously rated highly and exclude teaching methods that users have previously rated poorly. The teaching unit may also optimize the teaching method based on user feedback. In this way, by reflecting user feedback, a more effective teaching method can be provided. Some or all of the above-described processing in the teaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the teaching unit may input user feedback data into a generation AI and cause the generation AI to improve the teaching method.

[0107] The instruction unit can estimate the user's emotions and determine the priority of instruction based on the estimated user's emotions. The instruction unit, for example, estimates the user's emotions. For example, the instruction unit can estimate the user's emotions using facial expression recognition technology. The instruction unit can also estimate the user's emotions using voice analysis technology. For example, the instruction unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The instruction unit can also determine the priority of instruction based on the user's emotions. For example, if the user is low in motivation, the instruction unit can prioritize providing an easy instruction method that gives a sense of accomplishment. If the user is excited, the instruction unit can prioritize providing a challenging instruction method. If the user is relaxed, the instruction unit can prioritize providing an instruction method that has a relaxing effect. This enables more effective training by prioritizing instruction 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 instructor may be performed using AI, or may be performed without AI. For example, the instructor may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0108] When providing instruction, the instruction unit can select the optimal instruction method by taking into account the user's geographical location information. The instruction unit, for example, considers the user's geographical location information. For example, the instruction unit may prioritize providing instruction methods that can be performed near the user's current location. The instruction unit may also provide information on sporting events and facilities specific to the region based on the user's geographical location. The instruction unit may also provide instruction methods suitable for the weather and season based on the user's geographical location. For example, the instruction unit may prioritize providing instruction methods that can be performed near the user's current location and provide information on sporting events and facilities specific to the region based on the user's geographical location. The instruction unit may also provide instruction methods suitable for the weather and season based on the user's geographical location. In this way, by taking the user's geographical location information into account, more effective instruction methods can be provided. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may input the user's geographical location data into a generation AI and cause the generation AI to select the optimal instruction method.

[0109] When providing instruction, the instruction unit can analyze the user's social media activity and suggest instruction methods. The instruction unit, for example, analyzes the user's social media activity. For example, the instruction unit suggests instruction methods from athletes or trainers the user follows on social media. The instruction unit can also analyze the user's social media posts and suggest related instruction methods. The instruction unit can also suggest related instruction methods by referring to the activities of the user's friends on social media. For example, the instruction unit suggests instruction methods from athletes or trainers the user follows on social media, analyzes the user's social media posts, and suggests related instruction methods. The instruction unit can also suggest related instruction methods by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant instruction methods can be provided. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's social media data into a generation AI and have the generation AI suggest instruction methods.

[0110] When providing instruction, the instruction unit can customize the instruction method by reflecting the user's past feedback. The instruction unit, for example, reflects the user's past feedback. For example, the instruction unit may prioritize and provide instruction methods that the user has previously rated highly. The instruction unit may also exclude instruction methods that the user has previously rated poorly. The instruction unit may also optimize the instruction method based on the user's past feedback. For example, the instruction unit may prioritize and provide instruction methods that the user has previously rated highly, and exclude instruction methods that the user has previously rated poorly. The instruction unit may also optimize the instruction method based on the user's past feedback. In this way, by reflecting the user's past feedback, a more effective instruction method can be provided. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may input the user's feedback data into a generation AI and cause the generation AI to customize the instruction method.

[0111] The purchasing unit can estimate a user's emotions and adjust the purchase suggestion method based on the estimated user's emotions. The purchasing unit, for example, estimates the user's emotions. For example, the purchasing unit can estimate the user's emotions using facial expression recognition technology. The purchasing unit can also estimate the user's emotions using voice analysis technology. For example, the purchasing unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The purchasing unit can also adjust the purchase suggestion method based on the user's emotions. For example, the purchasing unit can provide a simple, highly visible suggestion method when the user is nervous. The purchasing unit can also provide a detailed suggestion method when the user is relaxed. The purchasing unit can also provide a suggestion method that focuses on the main points when the user is in a hurry. This enables more effective purchasing support by providing a purchase suggestion method that corresponds to the user's emotions. 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 purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit may input the user's facial expression data into the generating AI and have the generating AI estimate the emotion.

