System

Abstract

An object of a system according to an embodiment is to grasp the current state of a child's muscle strength and technique and provide a training plan based on the current state.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives information on a child from a parent. The analysis part analyzes the information received by the reception part and grasps the present state of the muscular strength or technique of the child. The generation unit generates a training plan based on the analysis result obtained by the analysis unit. The providing unit provides the training plan generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately grasp a child's current muscle strength and technique and provide an appropriate training plan based on that.

[0005] The system according to the embodiment aims to grasp the current state of a child's muscle strength and technique, and provide a training plan based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information about the child from the parent. The analysis unit analyzes the information received by the reception unit and determines the child's current muscle strength or technique. The generation unit generates a training plan based on the analysis results obtained by the analysis unit. The provision unit provides the training plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the current state of a child's muscle strength and technique, and provide a training plan based on that information. [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 improvement support system according to an embodiment of the present invention accepts and analyzes information about a child from parents, and generates and provides an optimal training plan. In the sports improvement support system, parents input information about their child's desired muscle strength and technique improvement. AI analyzes the information and generates an optimal training plan. This training plan is customized according to the child's age, physical strength, and skill level. For example, the sports improvement support system analyzes a child's pitching form or batting form and identifies areas for improvement. It also suggests strength training menus. This allows parents to provide appropriate training for their children, thereby improving their sports skills. For example, the sports improvement support system inputs information about their child's desired muscle strength and technique improvement. In this case, detailed information such as the child's age, physical strength, and skill level is input. For example, videos of the child's pitching form or batting form can be provided to the AI. The sports improvement support system then uses the AI ​​to analyze the input information. Based on the provided videos and data, the AI ​​understands the child's current muscle strength and technique. For example, in analyzing a pitching form, a detailed analysis of the child's arm swing and body usage is performed to identify areas for improvement. Based on the analysis results, the AI ​​in the sports improvement support system generates an optimal training plan. For example, it suggests specific strength training and technique exercises based on areas for improvement in pitching form. It also suggests swing practice methods based on areas for improvement in batting form. This improves a child's strength and technique. This allows parents to provide appropriate training for their children. For example, if a child practices daily according to the training plan suggested by the AI, their strength and technique will improve. They can also regularly provide videos to the AI ​​to check their training progress. This allows a child's sports technique to continuously improve.

[0029] A sports improvement support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information about a child from a parent. The information about a child from a parent includes, but is not limited to, information about the child's age, physical strength, skill level, and health condition. The reception unit receives, for example, information entered by the parent in digital format. The reception unit can also receive video and image data provided by the parent. For example, the reception unit has a function for uploading videos taken by the parent with a smartphone. The analysis unit analyzes the information received by the reception unit to understand the child's current muscular strength and skill. The analysis unit evaluates the child's current muscular strength and skill based on, for example, the provided video and data. For example, the analysis unit analyzes a video of a pitching form and performs a detailed analysis of the player's arm swing and body usage. The analysis unit can also analyze a video of a batting form and analyze the swing trajectory and body movement. The generation unit generates an optimal training plan based on the analysis results obtained by the analysis unit. The generation unit generates a training plan that suggests specific muscle strength training and skill practice based on the analysis results, for example. For example, the generation unit suggests specific muscle strength training and skill practice based on areas for improvement in pitching form. The generation unit can also suggest a swing practice method based on areas for improvement in batting form. The provision unit provides the training plan generated by the generation unit. For example, the provision unit provides the generated training plan to a parent. For example, the provision unit sends the training plan by email. The provision unit can also provide the training plan by app notification. For example, the provision unit can also provide the training plan as a printed document. This allows the sports improvement support system according to the embodiment to allow parents to provide appropriate training for their children. For example, if a child practices daily according to the training plan suggested by the AI, their muscle strength and skill will improve. Furthermore, videos can be periodically provided to the AI ​​to check the progress of the training. This allows the child's sports skill to continuously improve.

[0030] The reception unit can receive information on the child's age, physical strength, and skill level. The reception unit can receive, for example, information on the child's age, physical strength, and skill level input by a parent in digital format. For example, the reception unit can determine the child's age based on the age information input by the parent. The reception unit can also evaluate the child's physical strength based on the physical strength information input by the parent. The reception unit can also evaluate the child's skill level based on the skill level information input by the parent. By receiving detailed information about the child, a more appropriate training plan can be generated. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the information input by the parent to a generation AI and have the generation AI analyze the information.

[0031] The analysis unit can grasp the current state of a child's muscle strength or technique based on the provided video or data. The analysis unit grasps the current state of a child's muscle strength or technique based on, for example, the provided video. For example, the analysis unit analyzes a video of a pitching form and analyzes in detail how the arm swings and body is used. The analysis unit can also analyze a video of a batting form and analyze the swing trajectory and body movement. The analysis unit can also grasp the current state of a child's muscle strength or technique based on the provided data. For example, the analysis unit evaluates the child's muscle strength based on the results of a muscle strength test. The analysis unit can also evaluate the child's technical level based on a technical score. In this way, by analyzing the video and data, the current state of a child can be accurately grasped. 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 provided video and data into a generation AI and have the generation AI perform the analysis.

[0032] The generation unit can generate a training plan that proposes specific muscle training or technical practice based on the analysis results. The generation unit, for example, generates a training plan that proposes specific muscle training based on the analysis results. For example, the generation unit proposes specific muscle training based on areas for improvement in pitching form. The generation unit can also generate a training plan that proposes specific technical practice based on the analysis results. For example, the generation unit proposes a swing practice method based on areas for improvement in batting form. The generation unit can also generate a training plan that combines specific muscle training and technical practice based on the analysis results. For example, the generation unit proposes a plan that combines muscle training and technical practice. This allows for the generation of an optimal training plan based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate a training plan.

[0033] The providing unit can provide the generated training plan to the parent. The providing unit, for example, provides the generated training plan to the parent. For example, the providing unit sends the training plan by email. The providing unit can also provide the training plan by app notification. The providing unit can also provide the training plan as a printed document. For example, the providing unit can mail the training plan to the parent. In this way, by providing the generated training plan to the parent, appropriate training can be carried out. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated training plan into the generation AI and have the generation AI select a provision method.

