System

Abstract

An object of a system according to an embodiment is to provide an environment in which a golf swing can be efficiently practiced.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a feedback unit. The collection unit collects swing data. The analysis unit analyzes the swing data collected by the collection unit. The providing unit provides advice based on the analysis result obtained by the analyzing unit. The feedback unit provides visual feedback based on the advice provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have room for improvement due to time and location constraints for efficiently practicing a golf swing.

[0005] The system according to the embodiment aims to provide an environment in which golf swings can be practiced efficiently. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a feedback unit. The collection unit collects swing data. The analysis unit analyzes the swing data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The feedback unit provides visual feedback based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which golf swings can be practiced efficiently. [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 golf swing diagnosis system according to an embodiment of the present invention is a system that diagnoses a golf swing using AI in conjunction with a smartphone camera. In this golf swing diagnosis system, a user uses a smartphone camera to film their swing, and the AI ​​analyzes the swing and provides a diagnosis result. Furthermore, by incorporating a chatbot, accurate and immediate advice on the swing can be provided. Furthermore, by using AI glasses, visual analysis and lessons are possible, and audio can also be heard through the AI ​​glasses. Furthermore, by utilizing generative AI, visual lessons on correct swings can be provided through video. For example, in a golf swing diagnosis system, a user films their swing using a smartphone camera. The smartphone camera records the user's swing in detail and transmits the data to an AI. The AI ​​then analyzes the transmitted swing data. The AI ​​analyzes each step of the swing in detail and identifies swing problems and areas for improvement. Furthermore, by incorporating a chatbot, accurate and immediate advice on the swing can be provided. The user asks questions about their swing through the chatbot, and the AI ​​responds immediately. Furthermore, by using AI glasses, visual analysis and lessons are possible, and audio can also be heard through the AI ​​glasses. Furthermore, by utilizing generative AI, it is possible to provide visual lessons on correct swings and other aspects through video. This allows the golf swing diagnosis system to allow users to practice at any time, regardless of time. For example, if a user films their swing in their backyard, the video is sent to the AI. The AI ​​then analyzes the transmitted swing data. The AI ​​analyzes each step of the swing in detail to identify swing problems and areas for improvement. For example, it analyzes the swing trajectory, speed, angle, etc., and provides specific advice to the user. Furthermore, by introducing a chatbot, accurate and immediate advice on swings can be provided. Users can ask questions about their swing through the chatbot, and the AI ​​will immediately answer those questions.For example, in response to a question like, "My swing trajectory is bad. How can I improve it?", the AI ​​can provide specific advice. Furthermore, using AI glasses allows for visual analysis and lessons, and audio can also be heard through the AI ​​glasses. For example, a user can receive audio advice such as, "Extend your arms a little more" while swinging. Furthermore, by utilizing generative AI, it becomes possible to provide visual lessons on correct swings through video. The generative AI generates videos of correct swings based on the user's swing data and provides them to the user. This allows users to visually learn the correct swing. Finally, the monthly subscription allows for spot subscriptions, which is expected to attract new users who have given up on golf due to cost concerns. Users can use the service at any time by paying a monthly fee. For example, by paying a monthly fee, they can access services such as swing diagnosis, advice, and lesson videos.

[0029] A golf swing diagnosis system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a visualizing unit. The collection unit allows a user to film their swing using a smartphone camera and collect the resulting swing data. For example, when a user films their swing in their backyard, the collection unit records the footage in detail and transmits it to an AI. The collection unit can also collect data such as the swing trajectory, speed, and angle. The collection unit can also collect the user's swing data in real time. For example, the collection unit can film the swing using a smartphone camera and transmit the data to an AI in real time. The analysis unit analyzes the swing data collected by the collection unit. For example, the analysis unit can analyze each step of the swing in detail to identify problems and areas for improvement in the swing. The analysis unit can also analyze the swing trajectory, speed, angle, and the like and provide specific advice to the user. The analysis unit can also evaluate the user's swing performance based on the swing data. For example, the analysis unit can evaluate whether the swing trajectory is correct and point out areas for improvement to the user. The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the providing unit provides advice on swings through a chatbot. The providing unit can also allow a user to ask a question about a swing through the chatbot, and the AI ​​can instantly answer the question. Furthermore, the providing unit can use a generation AI to generate a video of a correct swing and provide it to the user. For example, the providing unit can use a generation AI to generate a video of a correct swing based on the user's swing data and provide it to the user. The vision unit provides visual feedback based on the advice provided by the providing unit. For example, the vision unit visually feeds back the results of swing analysis in real time using AI glasses. The vision unit can also visually confirm the results of real-time swing analysis by having the user wear the AI ​​glasses. Furthermore, the vision unit can provide advice via voice through the AI ​​glasses. For example, the vision unit provides voice advice such as "stretch your arms out a little more" during a swing.As a result, the golf swing diagnosis system according to the embodiment can efficiently collect, analyze, advise, and provide visual feedback to the user's swing data.

[0030] The providing unit can provide advice on swings through the chatbot. The providing unit provides advice on swings through the chatbot, for example. For example, the providing unit allows a user to ask a question about a swing through the chatbot, and the AI ​​immediately answers the question. The providing unit can also provide specific advice to a user through the chatbot. For example, the providing unit allows the AI ​​to provide specific advice in response to a question such as, "My swing trajectory is bad. How can I improve it?" This allows advice on swings to be provided immediately through the chatbot.

[0031] The vision unit can visually feed back the results of swing analysis in real time using AI glasses. The vision unit, for example, visually feeds back the results of swing analysis in real time using AI glasses. For example, the vision unit visually checks the results of real-time swing analysis by having the user wear the AI ​​glasses. The vision unit can also provide specific feedback to the user through the AI ​​glasses. For example, the vision unit provides audio advice such as "Extend your arms a little more" during the swing. In this way, the use of AI glasses can provide visual feedback in real time.

[0032] The visual unit can provide audio advice through the AI ​​glasses. For example, the visual unit can provide audio advice such as "Extend your arms a little more" during a swing. The visual unit can also provide specific audio advice to the user through the AI ​​glasses. For example, the visual unit can provide audio advice for each step of the swing. As a result, by providing audio advice through the AI ​​glasses, the user can receive audio advice in real time.

[0033] The providing unit can use the generation AI to generate a video of a correct swing and provide it to the user. For example, the providing unit can use the generation AI to generate a video of a correct swing based on the user's swing data and provide it to the user. The providing unit can also use the generation AI to provide the user with a video that shows specific swing improvements. For example, the providing unit can use the generation AI to analyze the trajectory, speed, angle, etc. of the swing and generate a video of a correct swing. In this way, the generation AI can be used to generate a video of a correct swing and provide it to the user.

