System

Abstract

An object of a system according to an embodiment is to measure a golf swing form in real time and guide an optimal practice method.SOLUTION: A system according to an embodiment includes a camera, a multi-modal AI, and an instructor. The camera is used in conjunction with a golf simulator to measure the player's swing form in real time. The multi-modal AI analyzes the video captured by the camera. The instructor instructs improvement points of the form and an optimal practice method on the basis of a result analyzed by the multimodal AI.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 had the problem that there are limited opportunities to receive professional instruction in improving golf swing form.

[0005] The system according to the embodiment aims to measure golf swing form in real time and provide guidance on the optimal practice method. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a multimodal AI, and an instructor. The camera is linked to a golf simulator and measures the player's swing form in real time. The multimodal AI analyzes the video data acquired by the camera. The instructor provides guidance on form improvements and optimal practice methods based on the results of the analysis by the multimodal AI. [Effects of the Invention]

[0007] The system according to the embodiment can measure golf swing form in real time and provide guidance on the optimal practice method. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The golf practice support system according to the embodiment of the present invention measures a player's swing form in real time, analyzes the video data using multimodal AI, and provides guidance on form improvements and optimal practice methods. This allows the golf practice support system to effectively improve a player's form.

[0029] A golf practice support system according to an embodiment includes a camera, a multimodal AI, and an instructor. The camera measures a player's swing form in real time. For example, a camera linked to a golf simulator can be used to capture a detailed sequence of movements from the start to the end of a swing. The camera can also capture the player's movements with high accuracy and record them as video data. The multimodal AI analyzes the video data acquired by the camera. For example, the multimodal AI can analyze the swing trajectory, body movement, club angle, etc. to identify areas that need improvement. The multimodal AI can also analyze the video data using a generative AI (e.g., a text generation AI or a multimodal generation AI). The instructor provides guidance on form improvements and optimal practice methods based on the results of the multimodal AI analysis. For example, the instructor may provide specific advice such as, "Your club is leaning back too much at the top of your swing. Try to lean forward a little more." The instructor also provides guidance using both video and text, making it visually easy for the player to understand. This allows the golf practice support system according to an embodiment to effectively improve a player's form. For example, players can effectively improve their swing form without receiving professional instruction. Also, by using a golf simulator, players can practice without going out on an actual course, which is expected to improve their form in a short period of time.

[0030] The camera can scan handwritten answers and convert them into digital data. For example, the camera can scan the handwritten answer sheet and save it as image data. It can then use OCR technology to convert the image data into text data. The camera can also use a smartphone camera to take a photo of the handwritten answer sheet and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The camera can also write the handwritten answer sheet with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting the handwritten answer sheet into digital data makes it easier for generative AI to analyze.

[0031] The camera can analyze writing pressure and stroke order to extract the writer's characteristics. The camera, for example, uses a writing pressure sensor to analyze how the answer is written. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. To analyze the stroke order, the camera also tracks the movement of the digital pen and collects data on the writer's stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. The camera also combines the data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in writing pressure and stroke order. In this way, by analyzing writing pressure and stroke order, the writer's characteristics can be grasped in detail.

[0032] The camera can convert what a student dictates into text using voice input and treat the text as an answer. For example, the camera records what a student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The camera can also create a system that recognizes what a student dictates in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The camera can also convert what a student dictates into text using voice input and save the text data as an answer. For example, voice recognition technology can be used to perform highly accurate text conversion. This allows students who have difficulty writing by hand or typing to submit answers using voice input.

[0033] The camera can analyze images and diagrams and include visual information in the evaluation. For example, the camera can analyze images and diagrams included in an answer sheet using image recognition technology and convert the content into text data. For example, the content of the diagram can be automatically analyzed and reflected in the evaluation. The camera can also analyze images and diagrams included in an answer sheet to build a system that evaluates based on visual information. For example, the content of the image can be analyzed and reflected in the evaluation of the answer. The camera can also analyze an answer sheet that includes images and diagrams and integrate the visual information with the text data to evaluate it. For example, the visual information can be analyzed using image recognition technology and reflected in the evaluation of the answer. This makes it possible to perform a more detailed evaluation by analyzing answer sheets that include images and diagrams.