[0034] The analysis unit can analyze videos of pitching form or batting form to identify areas for improvement. The analysis unit, for example, analyzes videos of pitching form to identify areas for improvement. For example, the analysis unit can analyze in detail how the arm swing and body are used to identify areas for improvement. The analysis unit can also analyze videos of batting form to analyze the swing trajectory and body movement. For example, the analysis unit can analyze the swing trajectory to identify areas for improvement. The analysis unit can also analyze both pitching form and batting form to identify overall areas for improvement. For example, the analysis unit can compare videos of pitching form and batting form to identify common areas for improvement. This can help improve skills by identifying areas for improvement in form. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input videos of pitching form or batting form into a generation AI and have the generation AI identify areas for improvement.

[0035] The generation unit can generate a training plan that suggests a method for practicing a swing. The generation unit, for example, generates a training plan that suggests a method for practicing a swing. For example, the generation unit suggests a method for practicing tee batting. The generation unit can also suggest a method for practicing in a batting cage. For example, the generation unit suggests swing practice in a batting cage. The generation unit can also generate a training plan that suggests a method for practicing a swing. For example, the generation unit generates a training plan that suggests a method for practicing a swing. In this way, by suggesting a method for practicing a swing, it is possible to support the improvement of batting technique. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a method for practicing a swing into the generation AI and cause the generation AI to generate a training plan.

[0036] The reception unit can analyze the child's past training history and select the optimal information reception method. The reception unit, for example, analyzes the child's past training history and selects the optimal information reception method. For example, the reception unit prioritizes and suggests frequently used input methods based on the past training history. The reception unit can also automatically display specific training menus as candidates based on the past training history. For example, the reception unit analyzes the past training history and suggests input methods to be used during specific time periods. This enables efficient information reception by selecting the optimal information reception method based on the past training history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past training history data into a generation AI and cause the generation AI to select the optimal information reception method.

[0037] The reception unit can filter the information based on the child's current health condition and lifestyle habits when receiving the information. For example, the reception unit can filter the information based on the child's current health condition and lifestyle habits when receiving the information. For example, if the child's health condition is not good, the reception unit can suggest a low-impact training menu. The reception unit can also suggest an appropriate training time based on the child's lifestyle habits. For example, the reception unit can suggest a nutritionally balanced training menu taking into account the child's eating habits. In this way, an appropriate training menu can be suggested by filtering the information based on the child's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the child's health condition and lifestyle habit data into the generation AI and have the generation AI perform the filtering.

[0038] The reception unit can select the optimal reception means depending on the parent's input method when receiving information. For example, when receiving information, the reception unit selects the optimal reception means depending on the parent's input method. For example, when the parent inputs information by voice, the reception unit uses voice recognition technology to receive the information. Also, when the parent inputs information by text, the reception unit can provide a text input interface. For example, when the parent inputs information by image, the reception unit uses image recognition technology to receive the information. This allows for smooth information reception by selecting the optimal reception means depending on the parent's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input parent's input method data to the generation AI and cause the generation AI to select the optimal reception means.

[0039] When accepting information, the reception unit can prioritize accepting highly relevant information by taking into consideration the geographical location information of the parent. For example, when accepting information, the reception unit prioritizes accepting highly relevant information by taking into consideration the geographical location information of the parent. For example, if the parent is in a specific area, the reception unit prioritizes accepting training information related to that area. Furthermore, if the parent is traveling, the reception unit can also prioritize accepting training information related to the travel destination. For example, if the parent is at home, the reception unit prioritizes accepting training information that can be done at home. In this way, by taking the geographical location information of the parent into consideration, highly relevant information can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the parent into the generation AI and cause the generation AI to select highly relevant information.

[0040] The reception unit can analyze the parent's social media activity and receive related information when receiving information. For example, the reception unit analyzes the parent's social media activity and receives related information when receiving information. For example, the reception unit preferentially receives training information shared by the parent on social media. The reception unit can also analyze the content of the parent's social media posts and receive related training information. For example, the reception unit receives related training information with reference to the activities of the parent's friends on social media. In this way, by analyzing the parent's social media activity, related information can be efficiently received. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data into the generation AI and cause the generation AI to select related information.

[0041] The reception unit can customize the reception method by reflecting the parent's past feedback when receiving information. For example, the reception unit customizes the reception method by reflecting the parent's past feedback when receiving information. For example, the reception unit adjusts the reception method based on feedback provided by the parent in the past. The reception unit can also preferentially receive specific training information based on the parent's past feedback. For example, the reception unit analyzes the parent's past feedback and suggests an optimal reception method. This allows the reception method to be optimized by reflecting the parent's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the parent's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the child's muscle strength or technique. For example, if the child's muscle strength is important, the analysis unit provides detailed analysis results related to muscle strength. Furthermore, if the child's technique is important, the analysis unit can also provide detailed analysis results related to technique. For example, if both the child's muscle strength and technique are important, the analysis unit provides balanced analysis results. In this way, by adjusting the level of detail of the analysis based on the importance of the child's muscle strength or technique, appropriate analysis results can be provided. 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 child's muscle strength and technique data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the child's sports category. For example, in the case of baseball, the analysis unit applies analysis algorithms for pitching form and batting form. The analysis unit can also apply analysis algorithms for dribbling and shooting in the case of soccer. For example, the analysis unit applies analysis algorithms for shooting form and dribbling in the case of basketball. By applying an analysis algorithm according to the sports category, more accurate analysis results can be provided. 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 category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the parent's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the analysis results provided by the parent in the past. The analysis unit can also preferentially apply a specific analysis method based on the parent's past analysis results. For example, the analysis unit analyzes the parent's past analysis results and proposes the optimal analysis method. This makes it possible to improve the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the parent's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine analysis priorities based on the child's training history. For example, the analysis unit prioritizes analysis of important training items based on the child's past training history. The analysis unit can also prioritize analysis of specific training items based on the child's training history. For example, the analysis unit analyzes the child's training history and suggests an optimal analysis order. This allows important items to be analyzed preferentially by determining analysis priorities based on the child's training history. 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 child's training history data into the generation AI and have the generation AI determine the analysis priorities.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the child's related data. For example, the analysis unit prioritizes analysis related to muscle strength based on the child's muscle strength data. The analysis unit can also prioritize analysis related to technique based on the child's technical data. For example, the analysis unit analyzes the child's related data and proposes an optimal analysis order. This enables efficient analysis by adjusting the analysis order based on the child's related data. 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 child's related data into the generation AI and have the generation AI adjust the analysis order.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the parent's level of expertise. For example, if the parent has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the parent does not have technical expertise, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can suggest an optimal way to express the analysis results based on the parent's level of expertise. This allows the analysis results to be provided that are easy for the parent to understand by adjusting the use of technical terminology in the analysis according to the parent's level of expertise. 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 parent's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0048] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the importance of the child's muscle strength and technique. For example, if the child's muscle strength is important, the generation unit can provide a plan that emphasizes muscle strength training. Also, if the child's technique is important, the generation unit can provide a plan that emphasizes technique practice. For example, if both the child's muscle strength and technique are important, the generation unit can provide a balanced plan. In this way, an appropriate training plan can be provided by adjusting the level of detail of the plan based on the importance of the child's muscle strength and technique. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's muscle strength and technique data into the generation AI and have the generation AI adjust the level of detail of the plan.