[0034] The providing unit may adopt a monthly usage business model and enable users to subscribe on a spot basis. The providing unit may adopt, for example, a monthly usage business model and enable users to subscribe on a spot basis. For example, the providing unit allows users to use the service at any time by paying a monthly fee. Furthermore, by adopting a monthly usage business model, the providing unit is expected to acquire new users who had given up golf due to cost concerns. For example, by paying a monthly fee, the providing unit can use services such as swing diagnosis, advice, and lesson videos. In this way, by adopting a monthly usage business model, users can subscribe on a spot basis and it is expected that new users will be acquired.

[0035] The collection unit can analyze the user's past swing data and select the optimal collection method. The collection unit, for example, analyzes the user's past swing data and selects the optimal collection method. For example, the collection unit identifies the most effective collection timing from the user's past swing data and collects data at that timing. The collection unit can also collect data focusing on a specific swing pattern based on the user's past swing data. Furthermore, the collection unit can analyze the user's past swing data and customize the collection method to obtain optimal data. For example, the collection unit adjusts the sensor placement and data collection frequency based on the user's past swing data. In this way, the optimal collection method can be selected by analyzing the user's past swing data.

[0036] The collection unit can filter the swing data based on the user's current physical condition and environment when collecting the swing data. For example, the collection unit filters the swing data based on the user's current physical condition and environment when collecting the swing data. For example, if the user is tired, the collection unit reduces the amount of data to be collected, thereby reducing the burden on the user. Furthermore, if the user is in a comfortable environment, the collection unit can collect detailed data and perform highly accurate analysis. Furthermore, if the user is in good physical condition, the collection unit can expand the range of data to be collected, thereby enabling comprehensive analysis. For example, the collection unit filters based on data such as the user's heart rate, temperature, and humidity. This enables optimal data collection by filtering data based on the user's physical condition and environment.

[0037] When collecting swing data, the collection unit can select the optimal collection means depending on the user's input method. For example, when collecting swing data, the collection unit selects the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects swing data using voice recognition technology. Also, when the user uses text input, the collection unit can collect swing data using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect swing data using image analysis technology. For example, the collection unit collects and analyzes swing data based on the user's voice data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting swing data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting swing data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a golf course, the collection unit prioritizes collecting swing data that is suitable for that environment. Furthermore, when the user is at home, the collection unit can prioritize collecting indoor swing data. Furthermore, when the user is at a practice range, the collection unit can prioritize collecting swing data that is suitable for the practice environment. For example, the collection unit acquires geographical location information based on the user's GPS data and collects highly relevant data based on that information. In this way, highly relevant data can be collected by taking into account the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting swing data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting swing data. For example, the collection unit collects data based on swing videos shared by the user on social media. The collection unit can also analyze the user's golf-related posts on social media to collect related swing data. Furthermore, the collection unit can collect related swing data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes data based on the content of the user's social media posts and the number of likes, and collects related swing data. In this way, related data can be collected by analyzing the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting swing data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting swing data. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past to acquire optimal data. The collection unit can also focus on collecting data for a specific swing pattern based on the user's past feedback. Furthermore, the collection unit can customize the collection timing and means by reflecting the user's past feedback. For example, the collection unit adjusts the collection method based on the user's evaluations and areas for improvement to acquire optimal data. In this way, the collection method can be customized by reflecting the user's past feedback, making it possible to collect optimal data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the swing during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the swing during analysis. For example, the analysis unit performs a detailed analysis of important swings and provides specific points for improvement. The analysis unit can also perform a concise analysis of less important swings and provide basic points for improvement. Furthermore, the analysis unit can focus on and perform a detailed analysis of swings that the user is particularly concerned about. For example, the analysis unit evaluates the importance based on data such as swing speed and angle, and adjusts the level of detail of the analysis based on the evaluation result. In this way, by adjusting the level of detail of the analysis based on the importance of the swing, optimal analysis results can be provided.

[0042] The analysis unit can apply different analysis algorithms depending on the swing category during analysis. For example, the analysis unit applies different analysis algorithms depending on the swing category during analysis. For example, the analysis unit applies a specific analysis algorithm to driver shots to perform a detailed analysis. The analysis unit can also apply a different analysis algorithm to iron shots to perform an appropriate analysis. Furthermore, the analysis unit can apply a dedicated analysis algorithm to putting to perform a highly accurate analysis. For example, the analysis unit selects an analysis algorithm depending on the swing category and performs analysis based on the result. In this way, by applying different analysis algorithms depending on the swing category, optimal analysis results can be provided.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can correct the current analysis results based on the user's past analysis results to improve accuracy. The analysis unit can also extract specific trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past analysis results to improve accuracy. For example, the analysis unit can optimize the analysis algorithm based on past analysis data and evaluation results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on the time when the swing was filmed during analysis. For example, the analysis unit determines the analysis priority based on the time when the swing was filmed during analysis. For example, the analysis unit prioritizes analysis of recently filmed swing data to provide the most recent analysis results. The analysis unit can also prioritize analysis of swing data filmed before a specific event to provide improvements for the event. Furthermore, the analysis unit can prioritize analysis of swing data filmed within a period specified by the user to provide appropriate analysis results. For example, the analysis unit determines the priority based on the date and time when the swing was filmed and performs analysis based on the result. In this way, by determining the analysis priority based on the time when the swing was filmed, optimal analysis results can be provided.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the swing during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the swing during analysis. For example, the analysis unit prioritizes analysis of important swing data and provides detailed analysis results. The analysis unit can also prioritize analysis of highly relevant swing data and provide specific points for improvement. Furthermore, the analysis unit can prioritize analysis of swing data that the user is particularly concerned about and provide appropriate analysis results. For example, the analysis unit adjusts the order of analysis based on the similarity and relevance of the swing data. In this way, by adjusting the order of analysis based on the relevance of the swing, optimal analysis results can be provided.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can explain the analysis results in simple terms. If the user is an intermediate user, the analysis unit can also explain the analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can also explain the analysis results using detailed technical terms. For example, the analysis unit evaluates the user's level of expertise and adjusts the use of technical terms in the analysis based on the evaluation result. In this way, optimal analysis results can be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0047] The providing unit can adjust the level of detail of the advice based on the importance of the swing when providing advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the swing when providing advice. For example, the providing unit provides detailed advice for an important swing. The providing unit can also provide concise advice for a swing that is not so important. Furthermore, the providing unit can provide detailed advice that focuses on a swing that the user is particularly concerned about. For example, the providing unit evaluates the importance based on data such as the speed and angle of the swing, and adjusts the level of detail of the advice based on the result. In this way, by adjusting the level of detail of the advice based on the importance of the swing, optimal advice can be provided.

[0048] The providing unit can apply different advice algorithms depending on the swing category when providing advice. For example, the providing unit applies different advice algorithms depending on the swing category when providing advice. For example, the providing unit applies a specific advice algorithm to driver shots to provide detailed advice. The providing unit can also apply a different advice algorithm to iron shots to provide appropriate advice. Furthermore, the providing unit can apply a dedicated advice algorithm to putting to provide highly accurate advice. For example, the providing unit selects an advice algorithm depending on the swing category and provides advice based on the result. In this way, optimal advice can be provided by applying different advice algorithms depending on the swing category.