[0034] Multimodal AI can refer to background information and topic models to understand the context. For example, when a generative AI creates a summary, it automatically collects relevant background information and refers to it to understand the context. For example, it collects related news articles and academic papers. Multimodal AI also uses topic models to understand the context when the generative AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. Multimodal AI also references relevant background information and topic models when the generative AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context.

[0035] Multimodal AI can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, in multimodal AI, a generative AI analyzes the logical structure of an answer and generates a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. Multimodal AI also builds a system in which a generative AI analyzes the development of arguments in an answer and generates a logical summary. For example, it generates a summary based on the importance and relevance of the arguments. Multimodal AI also develops algorithms for a generative AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of the arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0036] Multimodal AI can compare the analyzed data with other players and make a relative evaluation. Multimodal AI, for example, builds a system that compares the analyzed data with other players and makes a relative evaluation. For example, it identifies areas for improvement in form by comparing with players of the same level. This makes it possible to make a relative evaluation and identify areas for improvement by comparing with other players.

[0037] Multimodal AI can provide audio feedback on analysis results, facilitating understanding through both visual and auditory channels. For example, multimodal AI can build a system that provides audio feedback on analysis results, facilitating understanding through both visual and auditory channels. For example, it can explain swing improvements through audio. By providing audio feedback on analysis results, it can facilitate understanding through both visual and auditory channels.

[0038] The coaching department can provide an individually customized improvement plan based on the player's past practice data. For example, the coaching department could build a system in which AI analyzes the player's past practice data and provides an individually customized improvement plan. For example, it could suggest an optimal practice menu based on past swing data. This allows for effective form improvement by providing an individually customized improvement plan based on the player's past practice data.

[0039] Coaches can monitor players' movements in real time and immediately point out areas for improvement. Coaches can, for example, build a system in which AI monitors players' movements in real time and immediately points out areas for improvement. For example, it can point out problems with form in the middle of a swing and prompt immediate correction. This allows players' movements to be monitored in real time and immediately points out areas for improvement, enabling rapid form improvement.

[0040] The coaching department can propose specific ways to improve based on the success stories of other players. For example, the coaching department will build a system in which AI analyzes the success stories of other players and proposes specific ways to improve based on that. For example, it will suggest areas for improvement based on the swing data of top players. This will enable effective form improvement by proposing specific ways to improve based on the success stories of other players.

[0041] Coaches can animate a player's swing form and show areas for improvement in a visually easy-to-understand way. For example, coaches can build a system in which AI animates a player's swing form and shows areas for improvement in a visually easy-to-understand way. For example, each stage of the swing is animated to highlight areas for improvement. This allows coaches to show areas for improvement in a visually easy-to-understand way by animating the swing form.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The golf practice support system can also be equipped with a sensor unit that acquires biometric data. The sensor unit acquires biometric data such as the player's heart rate, muscle movement, and electrodermal activity in real time. For example, it can monitor heart rate fluctuations to determine whether the player is tense or relaxed. It can also analyze muscle movement to evaluate the amount of force and balance applied during the swing. It can also measure electrodermal activity to understand the player's stress level. This allows for more accurate instruction based on the player's biometric data.

[0044] The golf practice support system can further include a weather acquisition unit that acquires weather data. The weather acquisition unit acquires weather data from the practice field in real time and reflects it in the practice content. For example, the weather acquisition unit provides swing advice taking wind speed and direction into account. It can also suggest club selection and swing adjustments based on temperature and humidity. Furthermore, it can provide advice to help players choose the optimal practice timing based on weather data. This enables practice that takes weather conditions into account, which is expected to improve performance on the actual course.

[0045] The golf practice support system can also be equipped with a function that provides a long-term development plan based on a player's past practice data. For example, it can analyze past swing data and practice history to understand a player's development trends. It can also provide specific goals and areas for improvement based on past data. Furthermore, it can provide regular feedback and progress checks based on the long-term development plan to maintain the player's motivation. This supports the player's long-term development and is expected to lead to sustained performance improvements.