[0049] When generating a training plan, the generation unit can apply different plan generation algorithms depending on the child's sport category. For example, in the case of baseball, the generation unit provides a plan that focuses on improving pitching form and batting form. The generation unit can also provide a plan that focuses on improving dribbling and shooting in the case of soccer. For example, the generation unit provides a plan that focuses on improving shooting form and dribbling in the case of basketball. This allows for a more appropriate training plan to be provided by applying a plan generation algorithm depending on the sport category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sport category data into the generation AI and cause the generation AI to apply the plan generation algorithm.

[0050] When generating a training plan, the generation unit can improve the accuracy of the plan by referring to the parent's past plan results. The generation unit, for example, adjusts the plan generation algorithm based on the plan results provided by the parent in the past. The generation unit can also preferentially apply specific training items based on the parent's past plan results. For example, the generation unit analyzes the parent's past plan results and proposes an optimal plan generation method. This makes it possible to improve the accuracy of the plan by referring to the past plan results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the parent's past plan result data into the generation AI and have the generation AI improve the accuracy of the plan.

[0051] When generating a training plan, the generation unit can determine the priority of the plan based on the child's training history. For example, the generation unit can prioritize important training items based on the child's past training history and incorporate them into the plan. The generation unit can also prioritize specific training items based on the child's training history. For example, the generation unit can analyze the child's training history and propose an optimal plan order. This allows important items to be prioritized and incorporated into the plan. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the child's training history data into the generation AI and have the generation AI determine the plan priorities.

[0052] When generating a training plan, the generation unit can adjust the order of the plan based on the child's related data. For example, the generation unit can prioritize strength training in the plan based on the child's muscle strength data. The generation unit can also prioritize technical practice in the plan based on the child's technical data. For example, the generation unit can analyze the child's related data and propose an optimal plan order. This allows for an efficient training plan to be provided by adjusting the plan order based on the child's related data. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the child's related data into the generation AI and have the generation AI adjust the order of the plan.

[0053] When generating a training plan, the generation unit can adjust the use of technical terminology in the plan according to the parent's level of expertise. For example, if the parent has technical expertise, the generation unit can provide the plan using detailed technical terminology. Alternatively, if the parent does not have technical expertise, the generation unit can provide the plan in simple language. For example, the generation unit can suggest an optimal way to express the plan based on the parent's level of expertise. This allows the parent to be provided with a plan that is easy for the parent to understand by adjusting the use of technical terminology in the plan according to the parent's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the parent's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0054] When providing a training plan, the providing unit can adjust the level of detail of the plan based on the importance of the child's muscle strength and technique. For example, if the child's muscle strength is important, the providing unit can provide a detailed plan that emphasizes muscle strength training. Furthermore, if the child's technique is important, the providing unit can provide a detailed plan that emphasizes technique practice. For example, if both the child's muscle strength and technique are important, the providing unit can provide a balanced detailed plan. This allows an appropriate training plan to be provided by adjusting the level of detail of the plan based on the importance of the child's muscle strength and technique. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's muscle strength and technique data into the generating AI and cause the generating AI to adjust the level of detail of the plan.

[0055] When providing a training plan, the providing unit can apply different providing algorithms depending on the child's sports category. For example, in the case of baseball, the providing unit can provide a plan that focuses on improving pitching form and batting form. In addition, in the case of soccer, the providing unit can also provide a plan that focuses on improving dribbling and shooting. For example, in the case of basketball, the providing unit can provide a plan that focuses on improving shooting form and dribbling. In this way, by applying a providing algorithm according to the sports category, a more appropriate training plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input sports category data into the generation AI and cause the generation AI to apply the providing algorithm.

[0056] When providing a training plan, the providing unit can improve the accuracy of the provision by referring to the parent's past provision results. For example, the providing unit adjusts the provision algorithm based on the plan results provided by the parent in the past. The providing unit can also preferentially apply specific training items based on the parent's past provision results. For example, the providing unit analyzes the parent's past provision results and proposes an optimal provision method. This makes it possible to improve the accuracy of the provision by referring to the past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the parent's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0057] When providing a training plan, the providing unit can determine the priority of provision based on the child's training history. For example, the providing unit prioritizes important training items based on the child's past training history to incorporate them into the plan. The providing unit can also prioritize specific training items to incorporate into the plan based on the child's training history. For example, the providing unit analyzes the child's training history and proposes an optimal plan order. This allows important items to be prioritized and incorporated into the plan. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's training history data into the generating AI and have the generating AI determine the priority of provision.

[0058] When providing a training plan, the providing unit can adjust the order of provision based on the child's related data. For example, the providing unit prioritizes incorporating strength training into the plan based on the child's muscle strength data. The providing unit can also prioritize technical practice into the plan based on the child's technical data. For example, the providing unit analyzes the child's related data and proposes an optimal plan order. This makes it possible to provide an efficient training plan by adjusting the order of provision based on the child's related data. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's related data into a generating AI and cause the generating AI to adjust the order of provision.

[0059] When providing a training plan, the providing unit can adjust the use of technical terminology in the provided plan according to the parent's level of expertise. For example, if the parent has technical expertise, the providing unit can provide the plan using detailed technical terminology. Alternatively, if the parent does not have technical expertise, the providing unit can provide the plan in simple language. For example, the providing unit can suggest an optimal way to express the plan based on the parent's level of expertise. This allows the provision of a plan that is easy for the parent to understand by adjusting the use of technical terminology in the provided plan according to the parent's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the parent's level of expertise data into a generating AI and cause the generating AI to use technical terminology.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze a child's past training history and select the optimal analysis algorithm. For example, it can prioritize the application of frequently used analysis methods based on past training history. It can also automatically display specific training menus as candidates based on past training history. It can also suggest analysis methods to use during specific time periods. This allows for efficient analysis by selecting the optimal analysis method based on past training history.