[0049] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit corrects current advice based on the user's past advice results to improve accuracy. The providing unit can also extract specific trends from the user's past advice results and reflect them in the current advice. Furthermore, the providing unit can adjust the advice algorithm by referring to the user's past advice results to improve accuracy. For example, the providing unit optimizes the advice algorithm based on past advice data and evaluation results. In this way, the accuracy of advice can be improved by referring to the user's past advice results.

[0050] The providing unit can determine the priority of advice based on the time when the swing was captured when providing advice. For example, the providing unit determines the priority of advice based on the time when the swing was captured when providing advice. For example, the providing unit may preferentially analyze recently captured swing data and provide the latest advice. The providing unit may also preferentially analyze swing data captured before a specific event and provide improvements for the event. Furthermore, the providing unit may preferentially analyze swing data captured within a period specified by the user and provide appropriate advice. For example, the providing unit may determine the priority based on the date and time when the swing was captured and provide advice based on the result. In this way, by determining the priority of advice based on the time when the swing was captured, optimal advice can be provided.

[0051] The providing unit can adjust the order of advice based on the relevance of the swing when providing advice. For example, the providing unit adjusts the order of advice based on the relevance of the swing when providing advice. For example, the providing unit prioritizes analyzing important swing data and provides detailed advice. The providing unit can also prioritize analyzing highly relevant swing data and provide specific points for improvement. Furthermore, the providing unit can prioritize analyzing swing data that the user is particularly concerned about and provide appropriate advice. For example, the providing unit adjusts the order of advice based on the similarity and relevance of the swing data. In this way, optimal advice can be provided by adjusting the order of advice based on the relevance of the swing.

[0052] The providing unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing advice. For example, the providing unit adjusts the use of technical terms in the advice according to the user's level of expertise when providing advice. For example, if the user is a beginner, the providing unit explains the advice in simple terms. Furthermore, if the user is an intermediate user, the providing unit can also explain the advice using appropriate technical terms. Furthermore, if the user is an advanced user, the providing unit can also explain the advice using detailed technical terms. For example, the providing unit evaluates the user's level of expertise and adjusts the use of technical terms in the advice based on the evaluation result. In this way, optimal advice can be provided by adjusting the use of technical terms in the advice according to the user's level of expertise.

[0053] The vision unit may adjust the level of detail of the visual feedback based on the importance of the swing. For example, the vision unit may adjust the level of detail of the visual feedback based on the importance of the swing. For example, the vision unit may provide detailed visual feedback for important swings. The vision unit may also provide concise visual feedback for less important swings. Furthermore, the vision unit may provide focused, detailed visual feedback for swings that the user is particularly concerned about. For example, the vision unit may evaluate the importance based on data such as the speed and angle of the swing, and adjust the level of detail of the feedback based on the evaluation result. In this way, the level of detail of the feedback may be adjusted based on the importance of the swing, thereby providing optimal feedback.

[0054] The vision unit can apply different feedback algorithms depending on the swing category when providing visual feedback. For example, the vision unit can apply different feedback algorithms depending on the swing category when providing visual feedback. For example, the vision unit can apply a specific feedback algorithm to a driver shot to provide detailed visual feedback. The vision unit can also apply a different feedback algorithm to an iron shot to provide appropriate visual feedback. Furthermore, the vision unit can apply a dedicated feedback algorithm to putting to provide highly accurate visual feedback. For example, the vision unit can select a feedback algorithm depending on the swing category and provide feedback based on the result. In this way, optimal feedback can be provided by applying different feedback algorithms depending on the swing category.

[0055] The vision unit can improve the accuracy of the visual feedback by referring to the user's past feedback results. For example, the vision unit can improve the accuracy of the visual feedback by referring to the user's past feedback results. For example, the vision unit can correct the current feedback based on the user's past feedback results to improve accuracy. The vision unit can also extract specific trends from the user's past feedback results and reflect them in the current feedback. Furthermore, the vision unit can adjust the feedback algorithm by referring to the user's past feedback results to improve accuracy. For example, the vision unit can optimize the feedback algorithm based on past feedback data and evaluation results. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results.

[0056] The vision unit may prioritize feedback based on when the swing was captured during visual feedback. For example, the vision unit may prioritize feedback based on when the swing was captured during visual feedback. For example, the vision unit may prioritize analyzing recently captured swing data to provide the latest visual feedback. The vision unit may also prioritize analyzing swing data captured before a specific event to provide improvements for the event. The vision unit may also prioritize analyzing swing data captured within a user-specified period to provide appropriate visual feedback. For example, the vision unit may prioritize based on the date and time the swing was captured and provide feedback based on the results. This allows optimal feedback to be provided by prioritizing feedback based on when the swing was captured.

[0057] The vision unit can adjust the order of feedback based on the relevance of the swing when providing visual feedback. For example, the vision unit adjusts the order of feedback based on the relevance of the swing when providing visual feedback. For example, the vision unit prioritizes analysis of important swing data and provides detailed visual feedback. The vision unit can also prioritize analysis of highly relevant swing data and provide specific improvements. Furthermore, the vision unit can prioritize analysis of swing data that the user is particularly concerned about and provide appropriate visual feedback. For example, the vision unit adjusts the order of feedback based on the similarity or relevance of the swing data. In this way, optimal feedback can be provided by adjusting the order of feedback based on the relevance of the swing.

[0058] The visual unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing visual feedback. For example, the visual unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing visual feedback. For example, if the user is a beginner, the visual unit may explain the feedback in simple terms. If the user is an intermediate user, the visual unit may explain the feedback using moderate technical terms. If the user is an advanced user, the visual unit may explain the feedback using detailed technical terms. For example, the visual unit may evaluate the user's level of expertise and adjust the use of technical terms in the feedback based on the evaluation result. This allows optimal feedback to be provided by adjusting the use of technical terms in the feedback according to the user's level of expertise.

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

[0060] The collection unit can analyze the user's past swing data and select the optimal collection method. For example, the collection unit identifies the most effective collection timing from the user's past swing data and collects data at that timing. The collection unit can also collect data focusing on a specific swing pattern based on the user's past swing data. Furthermore, the collection unit can analyze the user's past swing data and customize the collection method to obtain optimal data. For example, the collection unit adjusts the sensor placement and data collection frequency based on the user's past swing data. In this way, the optimal collection method can be selected by analyzing the user's past swing data.

[0061] When collecting swing data, the collection unit can filter the data based on the user's current physical condition and environment. For example, if the user is tired, the collection unit reduces the amount of data to be collected, thereby reducing the burden on the user. Furthermore, if the user is in a comfortable environment, the collection unit can collect detailed data and perform highly accurate analysis. Furthermore, if the user is in good physical condition, the collection unit can expand the range of data to be collected, enabling comprehensive analysis. For example, the collection unit filters the data based on data such as the user's heart rate, temperature, and humidity. This allows optimal data collection by filtering data based on the user's physical condition and environment.