[0112] When making a purchase suggestion, the purchasing unit can provide optimal suggestions by referring to the user's past purchase history. The purchasing unit, for example, refers to the user's past purchase history. For example, the purchasing unit refers to products the user has purchased in the past. The purchasing unit can also refer to the user's past purchase frequency. For example, the purchasing unit can suggest related products based on the user's past purchase history. The purchasing unit can also suggest products that match the user's preferences and needs based on the user's past purchase history. The purchasing unit can also make suggestions that take into account the user's purchase frequency and timing based on the user's past purchase history. This enables more effective purchasing support by providing optimal suggestions based on the user's past purchase history. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's purchase history data into a generation AI and have the generation AI provide optimal suggestions.

[0113] When making a purchase suggestion, the purchasing unit can customize the suggestion content based on the user's current training status. The purchasing unit, for example, evaluates the user's current training status. For example, the purchasing unit evaluates the type, frequency, and intensity of the user's exercise. The purchasing unit can also customize the suggestion content according to the user's training progress. For example, the purchasing unit can recommend necessary equipment and apparel based on the user's current training status. The purchasing unit can also recommend appropriate supplements according to the user's training progress. The purchasing unit can also recommend effective training goods tailored to the user's training goals. This enables more effective purchasing support by providing suggestions based on the user's training status. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without AI. For example, the purchasing unit can input the user's training status data into a generation AI and have the generation AI customize the suggestion content.

[0114] The purchasing unit can reflect user feedback when making purchase suggestions to improve the suggestion method. The purchasing unit, for example, reflects user feedback. For example, the purchasing unit prioritizes suggesting products that the user has previously rated highly. The purchasing unit can also exclude products that the user has previously rated poorly. The purchasing unit can also optimize the suggestion method based on user feedback. For example, the purchasing unit prioritizes suggesting products that the user has previously rated highly and excludes products that the user has previously rated poorly. The purchasing unit can also optimize the suggestion method based on user feedback. In this way, more effective purchase suggestions can be provided by reflecting user feedback. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input user feedback data into a generation AI and cause the generation AI to improve the suggestion method.

[0115] The purchasing unit can estimate a user's emotions and determine purchase priorities based on the estimated user emotions. The purchasing unit, for example, estimates the user's emotions. For example, the purchasing unit can estimate the user's emotions using facial expression recognition technology. The purchasing unit can also estimate the user's emotions using voice analysis technology. For example, the purchasing unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The purchasing unit can also determine purchase priorities based on the user's emotions. For example, if the user is low in motivation, the purchasing unit can prioritize simple and effective products. If the user is excited, the purchasing unit can prioritize challenging products. If the user is relaxed, the purchasing unit can prioritize products with a relaxing effect. This enables more effective purchase support by providing purchase priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit may input the user's facial expression data into the generating AI and have the generating AI estimate the emotion.

[0116] When making purchase suggestions, the purchasing unit can provide optimal suggestions by taking into account the user's geographical location information. The purchasing unit, for example, considers the user's geographical location information. For example, the purchasing unit prioritizes suggesting products that can be purchased near the user's current location. The purchasing unit can also suggest products and services that are unique to the region based on the user's geographical location. The purchasing unit can also suggest products that are suitable for the weather or season based on the user's geographical location. For example, the purchasing unit prioritizes suggesting products that can be purchased near the user's current location and suggests products and services that are unique to the region based on the user's geographical location. The purchasing unit can also suggest products that are suitable for the weather or season based on the user's geographical location. This makes it possible to provide more effective purchase suggestions by taking into account the user's geographical location information. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's geographical location data into a generation AI and cause the generation AI to provide optimal suggestions.

[0117] When making a purchase suggestion, the purchasing unit can analyze the user's social media activity and suggest related products. The purchasing unit, for example, analyzes the user's social media activity. For example, the purchasing unit suggests information about brands and products that the user follows on social media. The purchasing unit can also analyze the user's social media posts and suggest related products. The purchasing unit can also suggest related products by referring to the activities of the user's friends on social media. For example, the purchasing unit suggests information about brands and products that the user follows on social media, analyzes the user's social media posts, and suggests related products. The purchasing unit can also suggest related products by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant products can be suggested. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit may input the user's social media data into a generation AI and have the generation AI suggest related products.