[0062] When receiving information, the reception unit can filter the information based on the child's current health condition and lifestyle habits. For example, if the child's health condition is not good, a low-impact training menu can be suggested. The reception unit can also suggest an appropriate training time based on the child's lifestyle habits. Furthermore, the reception unit can suggest a nutritionally balanced training menu taking into account the child's eating habits. In this way, by filtering information based on the child's health condition and lifestyle habits, an appropriate training menu can be suggested.

[0063] During analysis, the analysis unit can apply different analysis algorithms depending on the child's sports category. For example, in the case of baseball, an analysis algorithm for pitching form and batting form is applied. In addition, in the case of soccer, an analysis algorithm for dribbling and shooting can be applied. Furthermore, in the case of basketball, an analysis algorithm for shooting form and dribbling is applied. In this way, by applying an analysis algorithm according to the sports category, more accurate analysis results can be provided.

[0064] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the importance of the child's muscle strength or technique. For example, if the child's muscle strength is important, a plan that emphasizes muscle strength training can be provided. Alternatively, if the child's technique is important, a plan that emphasizes technique practice can be provided. Furthermore, if both the child's muscle strength and technique are important, a balanced plan can be provided. In this way, an appropriate training plan can be provided by adjusting the level of detail of the plan based on the importance of the child's muscle strength or technique.

[0065] When providing a training plan, the provider can determine the priority of provision based on the child's training history. For example, important training items can be preferentially incorporated into the plan based on the child's past training history. Also, specific training items can be preferentially incorporated into the plan based on the child's training history. Furthermore, the provider can analyze the child's training history and propose the optimal plan order. In this way, by determining the priority of provision based on the child's training history, important items can be preferentially incorporated into the plan.

[0066] When accepting information, the acceptance unit can analyze the parent's social media activity and accept related information. For example, training information shared by the parent on social media can be accepted with priority. The acceptance unit can also analyze the content of the parent's social media posts and accept related training information. Furthermore, the acceptance unit can accept related training information with reference to the activities of the parent's friends on social media. In this way, by analyzing the parent's social media activity, related information can be accepted efficiently.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit accepts information about the child from the parent. This information includes the child's age, physical strength, skill level, and health condition. The reception unit not only accepts the information entered by the parent in digital format, but also accepts video and image data provided by the parent. For example, the unit has a function that allows parents to upload videos they have taken with their smartphones. Step 2: The analysis unit analyzes the information received by the reception unit and understands the child's current muscle strength and technique. The analysis unit evaluates the child's current muscle strength and technique based on the provided video and data. For example, it can analyze a video of a pitching form and perform a detailed analysis of how the child swings their arm and uses their body. It can also analyze a video of a batting form to analyze the swing trajectory and body movement. Step 3: The generation unit generates an optimal training plan based on the analysis results obtained by the analysis unit. The generation unit generates a training plan that suggests specific muscle training and technique practice based on the analysis results. For example, the generation unit can suggest specific muscle training and technique practice based on areas for improvement in pitching form. It can also suggest swing practice methods based on areas for improvement in batting form. Step 4: The providing unit provides the training plan generated by the generating unit. The providing unit provides the generated training plan to the parent. For example, the training plan can be sent by email or provided via an app notification. The training plan can also be provided as a printed document.

[0069] (Example 2) A sports improvement support system according to an embodiment of the present invention accepts and analyzes information about a child from parents, and generates and provides an optimal training plan. In the sports improvement support system, parents input information about their child's desired muscle strength and technique improvement. AI analyzes the information and generates an optimal training plan. This training plan is customized according to the child's age, physical strength, and skill level. For example, the sports improvement support system analyzes a child's pitching form or batting form and identifies areas for improvement. It also suggests strength training menus. This allows parents to provide appropriate training for their children, thereby improving their sports skills. For example, the sports improvement support system inputs information about their child's desired muscle strength and technique improvement. In this case, detailed information such as the child's age, physical strength, and skill level is input. For example, videos of the child's pitching form or batting form can be provided to the AI. The sports improvement support system then uses the AI ​​to analyze the input information. Based on the provided videos and data, the AI ​​understands the child's current muscle strength and technique. For example, in analyzing a pitching form, a detailed analysis of the child's arm swing and body usage is performed to identify areas for improvement. Based on the analysis results, the AI ​​in the sports improvement support system generates an optimal training plan. For example, it suggests specific strength training and technique exercises based on areas for improvement in pitching form. It also suggests swing practice methods based on areas for improvement in batting form. This improves a child's strength and technique. This allows parents to provide appropriate training for their children. For example, if a child practices daily according to the training plan suggested by the AI, their strength and technique will improve. They can also regularly provide videos to the AI ​​to check their training progress. This allows a child's sports technique to continuously improve.

[0070] A sports improvement support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information about a child from a parent. The information about a child from a parent includes, but is not limited to, information about the child's age, physical strength, skill level, and health condition. The reception unit receives, for example, information entered by the parent in digital format. The reception unit can also receive video and image data provided by the parent. For example, the reception unit has a function for uploading videos taken by the parent with a smartphone. The analysis unit analyzes the information received by the reception unit to understand the child's current muscular strength and skill. The analysis unit evaluates the child's current muscular strength and skill based on, for example, the provided video and data. For example, the analysis unit analyzes a video of a pitching form and performs a detailed analysis of the player's arm swing and body usage. The analysis unit can also analyze a video of a batting form and analyze the swing trajectory and body movement. The generation unit generates an optimal training plan based on the analysis results obtained by the analysis unit. The generation unit generates a training plan that suggests specific muscle strength training and skill practice based on the analysis results, for example. For example, the generation unit suggests specific muscle strength training and skill practice based on areas for improvement in pitching form. The generation unit can also suggest a swing practice method based on areas for improvement in batting form. The provision unit provides the training plan generated by the generation unit. For example, the provision unit provides the generated training plan to a parent. For example, the provision unit sends the training plan by email. The provision unit can also provide the training plan by app notification. For example, the provision unit can also provide the training plan as a printed document. This allows the sports improvement support system according to the embodiment to allow parents to provide appropriate training for their children. For example, if a child practices daily according to the training plan suggested by the AI, their muscle strength and skill will improve. Furthermore, videos can be periodically provided to the AI ​​to check the progress of the training. This allows the child's sports skill to continuously improve.

[0071] The reception unit can receive information on the child's age, physical strength, and skill level. The reception unit can receive, for example, information on the child's age, physical strength, and skill level input by a parent in digital format. For example, the reception unit can determine the child's age based on the age information input by the parent. The reception unit can also evaluate the child's physical strength based on the physical strength information input by the parent. The reception unit can also evaluate the child's skill level based on the skill level information input by the parent. By receiving detailed information about the child, a more appropriate training plan can be generated. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the information input by the parent to a generation AI and have the generation AI analyze the information.