[0062] When collecting swing data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect swing data using voice recognition technology. Also, if the user uses text input, the collection unit can collect swing data using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect swing data using image analysis technology. For example, the collection unit collects and analyzes swing data based on the user's voice data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0063] When collecting swing data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a golf course, the collection unit can prioritize collecting swing data that is suitable for that environment. Furthermore, when the user is at home, the collection unit can prioritize collecting indoor swing data. Furthermore, when the user is at a driving range, the collection unit can prioritize collecting swing data that is suitable for the practice environment. For example, the collection unit acquires geographical location information based on the user's GPS data and collects highly relevant data based on that information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0064] When collecting swing data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data based on swing videos shared by the user on social media. The collection unit can also analyze the user's golf-related social media posts and collect related swing data. Furthermore, the collection unit can collect related swing data by referring to the activities of the user's friends on social media. For example, the collection unit can analyze data based on the content of the user's social media posts and the number of likes, and collect related swing data. In this way, related data can be collected by analyzing the user's social media activities.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit allows a user to film their swing using a smartphone camera and collects the swing data. For example, if a user films their swing in their backyard, the collection unit records the video in detail and sends it to the AI. The collection unit can also collect data such as the swing's trajectory, speed, and angle. Furthermore, the collection unit can collect the user's swing data in real time. For example, the collection unit can film their swing using a smartphone camera and send the data to the AI ​​in real time. Step 2: The analysis unit analyzes the swing data collected by the collection unit. For example, the analysis unit analyzes each step of the swing in detail to identify problems with the swing and areas for improvement. The analysis unit can also analyze the swing trajectory, speed, angle, etc., and provide specific advice to the user. Furthermore, the analysis unit can evaluate the user's swing performance based on the swing data. For example, the analysis unit can evaluate whether the swing trajectory is correct and point out areas for improvement to the user. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit provides advice on swings through a chatbot. The providing unit can also allow a user to ask a question about a swing through the chatbot, and the AI ​​can immediately answer the question. Furthermore, the providing unit can use a generating AI to generate a video of a correct swing and provide it to the user. For example, the providing unit can use a generating AI to generate a video of a correct swing based on the user's swing data and provide it to the user. Step 4: The feedback unit provides visual feedback based on the advice provided by the providing unit. For example, the feedback unit visually feeds back the results of swing analysis in real time using AI glasses. The feedback unit can also visually confirm the results of real-time swing analysis by having the user wear the AI ​​glasses. Furthermore, the feedback unit can also provide advice via voice through the AI ​​glasses. For example, the feedback unit provides voice advice such as "Stretch your arms out a little more" during the swing.

[0067] (Example 2) A golf swing diagnosis system according to an embodiment of the present invention is a system that diagnoses a golf swing using AI in conjunction with a smartphone camera. In this golf swing diagnosis system, a user uses a smartphone camera to film their swing, and the AI ​​analyzes the swing and provides a diagnosis result. Furthermore, by incorporating a chatbot, accurate and immediate advice on the swing can be provided. Furthermore, by using AI glasses, visual analysis and lessons are possible, and audio can also be heard through the AI ​​glasses. Furthermore, by utilizing generative AI, visual lessons on correct swings can be provided through video. For example, in a golf swing diagnosis system, a user films their swing using a smartphone camera. The smartphone camera records the user's swing in detail and transmits the data to an AI. The AI ​​then analyzes the transmitted swing data. The AI ​​analyzes each step of the swing in detail and identifies swing problems and areas for improvement. Furthermore, by incorporating a chatbot, accurate and immediate advice on the swing can be provided. The user asks questions about their swing through the chatbot, and the AI ​​responds immediately. Furthermore, by using AI glasses, visual analysis and lessons are possible, and audio can also be heard through the AI ​​glasses. Furthermore, by utilizing generative AI, it is possible to provide visual lessons on correct swings and other aspects through video. This allows the golf swing diagnosis system to allow users to practice at any time, regardless of time. For example, if a user films their swing in their backyard, the video is sent to the AI. The AI ​​then analyzes the transmitted swing data. The AI ​​analyzes each step of the swing in detail to identify swing problems and areas for improvement. For example, it analyzes the swing trajectory, speed, angle, etc., and provides specific advice to the user. Furthermore, by introducing a chatbot, accurate and immediate advice on swings can be provided. Users can ask questions about their swing through the chatbot, and the AI ​​will immediately answer those questions.For example, in response to a question like, "My swing trajectory is bad. How can I improve it?", the AI ​​can provide specific advice. Furthermore, using AI glasses allows for visual analysis and lessons, and audio can also be heard through the AI ​​glasses. For example, a user can receive audio advice such as, "Extend your arms a little more" while swinging. Furthermore, by utilizing generative AI, it becomes possible to provide visual lessons on correct swings through video. The generative AI generates videos of correct swings based on the user's swing data and provides them to the user. This allows users to visually learn the correct swing. Finally, the monthly subscription allows for spot subscriptions, which is expected to attract new users who have given up on golf due to cost concerns. Users can use the service at any time by paying a monthly fee. For example, by paying a monthly fee, they can access services such as swing diagnosis, advice, and lesson videos.

[0068] A golf swing diagnosis system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a visualizing unit. The collection unit allows a user to film their swing using a smartphone camera and collect the resulting swing data. For example, when a user films their swing in their backyard, the collection unit records the footage in detail and transmits it to an AI. The collection unit can also collect data such as the swing trajectory, speed, and angle. The collection unit can also collect the user's swing data in real time. For example, the collection unit can film the swing using a smartphone camera and transmit the data to an AI in real time. The analysis unit analyzes the swing data collected by the collection unit. For example, the analysis unit can analyze each step of the swing in detail to identify problems and areas for improvement in the swing. The analysis unit can also analyze the swing trajectory, speed, angle, and the like and provide specific advice to the user. The analysis unit can also evaluate the user's swing performance based on the swing data. For example, the analysis unit can evaluate whether the swing trajectory is correct and point out areas for improvement to the user. The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the providing unit provides advice on swings through a chatbot. The providing unit can also allow a user to ask a question about a swing through the chatbot, and the AI ​​can instantly answer the question. Furthermore, the providing unit can use a generation AI to generate a video of a correct swing and provide it to the user. For example, the providing unit can use a generation AI to generate a video of a correct swing based on the user's swing data and provide it to the user. The vision unit provides visual feedback based on the advice provided by the providing unit. For example, the vision unit visually feeds back the results of swing analysis in real time using AI glasses. The vision unit can also visually confirm the results of real-time swing analysis by having the user wear the AI ​​glasses. Furthermore, the vision unit can provide advice via voice through the AI ​​glasses. For example, the vision unit provides voice advice such as "stretch your arms out a little more" during a swing.As a result, the golf swing diagnosis system according to the embodiment can efficiently collect, analyze, advise, and provide visual feedback to the user's swing data.