[0118] When making a purchase suggestion, the purchasing unit can customize the suggestion method by reflecting the user's past feedback. The purchasing unit, for example, reflects the user's past feedback. For example, the purchasing unit may prioritize suggesting products that the user has previously rated highly. The purchasing unit may also exclude products that the user has previously rated poorly. The purchasing unit may also optimize the suggestion method based on the user's past feedback. For example, the purchasing unit may prioritize suggesting products that the user has previously rated highly and exclude products that the user has previously rated poorly. The purchasing unit may also optimize the suggestion method based on the user's past feedback. This allows for more effective purchase suggestions to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit may input user feedback data into a generation AI and have the generation AI customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, creation unit, instruction unit, and purchase 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 collection unit collects the user's exercise data and goals using sensors and applications of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training menu based on the analysis results. The instruction unit is realized by the control unit 46A of the smart device 14 and provides instruction to the user based on the created training menu. The purchase unit is realized by the control unit 46A of the smart device 14 and supports the purchase of exercise-related goods. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, creation unit, instruction unit, and purchase 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 collection unit collects the user's exercise data and goals using sensors and applications of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training menu based on the analysis results. The instruction unit is realized by the control unit 46A of the smart glasses 214 and provides instruction to the user based on the created training menu. The purchase unit is realized by the control unit 46A of the smart glasses 214 and supports the purchase of exercise-related goods. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, creation unit, instruction unit, and purchasing 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 collection unit collects the user's exercise data and goals using sensors and applications of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training menu based on the analysis results. The instruction unit is realized by the control unit 46A of the headset-type terminal 314 and provides instruction to the user based on the created training menu. The purchasing unit is realized by the control unit 46A of the headset-type terminal 314 and supports the purchase of exercise-related goods. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, creation unit, instruction unit, and purchase unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's exercise data and goals using sensors and applications of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a training menu based on the analysis results. The instruction unit is realized by the control unit 46A of the robot 414 and provides instruction to the user based on the created training menu. The purchase unit is realized by the control unit 46A of the robot 414 and supports the purchase of exercise-related goods.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] When collecting information about the user's sports, the collection unit can also collect the user's diet and nutritional intake status. For example, the collection unit evaluates nutritional balance by having the user input the details of their daily meals. The collection unit can also record the types and amounts of supplements the user takes. Furthermore, the collection unit can suggest meal menus suitable for training based on the user's dietary history. This makes it possible to create training menus that take into account the user's diet and nutritional intake status.

[0121] The analysis unit can analyze the user's sleep patterns based on the collected information. For example, the analysis unit can evaluate the user's sleep duration and quality. The analysis unit can also suggest optimal sleep duration to maximize the effectiveness of training based on the user's sleep data. Furthermore, the analysis unit can correlate and analyze the user's sleep patterns with the results of training and suggest an effective training schedule. This makes it possible to create a training menu that takes the user's sleep patterns into consideration.

[0122] The creation unit can evaluate the user's stress level and suggest a training menu aimed at stress reduction. For example, the creation unit can evaluate the user's stress level using a questionnaire or a sensor. The creation unit can also suggest yoga or meditation menus that are effective in reducing stress. Furthermore, the creation unit can provide a training menu with a relaxing effect according to the user's stress level. This makes it possible to create a training menu that takes the user's stress level into consideration.

[0123] The instructor can monitor the user's training progress in real time and adjust the training menu as needed. For example, the instructor can collect the user's exercise data in real time and evaluate the user's progress. The instructor can also adjust the user's training menu in real time to provide effective training. Furthermore, the instructor can provide feedback according to the user's training progress. This makes it possible to monitor the user's training progress in real time and provide effective training.

[0124] The purchasing department can suggest equipment and apparel necessary for training based on the user's training progress. For example, the purchasing department can evaluate the user's training progress and suggest the necessary equipment and apparel. The purchasing department can also suggest appropriate supplements based on the user's training progress. Furthermore, the purchasing department can suggest effective training goods that match the user's training goals. This allows for more effective purchasing support by providing suggestions based on the user's training progress.