[0072] The analysis unit can grasp the current state of a child's muscle strength or technique based on the provided video or data. The analysis unit grasps the current state of a child's muscle strength or technique based on, for example, the provided video. For example, the analysis unit analyzes a video of a pitching form and analyzes in detail how the arm swings and body is used. The analysis unit can also analyze a video of a batting form and analyze the swing trajectory and body movement. The analysis unit can also grasp the current state of a child's muscle strength or technique based on the provided data. For example, the analysis unit evaluates the child's muscle strength based on the results of a muscle strength test. The analysis unit can also evaluate the child's technical level based on a technical score. In this way, by analyzing the video and data, the current state of a child can be accurately grasped. 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 provided video and data into a generation AI and have the generation AI perform the analysis.

[0073] The generation unit can generate a training plan that proposes specific muscle training or technical practice based on the analysis results. The generation unit, for example, generates a training plan that proposes specific muscle training based on the analysis results. For example, the generation unit proposes specific muscle training based on areas for improvement in pitching form. The generation unit can also generate a training plan that proposes specific technical practice based on the analysis results. For example, the generation unit proposes a swing practice method based on areas for improvement in batting form. The generation unit can also generate a training plan that combines specific muscle training and technical practice based on the analysis results. For example, the generation unit proposes a plan that combines muscle training and technical practice. This allows for the generation of an optimal training plan based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate a training plan.

[0074] The providing unit can provide the generated training plan to the parent. The providing unit, for example, provides the generated training plan to the parent. For example, the providing unit sends the training plan by email. The providing unit can also provide the training plan by app notification. The providing unit can also provide the training plan as a printed document. For example, the providing unit can mail the training plan to the parent. In this way, by providing the generated training plan to the parent, appropriate training can be carried out. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated training plan into the generation AI and have the generation AI select a provision method.

[0075] The analysis unit can analyze videos of pitching form or batting form to identify areas for improvement. The analysis unit, for example, analyzes videos of pitching form to identify areas for improvement. For example, the analysis unit can analyze in detail how the arm swing and body are used to identify areas for improvement. The analysis unit can also analyze videos of batting form to analyze the swing trajectory and body movement. For example, the analysis unit can analyze the swing trajectory to identify areas for improvement. The analysis unit can also analyze both pitching form and batting form to identify overall areas for improvement. For example, the analysis unit can compare videos of pitching form and batting form to identify common areas for improvement. This can help improve skills by identifying areas for improvement in form. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input videos of pitching form or batting form into a generation AI and have the generation AI identify areas for improvement.

[0076] The generation unit can generate a training plan that suggests a method for practicing a swing. The generation unit, for example, generates a training plan that suggests a method for practicing a swing. For example, the generation unit suggests a method for practicing tee batting. The generation unit can also suggest a method for practicing in a batting cage. For example, the generation unit suggests swing practice in a batting cage. The generation unit can also generate a training plan that suggests a method for practicing a swing. For example, the generation unit generates a training plan that suggests a method for practicing a swing. In this way, by suggesting a method for practicing a swing, it is possible to support the improvement of batting technique. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a method for practicing a swing into the generation AI and cause the generation AI to generate a training plan.

[0077] The reception unit can estimate the parent's emotions and adjust the information reception method based on the estimated parent's emotions. For example, the reception unit can estimate the parent's emotions and adjust the information reception method based on the estimated parent's emotions. For example, if the parent is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the parent is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the parent is in a hurry, the reception unit can prioritize voice input to allow information to be entered quickly. This can reduce the burden on the parent by adjusting the information reception method according to the parent'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the parent's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The reception unit can analyze the child's past training history and select the optimal information reception method. The reception unit, for example, analyzes the child's past training history and selects the optimal information reception method. For example, the reception unit prioritizes and suggests frequently used input methods based on the past training history. The reception unit can also automatically display specific training menus as candidates based on the past training history. For example, the reception unit analyzes the past training history and suggests input methods to be used during specific time periods. This enables efficient information reception by selecting the optimal information reception method based on the past training history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past training history data into a generation AI and cause the generation AI to select the optimal information reception method.

[0079] The reception unit can filter the information based on the child's current health condition and lifestyle habits when receiving the information. For example, the reception unit can filter the information based on the child's current health condition and lifestyle habits when receiving the information. For example, if the child's health condition is not good, the reception unit can suggest a low-impact training menu. The reception unit can also suggest an appropriate training time based on the child's lifestyle habits. For example, the reception unit can suggest a nutritionally balanced training menu taking into account the child's eating habits. In this way, an appropriate training menu can be suggested by filtering the information based on the child's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the child's health condition and lifestyle habit data into the generation AI and have the generation AI perform the filtering.

[0080] The reception unit can select the optimal reception means depending on the parent's input method when receiving information. For example, when receiving information, the reception unit selects the optimal reception means depending on the parent's input method. For example, when the parent inputs information by voice, the reception unit uses voice recognition technology to receive the information. Also, when the parent inputs information by text, the reception unit can provide a text input interface. For example, when the parent inputs information by image, the reception unit uses image recognition technology to receive the information. This allows for smooth information reception by selecting the optimal reception means depending on the parent's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input parent's input method data to the generation AI and cause the generation AI to select the optimal reception means.

[0081] The reception unit can estimate the parent's emotion and determine the priority of information to be received based on the estimated parent's emotion. The reception unit, for example, estimates the parent's emotion and determines the priority of information to be received based on the estimated parent's emotion. For example, if the parent is stressed, the reception unit prioritizes receiving important information. The reception unit can also receive detailed information if the parent is relaxed. For example, if the parent is in a hurry, the reception unit prioritizes receiving information that can be input quickly. In this way, by determining the priority of information according to the parent's emotion, important information can be received preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the parent's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0082] When accepting information, the reception unit can prioritize accepting highly relevant information by taking into consideration the geographical location information of the parent. For example, when accepting information, the reception unit prioritizes accepting highly relevant information by taking into consideration the geographical location information of the parent. For example, if the parent is in a specific area, the reception unit prioritizes accepting training information related to that area. Furthermore, if the parent is traveling, the reception unit can also prioritize accepting training information related to the travel destination. For example, if the parent is at home, the reception unit prioritizes accepting training information that can be done at home. In this way, by taking the geographical location information of the parent into consideration, highly relevant information can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the parent into the generation AI and cause the generation AI to select highly relevant information.