[0069] The providing unit can provide advice on swings through the chatbot. The providing unit provides advice on swings through the chatbot, for example. For example, the providing unit allows a user to ask a question about a swing through the chatbot, and the AI ​​immediately answers the question. The providing unit can also provide specific advice to a user through the chatbot. For example, the providing unit allows the AI ​​to provide specific advice in response to a question such as, "My swing trajectory is bad. How can I improve it?" This allows advice on swings to be provided immediately through the chatbot.

[0070] The vision unit can visually feed back the results of swing analysis in real time using AI glasses. The vision unit, for example, visually feeds back the results of swing analysis in real time using AI glasses. For example, the vision unit visually checks the results of real-time swing analysis by having the user wear the AI ​​glasses. The vision unit can also provide specific feedback to the user through the AI ​​glasses. For example, the vision unit provides audio advice such as "Extend your arms a little more" during the swing. In this way, the use of AI glasses can provide visual feedback in real time.

[0071] The visual unit can provide audio advice through the AI ​​glasses. For example, the visual unit can provide audio advice such as "Extend your arms a little more" during a swing. The visual unit can also provide specific audio advice to the user through the AI ​​glasses. For example, the visual unit can provide audio advice for each step of the swing. As a result, by providing audio advice through the AI ​​glasses, the user can receive audio advice in real time.

[0072] The providing unit can use the generation AI to generate a video of a correct swing and provide it to the user. For example, the providing unit can use the generation AI to generate a video of a correct swing based on the user's swing data and provide it to the user. The providing unit can also use the generation AI to provide the user with a video that shows specific swing improvements. For example, the providing unit can use the generation AI to analyze the trajectory, speed, angle, etc. of the swing and generate a video of a correct swing. In this way, the generation AI can be used to generate a video of a correct swing and provide it to the user.

[0073] The providing unit may adopt a monthly usage business model and enable users to subscribe on a spot basis. The providing unit may adopt, for example, a monthly usage business model and enable users to subscribe on a spot basis. For example, the providing unit allows users to use the service at any time by paying a monthly fee. Furthermore, by adopting a monthly usage business model, the providing unit is expected to acquire new users who had given up golf due to cost concerns. For example, by paying a monthly fee, the providing unit can use services such as swing diagnosis, advice, and lesson videos. In this way, by adopting a monthly usage business model, users can subscribe on a spot basis and it is expected that new users will be acquired.

[0074] The collection unit can estimate the user's emotions and adjust the timing of collecting swing data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting swing data based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects swing data frequently to obtain detailed data. Furthermore, when the user is feeling stressed, the collection unit can reduce the amount of swing data collected to reduce the burden on the user. Furthermore, when the user is concentrating, the collection unit can collect swing data at a timely pace to obtain optimal data. For example, the collection unit estimates the user's emotions using facial expression recognition or voice analysis and adjusts the timing of collection based on the results. In this way, optimal data collection is possible by adjusting the timing of collecting swing data based on the user's emotions.

[0075] The collection unit can analyze the user's past swing data and select the optimal collection method. The collection unit, for example, analyzes the user's past swing data and selects the optimal collection method. For example, the collection unit identifies the most effective collection timing from the user's past swing data and collects data at that timing. The collection unit can also collect data focusing on a specific swing pattern based on the user's past swing data. Furthermore, the collection unit can analyze the user's past swing data and customize the collection method to obtain optimal data. For example, the collection unit adjusts the sensor placement and data collection frequency based on the user's past swing data. In this way, the optimal collection method can be selected by analyzing the user's past swing data.

[0076] The collection unit can filter the swing data based on the user's current physical condition and environment when collecting the swing data. For example, the collection unit filters the swing data based on the user's current physical condition and environment when collecting the swing data. For example, if the user is tired, the collection unit reduces the amount of data to be collected, thereby reducing the burden on the user. Furthermore, if the user is in a comfortable environment, the collection unit can collect detailed data and perform highly accurate analysis. Furthermore, if the user is in good physical condition, the collection unit can expand the range of data to be collected, thereby enabling comprehensive analysis. For example, the collection unit filters based on data such as the user's heart rate, temperature, and humidity. This enables optimal data collection by filtering data based on the user's physical condition and environment.

[0077] When collecting swing data, the collection unit can select the optimal collection means depending on the user's input method. For example, when collecting swing data, the collection unit selects the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects swing data using voice recognition technology. Also, when the user uses text input, the collection unit can collect swing data using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect swing data using image analysis technology. For example, the collection unit collects and analyzes swing data based on the user's voice data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0078] The collection unit can estimate the user's emotions and determine the priority of the swing data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the swing data to be collected based on the estimated user emotions. For example, the collection unit prioritizes collecting detailed swing data when the user is relaxed. The collection unit can also prioritize collecting basic swing data when the user is feeling stressed. Furthermore, the collection unit can prioritize collecting data for a specific swing pattern when the user is concentrating. For example, the collection unit estimates the user's emotions using facial expression recognition or voice analysis and determines the priority of the data to be collected based on the result. In this way, by prioritizing the swing data based on the user's emotions, optimal data collection is possible.

[0079] When collecting swing data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting swing data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a golf course, the collection unit prioritizes collecting swing data that is suitable for that environment. Furthermore, when the user is at home, the collection unit can prioritize collecting indoor swing data. Furthermore, when the user is at a practice range, the collection unit can prioritize collecting swing data that is suitable for the practice environment. For example, the collection unit acquires geographical location information based on the user's GPS data and collects highly relevant data based on that information. In this way, highly relevant data can be collected by taking into account the user's geographical location information.

[0080] The collection unit can analyze the user's social media activities and collect related data when collecting swing data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting swing data. For example, the collection unit collects data based on swing videos shared by the user on social media. The collection unit can also analyze the user's golf-related posts on social media to collect related swing data. Furthermore, the collection unit can collect related swing data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes data based on the content of the user's social media posts and the number of likes, and collects related swing data. In this way, related data can be collected by analyzing the user's social media activities.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting swing data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting swing data. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past to acquire optimal data. The collection unit can also focus on collecting data for a specific swing pattern based on the user's past feedback. Furthermore, the collection unit can customize the collection timing and means by reflecting the user's past feedback. For example, the collection unit adjusts the collection method based on the user's evaluations and areas for improvement to acquire optimal data. In this way, the collection method can be customized by reflecting the user's past feedback, making it possible to collect optimal data.