[0046] The golf practice support system can also be equipped with a function that creates a 3D model of the player's swing form and shows areas for improvement in a visually easy-to-understand format. For example, the player's swing can be displayed as a 3D model and checked from various angles. The 3D model can also be used to perform a detailed analysis of each stage of the swing and show specific areas for improvement. Furthermore, the 3D model can be used to compare the player's swing form with an ideal swing form and visually show which areas the player needs to improve. This allows players to understand their own swing form in three dimensions and make effective improvements.

[0047] The golf practice support system can also store a player's swing data in the cloud and be equipped with the ability to link with other devices. For example, by linking with a smartphone or tablet, practice data can be checked anytime, anywhere. Furthermore, based on the data stored in the cloud, a player's progress can be tracked in real time. Furthermore, by linking with other devices, data can be shared with coaches and trainers, allowing them to receive expert advice. This allows players to efficiently manage their practice data and practice more effectively.

[0048] The golf practice support system can also analyze a player's swing form and provide an individually customized training plan. For example, it can suggest a training menu to strengthen specific muscles based on the player's swing data. It can also provide specific stretches and exercises needed to improve the player's form. Furthermore, the training plan can be regularly updated to provide an optimal menu based on the player's progress. This allows players to effectively improve their swing form and expect sustained performance improvements.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The camera measures the player's swing form in real time. For example, a camera linked to a golf simulator can be used to capture the entire swing in detail, from start to finish. The camera can also capture the player's movements with high precision and record them as video data. Step 2: Multimodal AI analyzes the video data captured by the camera. For example, multimodal AI analyzes the swing trajectory, body movement, club angle, etc. to identify areas that need improvement. Multimodal AI can also use generative AI (e.g., text generation AI or multimodal generation AI) to analyze the video data. Step 3: Based on the results of the multimodal AI analysis, the instructor provides guidance on form improvements and optimal practice methods. For example, the instructor may provide specific advice such as, "Your club is leaning back too much at the top of your swing. Try to lean forward a little more." The instructor also provides instruction using both video and text, making it easy for players to understand visually.

[0051] (Example 2) The golf practice support system according to the embodiment of the present invention measures a player's swing form in real time, analyzes the video data using multimodal AI, and provides guidance on form improvements and optimal practice methods. This allows the golf practice support system to effectively improve a player's form.

[0052] A golf practice support system according to an embodiment includes a camera, a multimodal AI, and an instructor. The camera measures a player's swing form in real time. For example, a camera linked to a golf simulator can be used to capture a detailed sequence of movements from the start to the end of a swing. The camera can also capture the player's movements with high accuracy and record them as video data. The multimodal AI analyzes the video data acquired by the camera. For example, the multimodal AI can analyze the swing trajectory, body movement, club angle, etc. to identify areas that need improvement. The multimodal AI can also analyze the video data using a generative AI (e.g., a text generation AI or a multimodal generation AI). The instructor provides guidance on form improvements and optimal practice methods based on the results of the multimodal AI analysis. For example, the instructor may provide specific advice such as, "Your club is leaning back too much at the top of your swing. Try to lean forward a little more." The instructor also provides guidance using both video and text, making it visually easy for the player to understand. This allows the golf practice support system according to an embodiment to effectively improve a player's form. For example, players can effectively improve their swing form without receiving professional instruction. Also, by using a golf simulator, players can practice without going out on an actual course, which is expected to improve their form in a short period of time.

[0053] The camera can scan handwritten answers and convert them into digital data. For example, the camera can scan the handwritten answer sheet and save it as image data. It can then use OCR technology to convert the image data into text data. The camera can also use a smartphone camera to take a photo of the handwritten answer sheet and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The camera can also write the handwritten answer sheet with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting the handwritten answer sheet into digital data makes it easier for generative AI to analyze.

[0054] The camera can analyze writing pressure and stroke order to extract the writer's characteristics. The camera, for example, uses a writing pressure sensor to analyze how the answer is written. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. To analyze the stroke order, the camera also tracks the movement of the digital pen and collects data on the writer's stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. The camera also combines the data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in writing pressure and stroke order. In this way, by analyzing writing pressure and stroke order, the writer's characteristics can be grasped in detail.