[0125] The collection unit can estimate the user's emotions and customize the sports information collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can collect information in a way that allows the user to relax. Also, if the user is highly motivated, the collection unit can proactively collect information. Furthermore, if the user is tired, the collection unit can collect information after the user has rested. This makes it possible to collect sports information in an optimal way according to the user's emotions.

[0126] The analysis unit can estimate the user's emotions and adjust the method of providing feedback of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible feedback. If the user is relaxed, the analysis unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the analysis unit can also provide feedback that focuses on the main points. This makes it possible to provide optimal feedback according to the user's emotions.

[0127] The creation unit can estimate the user's emotions and adjust the difficulty of the training menu based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit can provide a training menu with a low level of difficulty. Also, if the user is highly motivated, the creation unit can provide a training menu with a high level of difficulty. Furthermore, if the user is tired, the creation unit can provide a recovery menu. In this way, it is possible to provide an optimal training menu according to the user's emotions.

[0128] The instruction unit can estimate the user's emotions and adjust the timing of instruction based on the estimated user's emotions. For example, if the user is feeling stressed, the instruction unit can provide instruction at a time when the user can relax. Also, if the user is highly motivated, the instruction unit can provide instruction immediately. Furthermore, if the user is tired, the instruction unit can provide instruction after the user has rested. This allows instruction to be provided at the optimal timing according to the user's emotions.

[0129] The purchasing unit can estimate the user's emotions and adjust the content of purchase suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the purchasing unit can suggest products with a relaxing effect. Also, if the user is highly motivated, the purchasing unit can suggest challenging products. Furthermore, if the user is tired, the purchasing unit can suggest products with a recovery effect. In this way, it is possible to provide optimal purchase suggestions according to the user's emotions.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The collection unit collects information about the user's sport. The information about the user's sport includes, for example, exercise history, health status, and goals. The collection unit collects the user's exercise data using sensors and collects the user's goals and challenges through a questionnaire. The collection unit can also collect the user's training history through an application. For example, the collection unit can collect the user's exercise data in real time using a wearable device and collect the user's goals and challenges through an online questionnaire. The collection unit can also collect the user's training history through a training app. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data analysis techniques and algorithms. For example, machine learning algorithms can be used to analyze the user's exercise data, and statistical analysis can be used to evaluate the user's health condition. Data mining techniques can also be used to analyze the user's training history. This allows the effectiveness of training to be evaluated and an appropriate training menu to be proposed. Step 3: The creation unit creates a training menu based on the analysis results obtained by the analysis unit. The training menu is created based on the type, frequency, intensity, etc. of exercise. For example, a strength training menu is created based on the user's goals, and an endurance training menu is created based on the user's challenges. It is also possible to create a training menu for improving skills based on the user's training history. Step 4: The instructor provides instruction based on the training menu created by the creator. The instruction is provided through online instruction, face-to-face instruction, feedback methods, etc. For example, the instructor can instruct the user on training methods and important points through online instruction, and check the user's training progress through face-to-face instruction. The instructor can also use feedback methods to evaluate the effectiveness of the training and suggest areas for improvement. Step 5: The purchasing department provides support for purchasing exercise-related goods. Purchasing support is based on product recommendation methods and the flow of the purchasing process. For example, they recommend the equipment and clothing needed for training and support the purchasing process for supplements. They can also make suggestions to easily gather the items needed for training and support the selection of products that meet the user's needs.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about a user's sports; an analysis unit that analyzes the information collected by the collection unit; a creation unit that creates a training menu based on the analysis results obtained by the analysis unit; a training unit that provides training based on the training menu created by the creation unit; A purchasing section that supports the purchase of sports-related goods. A system characterized by:

2. The collecting unit Collect information including your current sports level, goals, challenges, and past training history 2. The system of claim 1.

3. The analysis unit Analyze the user's current abilities and challenges based on the collected information 2. The system of claim 1.

4. The creation unit Propose training menus tailored to the user's goals and challenges 2. The system of claim 1.

5. The leadership team: Training methods, points to note, and progress checks 2. The system of claim 1.

6. The purchasing department Recommend and purchase equipment, clothing, and supplements necessary for training 2. The system of claim 1.

7. The collecting unit To estimate a user's emotions and adjust the timing of collecting sports information based on the estimated user's emotions.

2. The system of claim 1.

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

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