[0082] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the method of expressing the analysis based on the estimated user's emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. Furthermore, the analysis unit can provide detailed analysis results for a specific swing pattern when the user is concentrating. For example, the analysis unit estimates the user's emotions using facial expression recognition or voice analysis and adjusts the method of expressing the analysis based on the results. In this way, optimal analysis results can be provided by adjusting the method of expressing the analysis based on the user's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the swing during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the swing during analysis. For example, the analysis unit performs a detailed analysis of important swings and provides specific points for improvement. The analysis unit can also perform a concise analysis of less important swings and provide basic points for improvement. Furthermore, the analysis unit can focus on and perform a detailed analysis of swings that the user is particularly concerned about. For example, the analysis unit evaluates the importance based on data such as swing speed and angle, and adjusts the level of detail of the analysis based on the evaluation result. In this way, by adjusting the level of detail of the analysis based on the importance of the swing, optimal analysis results can be provided.

[0084] The analysis unit can apply different analysis algorithms depending on the swing category during analysis. For example, the analysis unit applies different analysis algorithms depending on the swing category during analysis. For example, the analysis unit applies a specific analysis algorithm to driver shots to perform a detailed analysis. The analysis unit can also apply a different analysis algorithm to iron shots to perform an appropriate analysis. Furthermore, the analysis unit can apply a dedicated analysis algorithm to putting to perform a highly accurate analysis. For example, the analysis unit selects an analysis algorithm depending on the swing category and performs analysis based on the result. In this way, by applying different analysis algorithms depending on the swing category, optimal analysis results can be provided.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can correct the current analysis results based on the user's past analysis results to improve accuracy. The analysis unit can also extract specific trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past analysis results to improve accuracy. For example, the analysis unit can optimize the analysis algorithm based on past analysis data and evaluation results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer analysis result. Also, if the user is stressed, the analysis unit can perform a concise analysis and provide a shorter analysis result. Furthermore, if the user is concentrating, the analysis unit can perform a detailed analysis of a specific swing pattern and provide an analysis result of an appropriate length. For example, the analysis unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the length of the analysis based on the result. In this way, by adjusting the length of the analysis based on the user's emotions, optimal analysis results can be provided.

[0087] The analysis unit can determine the analysis priority based on the time when the swing was filmed during analysis. For example, the analysis unit determines the analysis priority based on the time when the swing was filmed during analysis. For example, the analysis unit prioritizes analysis of recently filmed swing data to provide the most recent analysis results. The analysis unit can also prioritize analysis of swing data filmed before a specific event to provide improvements for the event. Furthermore, the analysis unit can prioritize analysis of swing data filmed within a period specified by the user to provide appropriate analysis results. For example, the analysis unit determines the priority based on the date and time when the swing was filmed and performs analysis based on the result. In this way, by determining the analysis priority based on the time when the swing was filmed, optimal analysis results can be provided.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the swing during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the swing during analysis. For example, the analysis unit prioritizes analysis of important swing data and provides detailed analysis results. The analysis unit can also prioritize analysis of highly relevant swing data and provide specific points for improvement. Furthermore, the analysis unit can prioritize analysis of swing data that the user is particularly concerned about and provide appropriate analysis results. For example, the analysis unit adjusts the order of analysis based on the similarity and relevance of the swing data. In this way, by adjusting the order of analysis based on the relevance of the swing, optimal analysis results can be provided.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can explain the analysis results in simple terms. If the user is an intermediate user, the analysis unit can also explain the analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can also explain the analysis results using detailed technical terms. For example, the analysis unit evaluates the user's level of expertise and adjusts the use of technical terms in the analysis based on the evaluation result. In this way, optimal analysis results can be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0090] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the way in which advice is expressed based on the estimated user's emotions. For example, the providing unit can provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide concise and to-the-point advice when the user is feeling stressed. Furthermore, the providing unit can provide detailed advice for a specific swing pattern when the user is concentrating. For example, the providing unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the way in which advice is expressed based on the results. In this way, optimal advice can be provided by adjusting the way in which advice is expressed based on the user's emotions.

[0091] The providing unit can adjust the level of detail of the advice based on the importance of the swing when providing advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the swing when providing advice. For example, the providing unit provides detailed advice for an important swing. The providing unit can also provide concise advice for a swing that is not so important. Furthermore, the providing unit can provide detailed advice that focuses on a swing that the user is particularly concerned about. For example, the providing unit evaluates the importance based on data such as the speed and angle of the swing, and adjusts the level of detail of the advice based on the result. In this way, by adjusting the level of detail of the advice based on the importance of the swing, optimal advice can be provided.

[0092] The providing unit can apply different advice algorithms depending on the swing category when providing advice. For example, the providing unit applies different advice algorithms depending on the swing category when providing advice. For example, the providing unit applies a specific advice algorithm to driver shots to provide detailed advice. The providing unit can also apply a different advice algorithm to iron shots to provide appropriate advice. Furthermore, the providing unit can apply a dedicated advice algorithm to putting to provide highly accurate advice. For example, the providing unit selects an advice algorithm depending on the swing category and provides advice based on the result. In this way, optimal advice can be provided by applying different advice algorithms depending on the swing category.

[0093] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit corrects current advice based on the user's past advice results to improve accuracy. The providing unit can also extract specific trends from the user's past advice results and reflect them in the current advice. Furthermore, the providing unit can adjust the advice algorithm by referring to the user's past advice results to improve accuracy. For example, the providing unit optimizes the advice algorithm based on past advice data and evaluation results. In this way, the accuracy of advice can be improved by referring to the user's past advice results.

[0094] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the length of advice based on the estimated user's emotions. For example, the providing unit can provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide concise and to-the-point advice when the user is feeling stressed. Furthermore, the providing unit can provide detailed advice for a specific swing pattern when the user is concentrating. For example, the providing unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the length of advice based on the result. In this way, optimal advice can be provided by adjusting the length of advice based on the user's emotions.

[0095] The providing unit can determine the priority of advice based on the time when the swing was captured when providing advice. For example, the providing unit determines the priority of advice based on the time when the swing was captured when providing advice. For example, the providing unit may preferentially analyze recently captured swing data and provide the latest advice. The providing unit may also preferentially analyze swing data captured before a specific event and provide improvements for the event. Furthermore, the providing unit may preferentially analyze swing data captured within a period specified by the user and provide appropriate advice. For example, the providing unit may determine the priority based on the date and time when the swing was captured and provide advice based on the result. In this way, by determining the priority of advice based on the time when the swing was captured, optimal advice can be provided.

[0096] The providing unit can adjust the order of advice based on the relevance of the swing when providing advice. For example, the providing unit adjusts the order of advice based on the relevance of the swing when providing advice. For example, the providing unit prioritizes analyzing important swing data and provides detailed advice. The providing unit can also prioritize analyzing highly relevant swing data and provide specific points for improvement. Furthermore, the providing unit can prioritize analyzing swing data that the user is particularly concerned about and provide appropriate advice. For example, the providing unit adjusts the order of advice based on the similarity and relevance of the swing data. In this way, optimal advice can be provided by adjusting the order of advice based on the relevance of the swing.