[0083] The reception unit can analyze the parent's social media activity and receive related information when receiving information. For example, the reception unit analyzes the parent's social media activity and receives related information when receiving information. For example, the reception unit preferentially receives training information shared by the parent on social media. The reception unit can also analyze the content of the parent's social media posts and receive related training information. For example, the reception unit receives related training information with reference to the activities of the parent's friends on social media. In this way, by analyzing the parent's social media activity, related information can be efficiently received. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the parent's social media activity data into the generation AI and cause the generation AI to select related information.

[0084] The reception unit can customize the reception method by reflecting the parent's past feedback when receiving information. For example, the reception unit customizes the reception method by reflecting the parent's past feedback when receiving information. For example, the reception unit adjusts the reception method based on feedback provided by the parent in the past. The reception unit can also preferentially receive specific training information based on the parent's past feedback. For example, the reception unit analyzes the parent's past feedback and suggests an optimal reception method. This allows the reception method to be optimized by reflecting the parent's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the parent's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0085] The analysis unit can estimate the parent's emotions and adjust the way the analysis is presented based on the estimated parent's emotions. For example, the analysis unit can estimate the parent's emotions and adjust the way the analysis is presented based on the estimated parent's emotions. For example, if the parent is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the parent is relaxed. For example, if the parent is in a hurry, the analysis unit provides a summary analysis result. By adjusting the way the analysis is presented based on the parent's emotions, it is possible to provide an analysis result that is easy for the parent to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the parent's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the child's muscle strength or technique. For example, if the child's muscle strength is important, the analysis unit provides detailed analysis results related to muscle strength. Furthermore, if the child's technique is important, the analysis unit can also provide detailed analysis results related to technique. For example, if both the child's muscle strength and technique are important, the analysis unit provides balanced analysis results. In this way, by adjusting the level of detail of the analysis based on the importance of the child's muscle strength or technique, appropriate analysis results can be provided. 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 child's muscle strength and technique data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the child's sports category. For example, in the case of baseball, the analysis unit applies analysis algorithms for pitching form and batting form. The analysis unit can also apply analysis algorithms for dribbling and shooting in the case of soccer. For example, the analysis unit applies analysis algorithms for shooting form and dribbling in the case of basketball. By applying an analysis algorithm according to the sports category, more accurate analysis results can be provided. 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 category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the parent's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the analysis results provided by the parent in the past. The analysis unit can also preferentially apply a specific analysis method based on the parent's past analysis results. For example, the analysis unit analyzes the parent's past analysis results and proposes the optimal analysis method. This makes it possible to improve the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the parent's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0089] The analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated parent's emotions. For example, the analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated parent's emotions. For example, if the parent is nervous, the analysis unit can provide a short, concise analysis result. Furthermore, if the parent is relaxed, the analysis unit can provide a detailed analysis result. For example, if the parent is in a hurry, the analysis unit can quickly provide the analysis result. By adjusting the length of the analysis according to the parent's emotions, an appropriate analysis result can be provided for the parent. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0090] During analysis, the analysis unit can determine analysis priorities based on the child's training history. For example, the analysis unit prioritizes analysis of important training items based on the child's past training history. The analysis unit can also prioritize analysis of specific training items based on the child's training history. For example, the analysis unit analyzes the child's training history and suggests an optimal analysis order. This allows important items to be analyzed preferentially by determining analysis priorities based on the child's training history. 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 child's training history data into the generation AI and have the generation AI determine the analysis priorities.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the child's related data. For example, the analysis unit prioritizes analysis related to muscle strength based on the child's muscle strength data. The analysis unit can also prioritize analysis related to technique based on the child's technical data. For example, the analysis unit analyzes the child's related data and proposes an optimal analysis order. This enables efficient analysis by adjusting the analysis order based on the child's related data. 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 child's related data into the generation AI and have the generation AI adjust the analysis order.

[0092] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the parent's level of expertise. For example, if the parent has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the parent does not have technical expertise, the analysis unit can provide the analysis results in simple language. For example, the analysis unit can suggest an optimal way to express the analysis results based on the parent's level of expertise. This allows the analysis results to be provided that are easy for the parent to understand by adjusting the use of technical terminology in the analysis according to the parent's level of expertise. 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 parent's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0093] The generation unit can estimate the parent's emotions and adjust the presentation of the training plan based on the estimated parent's emotions. The generation unit, for example, estimates the parent's emotions and adjusts the presentation of the training plan based on the estimated parent's emotions. For example, if the parent is nervous, the generation unit provides a simple, highly visible training plan. The generation unit can also provide a detailed training plan if the parent is relaxed. For example, if the parent is in a hurry, the generation unit provides a training plan that focuses on the main points. By adjusting the presentation of the training plan according to the parent's emotions, a plan that is easy for the parent to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using an AI, or may be performed without an AI. For example, the generation unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0094] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the importance of the child's muscle strength and technique. For example, if the child's muscle strength is important, the generation unit can provide a plan that emphasizes muscle strength training. Also, if the child's technique is important, the generation unit can provide a plan that emphasizes technique practice. For example, if both the child's muscle strength and technique are important, the generation unit can provide a balanced plan. In this way, an appropriate training plan can be provided by adjusting the level of detail of the plan based on the importance of the child's muscle strength and technique. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's muscle strength and technique data into the generation AI and have the generation AI adjust the level of detail of the plan.

[0095] When generating a training plan, the generation unit can apply different plan generation algorithms depending on the child's sport category. For example, in the case of baseball, the generation unit provides a plan that focuses on improving pitching form and batting form. The generation unit can also provide a plan that focuses on improving dribbling and shooting in the case of soccer. For example, the generation unit provides a plan that focuses on improving shooting form and dribbling in the case of basketball. This allows for a more appropriate training plan to be provided by applying a plan generation algorithm depending on the sport category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sport category data into the generation AI and cause the generation AI to apply the plan generation algorithm.

[0096] When generating a training plan, the generation unit can improve the accuracy of the plan by referring to the parent's past plan results. The generation unit, for example, adjusts the plan generation algorithm based on the plan results provided by the parent in the past. The generation unit can also preferentially apply specific training items based on the parent's past plan results. For example, the generation unit analyzes the parent's past plan results and proposes an optimal plan generation method. This makes it possible to improve the accuracy of the plan by referring to the past plan results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the parent's past plan result data into the generation AI and have the generation AI improve the accuracy of the plan.