[0097] The providing unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing advice. For example, the providing unit adjusts the use of technical terms in the advice according to the user's level of expertise when providing advice. For example, if the user is a beginner, the providing unit explains the advice in simple terms. Furthermore, if the user is an intermediate user, the providing unit can also explain the advice using appropriate technical terms. Furthermore, if the user is an advanced user, the providing unit can also explain the advice using detailed technical terms. For example, the providing unit evaluates the user's level of expertise and adjusts the use of technical terms in the advice based on the evaluation result. In this way, optimal advice can be provided by adjusting the use of technical terms in the advice according to the user's level of expertise.

[0098] The vision unit can estimate the user's emotions and adjust the visual feedback method based on the estimated user's emotions. For example, the vision unit can estimate the user's emotions and adjust the visual feedback method based on the estimated user's emotions. For example, the vision unit can provide detailed visual feedback when the user is relaxed. Furthermore, the vision unit can provide concise and to-the-point visual feedback when the user is stressed. Furthermore, the vision unit can provide detailed visual feedback for a specific swing pattern when the user is concentrating. For example, the vision unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the visual feedback method based on the results. In this way, optimal feedback can be provided by adjusting the visual feedback method based on the user's emotions.

[0099] The vision unit may adjust the level of detail of the visual feedback based on the importance of the swing. For example, the vision unit may adjust the level of detail of the visual feedback based on the importance of the swing. For example, the vision unit may provide detailed visual feedback for important swings. The vision unit may also provide concise visual feedback for less important swings. Furthermore, the vision unit may provide focused, detailed visual feedback for swings that the user is particularly concerned about. For example, the vision unit may evaluate the importance based on data such as the speed and angle of the swing, and adjust the level of detail of the feedback based on the evaluation result. In this way, the level of detail of the feedback may be adjusted based on the importance of the swing, thereby providing optimal feedback.

[0100] The vision unit can apply different feedback algorithms depending on the swing category when providing visual feedback. For example, the vision unit can apply different feedback algorithms depending on the swing category when providing visual feedback. For example, the vision unit can apply a specific feedback algorithm to a driver shot to provide detailed visual feedback. The vision unit can also apply a different feedback algorithm to an iron shot to provide appropriate visual feedback. Furthermore, the vision unit can apply a dedicated feedback algorithm to putting to provide highly accurate visual feedback. For example, the vision unit can select a feedback algorithm depending on the swing category and provide feedback based on the result. In this way, optimal feedback can be provided by applying different feedback algorithms depending on the swing category.

[0101] The vision unit can improve the accuracy of the visual feedback by referring to the user's past feedback results. For example, the vision unit can improve the accuracy of the visual feedback by referring to the user's past feedback results. For example, the vision unit can correct the current feedback based on the user's past feedback results to improve accuracy. The vision unit can also extract specific trends from the user's past feedback results and reflect them in the current feedback. Furthermore, the vision unit can adjust the feedback algorithm by referring to the user's past feedback results to improve accuracy. For example, the vision unit can optimize the feedback algorithm based on past feedback data and evaluation results. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results.

[0102] The vision unit can estimate the user's emotion and adjust the length of the visual feedback based on the estimated user's emotion. For example, the vision unit can estimate the user's emotion and adjust the length of the visual feedback based on the estimated user's emotion. For example, the vision unit can provide detailed visual feedback when the user is relaxed. Furthermore, the vision unit can provide concise and to-the-point visual feedback when the user is stressed. Furthermore, the vision unit can provide detailed visual feedback for a specific swing pattern when the user is concentrating. For example, the vision unit can estimate the user's emotion using facial expression recognition or voice analysis and adjust the length of the visual feedback based on the result. In this way, optimal feedback can be provided by adjusting the length of the visual feedback based on the user's emotion.

[0103] The vision unit may prioritize feedback based on when the swing was captured during visual feedback. For example, the vision unit may prioritize feedback based on when the swing was captured during visual feedback. For example, the vision unit may prioritize analyzing recently captured swing data to provide the latest visual feedback. The vision unit may also prioritize analyzing swing data captured before a specific event to provide improvements for the event. The vision unit may also prioritize analyzing swing data captured within a user-specified period to provide appropriate visual feedback. For example, the vision unit may prioritize based on the date and time the swing was captured and provide feedback based on the results. This allows optimal feedback to be provided by prioritizing feedback based on when the swing was captured.

[0104] The vision unit can adjust the order of feedback based on the relevance of the swing when providing visual feedback. For example, the vision unit adjusts the order of feedback based on the relevance of the swing when providing visual feedback. For example, the vision unit prioritizes analysis of important swing data and provides detailed visual feedback. The vision unit can also prioritize analysis of highly relevant swing data and provide specific improvements. Furthermore, the vision unit can prioritize analysis of swing data that the user is particularly concerned about and provide appropriate visual feedback. For example, the vision unit adjusts the order of feedback based on the similarity or relevance of the swing data. In this way, optimal feedback can be provided by adjusting the order of feedback based on the relevance of the swing.

[0105] The visual unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing visual feedback. For example, the visual unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing visual feedback. For example, if the user is a beginner, the visual unit may explain the feedback in simple terms. If the user is an intermediate user, the visual unit may explain the feedback using moderate technical terms. If the user is an advanced user, the visual unit may explain the feedback using detailed technical terms. For example, the visual unit may evaluate the user's level of expertise and adjust the use of technical terms in the feedback based on the evaluation result. This allows optimal feedback to be provided by adjusting the use of technical terms in the feedback according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and visual unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit captures a swing using the camera 42 of the smart device 14 and transmits the data to the data processing device 12. The analysis unit analyzes the swing data using, for example, the specific processing unit 290 of the data processing device 12 and identifies problems and areas for improvement. The provision unit provides advice generated by the specific processing unit 290 of the data processing device 12 to the user via a chatbot, for example. The visual unit provides feedback visually and audibly using, for example, the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and vision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit captures a swing using the camera 42 of the smart glasses 214 and transmits the data to the data processing device 12. The analysis unit analyzes the swing data using, for example, the specific processing unit 290 of the data processing device 12 and identifies problems and areas for improvement. The provision unit provides advice generated by the specific processing unit 290 of the data processing device 12 to the user via a chatbot, for example. The vision unit provides feedback visually and audibly using, for example, the display and speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and visual unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit takes a picture of the swing using the camera 42 of the headset-type terminal 314 and transmits the data to the data processing device 12. The analysis unit analyzes the swing data using, for example, the specific processing unit 290 of the data processing device 12 and identifies problems and areas for improvement. The provision unit provides the user with advice generated by the specific processing unit 290 of the data processing device 12 via a chatbot, for example. The visual unit provides feedback visually and audibly using, for example, the display and speaker of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and vision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit takes a picture of the swing using the camera 42 of the robot 414 and transmits the data to the data processing device 12. The analysis unit analyzes the swing data using, for example, the specific processing unit 290 of the data processing device 12 and identifies problems and areas for improvement. The provision unit provides the user with advice generated by the specific processing unit 290 of the data processing device 12 via a chatbot, for example. The vision unit provides visual and audio feedback using, for example, the display and speaker of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. Furthermore, the analysis unit can provide detailed analysis results for a specific swing pattern when the user is concentrating. For example, the analysis unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the way the analysis is presented based on the results. In this way, the analysis unit can provide optimal analysis results by adjusting the way the analysis is presented based on the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, the providing unit can provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide concise and to-the-point advice when the user is feeling stressed. Furthermore, the providing unit can provide detailed advice for a specific swing pattern when the user is concentrating. For example, the providing unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the way in which advice is expressed based on the results. In this way, optimal advice can be provided by adjusting the way in which advice is expressed based on the user's emotions.