[0097] The generation unit can estimate the parent's emotions and adjust the length of the training plan based on the estimated parent's emotions. The generation unit, for example, estimates the parent's emotions and adjusts the length of the training plan based on the estimated parent's emotions. For example, if the parent is nervous, the generation unit provides a short and concise training plan. The generation unit can also provide a detailed training plan if the parent is relaxed. For example, if the parent is in a hurry, the generation unit provides a training plan that can be quickly implemented. This allows the length of the training plan to be adjusted according to the parent's emotions, thereby providing an appropriate plan for the parent. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using an AI, or may be performed without an AI. For example, the generation unit may input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0098] When generating a training plan, the generation unit can determine the priority of the plan based on the child's training history. For example, the generation unit can prioritize important training items based on the child's past training history and incorporate them into the plan. The generation unit can also prioritize specific training items based on the child's training history. For example, the generation unit can analyze the child's training history and propose an optimal plan order. This allows important items to be prioritized and incorporated into the plan. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the child's training history data into the generation AI and have the generation AI determine the plan priorities.

[0099] When generating a training plan, the generation unit can adjust the order of the plan based on the child's related data. For example, the generation unit can prioritize strength training in the plan based on the child's muscle strength data. The generation unit can also prioritize technical practice in the plan based on the child's technical data. For example, the generation unit can analyze the child's related data and propose an optimal plan order. This allows for an efficient training plan to be provided by adjusting the plan order based on the child's related data. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the child's related data into the generation AI and have the generation AI adjust the order of the plan.

[0100] When generating a training plan, the generation unit can adjust the use of technical terminology in the plan according to the parent's level of expertise. For example, if the parent has technical expertise, the generation unit can provide the plan using detailed technical terminology. Alternatively, if the parent does not have technical expertise, the generation unit can provide the plan in simple language. For example, the generation unit can suggest an optimal way to express the plan based on the parent's level of expertise. This allows the parent to be provided with a plan that is easy for the parent to understand by adjusting the use of technical terminology in the plan according to the parent's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the parent's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0101] The providing unit can estimate the parent's emotions and adjust the method of providing the training plan based on the estimated parent's emotions. The providing unit, for example, estimates the parent's emotions and adjusts the method of providing the training plan based on the estimated parent's emotions. For example, if the parent is nervous, the providing unit provides the plan in a simple, highly visible manner. Furthermore, if the parent is relaxed, the providing unit can provide the plan in a manner that includes detailed information. For example, if the parent is in a hurry, the providing unit provides the plan in a manner that can be quickly understood. This allows the plan to be provided in a manner that is easy for the parent to understand by adjusting the method of providing the training plan according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit may input parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0102] When providing a training plan, the providing unit can adjust the level of detail of the plan based on the importance of the child's muscle strength and technique. For example, if the child's muscle strength is important, the providing unit can provide a detailed plan that emphasizes muscle strength training. Furthermore, if the child's technique is important, the providing unit can provide a detailed plan that emphasizes technique practice. For example, if both the child's muscle strength and technique are important, the providing unit can provide a balanced detailed plan. This allows an appropriate training plan to be provided by adjusting the level of detail of the plan based on the importance of the child's muscle strength and technique. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's muscle strength and technique data into the generating AI and cause the generating AI to adjust the level of detail of the plan.

[0103] When providing a training plan, the providing unit can apply different providing algorithms depending on the child's sports category. For example, in the case of baseball, the providing unit can provide a plan that focuses on improving pitching form and batting form. In addition, in the case of soccer, the providing unit can also provide a plan that focuses on improving dribbling and shooting. For example, in the case of basketball, the providing unit can provide a plan that focuses on improving shooting form and dribbling. In this way, by applying a providing algorithm according to the sports category, a more appropriate training plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input sports category data into the generation AI and cause the generation AI to apply the providing algorithm.

[0104] When providing a training plan, the providing unit can improve the accuracy of the provision by referring to the parent's past provision results. For example, the providing unit adjusts the provision algorithm based on the plan results provided by the parent in the past. The providing unit can also preferentially apply specific training items based on the parent's past provision results. For example, the providing unit analyzes the parent's past provision results and proposes an optimal provision method. This makes it possible to improve the accuracy of the provision by referring to the past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the parent's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0105] The providing unit can estimate the parent's emotions and adjust the length of the training plan provided based on the estimated parent's emotions. The providing unit, for example, estimates the parent's emotions and adjusts the length of the training plan provided based on the estimated parent's emotions. For example, if the parent is nervous, the providing unit provides a short, concise plan. The providing unit can also provide a detailed plan if the parent is relaxed. For example, if the parent is in a hurry, the providing unit provides a plan that can be quickly implemented. This allows the length of the training plan provided to be adjusted according to the parent's emotions, thereby providing an appropriate plan for the parent. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit may input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0106] When providing a training plan, the providing unit can determine the priority of provision based on the child's training history. For example, the providing unit prioritizes important training items based on the child's past training history to incorporate them into the plan. The providing unit can also prioritize specific training items to incorporate into the plan based on the child's training history. For example, the providing unit analyzes the child's training history and proposes an optimal plan order. This allows important items to be prioritized and incorporated into the plan. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's training history data into the generating AI and have the generating AI determine the priority of provision.

[0107] When providing a training plan, the providing unit can adjust the order of provision based on the child's related data. For example, the providing unit prioritizes incorporating strength training into the plan based on the child's muscle strength data. The providing unit can also prioritize technical practice into the plan based on the child's technical data. For example, the providing unit analyzes the child's related data and proposes an optimal plan order. This makes it possible to provide an efficient training plan by adjusting the order of provision based on the child's related data. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's related data into a generating AI and cause the generating AI to adjust the order of provision.

[0108] When providing a training plan, the providing unit can adjust the use of technical terminology in the provided plan according to the parent's level of expertise. For example, if the parent has technical expertise, the providing unit can provide the plan using detailed technical terminology. Alternatively, if the parent does not have technical expertise, the providing unit can provide the plan in simple language. For example, the providing unit can suggest an optimal way to express the plan based on the parent's level of expertise. This allows the provision of a plan that is easy for the parent to understand by adjusting the use of technical terminology in the provided plan according to the parent's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the parent's level of expertise data into a generating AI and cause the generating AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive information about the child from the parent using the reception device 38 of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and grasp the child's current muscle strength and technique. For example, the generation unit generates an optimal training plan using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the training plan to the parent using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive information about the child from the parent using the microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and grasp the child's current muscle strength and technique. For example, the generation unit generates an optimal training plan using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the training plan to the parent using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive information about the child from the parent using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and grasps the child's current muscle strength and technique. For example, the generation unit generates an optimal training plan using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the training plan to the parent using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive information about the child from the parent using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and grasps the child's current muscle strength and technique. For example, the generation unit generates an optimal training plan using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the training plan to the parent using the speaker 240 of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can estimate the parent's emotions and adjust the information reception method based on the estimated parent's emotions. For example, if the parent is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the parent is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the parent is in a hurry, voice input can be prioritized to allow information to be entered quickly. In this way, the burden on the parent can be reduced by adjusting the information reception method according to the parent's emotions.

[0111] The analysis unit can analyze a child's past training history and select the optimal analysis algorithm. For example, it can prioritize the application of frequently used analysis methods based on past training history. It can also automatically display specific training menus as candidates based on past training history. It can also suggest analysis methods to use during specific time periods. This allows for efficient analysis by selecting the optimal analysis method based on past training history.

[0112] The generation unit can estimate the parent's emotions and adjust the way the training plan is presented based on the estimated parent's emotions. For example, if the parent is nervous, a simple, highly visible training plan can be provided. If the parent is relaxed, a detailed training plan can be provided. Furthermore, if the parent is in a hurry, a training plan that focuses on the main points can be provided. In this way, by adjusting the way the training plan is presented according to the parent's emotions, a plan that is easy for the parent to understand can be provided.

[0113] The provision unit can estimate the parent's emotions and adjust the way in which the training plan is provided based on the estimated parent's emotions. For example, if the parent is nervous, the plan can be provided in a simple, highly visible manner. If the parent is relaxed, the plan can be provided in a manner that includes detailed information. Furthermore, if the parent is in a hurry, the plan can be provided in a manner that is quickly understandable. In this way, by adjusting the way in which the training plan is provided according to the parent's emotions, the plan can be provided in a way that is easy for the parent to understand.

[0114] The analysis unit can estimate the parent's emotions and adjust the way the analysis is presented based on the estimated parent's emotions. For example, if the parent is nervous, it can provide simple, highly visible analysis results. If the parent is relaxed, it can provide detailed analysis results. Furthermore, if the parent is in a hurry, it can provide analysis results that are concise. In this way, by adjusting the way the analysis is presented according to the parent's emotions, it is possible to provide analysis results that are easy for parents to understand.

[0115] When receiving information, the reception unit can filter the information based on the child's current health condition and lifestyle habits. For example, if the child's health condition is not good, a low-impact training menu can be suggested. The reception unit can also suggest an appropriate training time based on the child's lifestyle habits. Furthermore, the reception unit can suggest a nutritionally balanced training menu taking into account the child's eating habits. In this way, by filtering information based on the child's health condition and lifestyle habits, an appropriate training menu can be suggested.

[0116] During analysis, the analysis unit can apply different analysis algorithms depending on the child's sports category. For example, in the case of baseball, an analysis algorithm for pitching form and batting form is applied. In addition, in the case of soccer, an analysis algorithm for dribbling and shooting can be applied. Furthermore, in the case of basketball, an analysis algorithm for shooting form and dribbling is applied. In this way, by applying an analysis algorithm according to the sports category, more accurate analysis results can be provided.

[0117] When generating a training plan, the generation unit can adjust the level of detail of the plan based on the importance of the child's muscle strength or technique. For example, if the child's muscle strength is important, a plan that emphasizes muscle strength training can be provided. Alternatively, if the child's technique is important, a plan that emphasizes technique practice can be provided. Furthermore, if both the child's muscle strength and technique are important, a balanced plan can be provided. In this way, an appropriate training plan can be provided by adjusting the level of detail of the plan based on the importance of the child's muscle strength or technique.

[0118] When providing a training plan, the provider can determine the priority of provision based on the child's training history. For example, important training items can be preferentially incorporated into the plan based on the child's past training history. Also, specific training items can be preferentially incorporated into the plan based on the child's training history. Furthermore, the provider can analyze the child's training history and propose the optimal plan order. In this way, by determining the priority of provision based on the child's training history, important items can be preferentially incorporated into the plan.

[0119] When accepting information, the acceptance unit can analyze the parent's social media activity and accept related information. For example, training information shared by the parent on social media can be accepted with priority. The acceptance unit can also analyze the content of the parent's social media posts and accept related training information. Furthermore, the acceptance unit can accept related training information with reference to the activities of the parent's friends on social media. In this way, by analyzing the parent's social media activity, related information can be accepted efficiently.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit accepts information about the child from the parent. This information includes the child's age, physical strength, skill level, and health condition. The reception unit not only accepts the information entered by the parent in digital format, but also accepts video and image data provided by the parent. For example, the unit has a function that allows parents to upload videos they have taken with their smartphones. Step 2: The analysis unit analyzes the information received by the reception unit and understands the child's current muscle strength and technique. The analysis unit evaluates the child's current muscle strength and technique based on the provided video and data. For example, it can analyze a video of a pitching form and perform a detailed analysis of how the child swings their arm and uses their body. It can also analyze a video of a batting form to analyze the swing trajectory and body movement. Step 3: The generation unit generates an optimal training plan based on the analysis results obtained by the analysis unit. The generation unit generates a training plan that suggests specific muscle training and technique practice based on the analysis results. For example, the generation unit can suggest specific muscle training and technique practice based on areas for improvement in pitching form. It can also suggest swing practice methods based on areas for improvement in batting form. Step 4: The providing unit provides the training plan generated by the generating unit. The providing unit provides the generated training plan to the parent. For example, the training plan can be sent by email or provided via an app notification. The training plan can also be provided as a printed document.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about a child from a parent; an analysis unit that analyzes the information received by the reception unit and grasps the current state of the child's muscle strength or technique; a generation unit that generates a training plan based on the analysis result obtained by the analysis unit; a providing unit that provides the training plan generated by the generating unit. A system characterized by:

2. The reception unit Accept information about the child's age, physical strength, and skill level 2. The system of claim 1.

3. The analysis unit Based on the video or data provided, understand the child's current strength or technique 2. The system of claim 1.

4. The generation unit Generate a training plan based on the analysis, suggesting specific strength or technique exercises 2. The system of claim 1.

5. The providing unit Provide the generated training plan to parents 2. The system of claim 1.

6. The analysis unit Analyze videos of pitching or batting form to identify areas for improvement 2. The system of claim 1.

7. The generation unit Generate a training plan that suggests how to practice your swing 2. The system of claim 1.

8. The reception unit Estimate the parent's emotions and adjust the way information is received based on the estimated parent's emotions 2. The system of claim 1.

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