[0109] The vision unit can estimate the user's emotions and adjust the method of visual feedback based on the estimated user's emotions. For example, the vision unit can provide detailed visual feedback when the user is relaxed. The vision unit can also provide concise and to-the-point visual feedback when the user is stressed. Furthermore, the vision unit can provide detailed visual feedback for a specific swing pattern when the user is concentrating. For example, the vision unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the method of visual feedback based on the results. In this way, optimal feedback can be provided by adjusting the method of visual feedback based on the user's emotions.

[0110] The collection unit can estimate the user's emotions and adjust the timing of swing data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects swing data frequently to obtain detailed data. Furthermore, when the user is feeling stressed, the collection unit can reduce the amount of swing data collected to reduce the burden on the user. Furthermore, when the user is concentrating, the collection unit can collect swing data at the appropriate time to obtain optimal data. For example, the collection unit estimates the user's emotions using facial expression recognition or voice analysis and adjusts the collection timing based on the results. In this way, optimal data collection is possible by adjusting the timing of swing data collection based on the user's emotions.

[0111] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer analysis result. Alternatively, if the user is stressed, the analysis unit can perform a concise analysis and provide a shorter analysis result. Furthermore, if the user is concentrating, the analysis unit can perform a detailed analysis of a specific swing pattern and provide an analysis result of an appropriate length. For example, the analysis unit can estimate the user's emotions using facial expression recognition or voice analysis and adjust the length of the analysis based on the result. In this way, by adjusting the length of the analysis based on the user's emotions, optimal analysis results can be provided.

[0112] The collection unit can analyze the user's past swing data and select the optimal collection method. For example, the collection unit identifies the most effective collection timing from the user's past swing data and collects data at that timing. The collection unit can also collect data focusing on a specific swing pattern based on the user's past swing data. Furthermore, the collection unit can analyze the user's past swing data and customize the collection method to obtain optimal data. For example, the collection unit adjusts the sensor placement and data collection frequency based on the user's past swing data. In this way, the optimal collection method can be selected by analyzing the user's past swing data.

[0113] When collecting swing data, the collection unit can filter the data based on the user's current physical condition and environment. For example, if the user is tired, the collection unit reduces the amount of data to be collected, thereby reducing the burden on the user. Furthermore, if the user is in a comfortable environment, the collection unit can collect detailed data and perform highly accurate analysis. Furthermore, if the user is in good physical condition, the collection unit can expand the range of data to be collected, enabling comprehensive analysis. For example, the collection unit filters the data based on data such as the user's heart rate, temperature, and humidity. This allows optimal data collection by filtering data based on the user's physical condition and environment.

[0114] When collecting swing data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect swing data using voice recognition technology. Also, if the user uses text input, the collection unit can collect swing data using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect swing data using image analysis technology. For example, the collection unit collects and analyzes swing data based on the user's voice data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method.

[0115] When collecting swing data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a golf course, the collection unit can prioritize collecting swing data that is suitable for that environment. Furthermore, when the user is at home, the collection unit can prioritize collecting indoor swing data. Furthermore, when the user is at a driving range, the collection unit can prioritize collecting swing data that is suitable for the practice environment. For example, the collection unit acquires geographical location information based on the user's GPS data and collects highly relevant data based on that information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0116] When collecting swing data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data based on swing videos shared by the user on social media. The collection unit can also analyze the user's golf-related social media posts and collect related swing data. Furthermore, the collection unit can collect related swing data by referring to the activities of the user's friends on social media. For example, the collection unit can analyze data based on the content of the user's social media posts and the number of likes, and collect related swing data. In this way, related data can be collected by analyzing the user's social media activities.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit allows a user to film their swing using a smartphone camera and collects the swing data. For example, if a user films their swing in their backyard, the collection unit records the video in detail and sends it to the AI. The collection unit can also collect data such as the swing's trajectory, speed, and angle. Furthermore, the collection unit can collect the user's swing data in real time. For example, the collection unit can film their swing using a smartphone camera and send the data to the AI ​​in real time. Step 2: The analysis unit analyzes the swing data collected by the collection unit. For example, the analysis unit analyzes each step of the swing in detail to identify problems with the swing and areas for improvement. The analysis unit can also analyze the swing trajectory, speed, angle, etc., and provide specific advice to the user. Furthermore, the analysis unit can evaluate the user's swing performance based on the swing data. For example, the analysis unit can evaluate whether the swing trajectory is correct and point out areas for improvement to the user. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. For example, the providing unit provides advice on swings through a chatbot. The providing unit can also allow a user to ask a question about a swing through the chatbot, and the AI ​​can immediately answer the question. Furthermore, the providing unit can use a generating AI to generate a video of a correct swing and provide it to the user. For example, the providing unit can use a generating AI to generate a video of a correct swing based on the user's swing data and provide it to the user. Step 4: The feedback unit provides visual feedback based on the advice provided by the providing unit. For example, the feedback unit visually feeds back the results of swing analysis in real time using AI glasses. The feedback unit can also visually confirm the results of real-time swing analysis by having the user wear the AI ​​glasses. Furthermore, the feedback unit can also provide advice via voice through the AI ​​glasses. For example, the feedback unit provides voice advice such as "Stretch your arms out a little more" during the swing.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects swing data; an analysis unit that analyzes the swing data collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; a feedback unit that provides visual feedback based on the advice provided by the providing unit; Equipped with A system characterized by:

2. The providing unit Providing swing advice via chatbot 2. The system of claim 1.

3. The visual unit is AI glasses are used to visually provide feedback on swing analysis results in real time.

2. The system of claim 1.

4. The visual unit is Providing advice via voice through AI glasses 2. The system of claim 1.

5. The providing unit Using generative AI, we generate videos of correct swings and provide them to users.

2. The system of claim 1.

6. The providing unit Adopting a monthly subscription business model that allows users to subscribe on a spot basis 2. The system of claim 1.

7. The collecting unit To estimate a user's emotion and adjust a timing of collecting swing data based on the estimated user's emotion.

2. The system of claim 1.

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

9. The collecting unit When collecting swing data, filtering is performed based on the user's current physical condition and environment.

2. The system of claim 1.

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