[0055] The camera can estimate a student's emotions using an emotion estimation function and reflect the emotion data in the evaluation of the answer sheet. For example, the camera captures the student's facial expression while writing the answer sheet and analyzes the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on changes in facial expression. The camera also records the student's voice while writing the answer sheet and estimates the emotion using voice analysis technology. For example, the tone and speed of the voice are analyzed to calculate the emotion score. The camera also uses a sensor to collect the student's biometric data (heart rate and electrodermal activity) while writing the answer sheet and analyzes the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on fluctuations in heart rate. This allows the student's emotions to be reflected in the evaluation, making it possible to perform a more comprehensive evaluation.

[0056] The camera can convert what a student dictates into text using voice input and treat the text as an answer. For example, the camera records what a student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The camera can also create a system that recognizes what a student dictates in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The camera can also convert what a student dictates into text using voice input and save the text data as an answer. For example, voice recognition technology can be used to perform highly accurate text conversion. This allows students who have difficulty writing by hand or typing to submit answers using voice input.

[0057] The camera can analyze images and diagrams and include visual information in the evaluation. For example, the camera can analyze images and diagrams included in an answer sheet using image recognition technology and convert the content into text data. For example, the content of the diagram can be automatically analyzed and reflected in the evaluation. The camera can also analyze images and diagrams included in an answer sheet to build a system that evaluates based on visual information. For example, the content of the image can be analyzed and reflected in the evaluation of the answer. The camera can also analyze an answer sheet that includes images and diagrams and integrate the visual information with the text data to evaluate it. For example, the visual information can be analyzed using image recognition technology and reflected in the evaluation of the answer. This makes it possible to perform a more detailed evaluation by analyzing answer sheets that include images and diagrams.

[0058] The camera can monitor students' emotions in real time using an emotion estimation function and provide feedback based on their emotions. For example, the camera captures the student's facial expression while reading their answer sheet and uses an emotion estimation algorithm to analyze their emotions in real time. For example, it calculates an emotion score based on changes in their facial expression and provides feedback. The camera also records the student's voice while reading their answer sheet and uses voice analysis technology to estimate their emotions in real time. For example, it analyzes the tone and speed of their voice to calculate an emotion score and provide feedback. The camera also uses a sensor to collect the student's biometric data (heart rate and electrodermal activity) while they read their answer sheet and uses an emotion estimation algorithm to analyze their emotions in real time. For example, it calculates an emotion score based on heart rate fluctuations and provides feedback. In this way, students' emotions can be monitored in real time and appropriate feedback can be provided, improving learning effectiveness.

[0059] Multimodal AI can refer to background information and topic models to understand the context. For example, when a generative AI creates a summary, it automatically collects relevant background information and refers to it to understand the context. For example, it collects related news articles and academic papers. Multimodal AI also uses topic models to understand the context when the generative AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. Multimodal AI also references relevant background information and topic models when the generative AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context.

[0060] Multimodal AI can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, in multimodal AI, a generative AI analyzes the logical structure of an answer and generates a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. Multimodal AI also builds a system in which a generative AI analyzes the development of arguments in an answer and generates a logical summary. For example, it generates a summary based on the importance and relevance of the arguments. Multimodal AI also develops algorithms for a generative AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of the arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0061] Multimodal AI uses emotion estimation to generate summaries that capture the emotional nuances of answers, allowing emotional elements to be reflected in the evaluation. For example, when a generation AI summarizes, multimodal AI uses emotion estimation to capture the emotional nuances of answers. For example, it generates summaries based on emotion scores. Multimodal AI also uses emotion estimation to build systems that allow generation AI to reflect the emotional elements of answers in evaluations. For example, it performs evaluations based on emotion scores. Multimodal AI also develops algorithms that allow generation AI to use emotion estimation to generate summaries that capture the emotional nuances of answers. For example, it generates summaries based on emotion scores and reflects these in the evaluation. In this way, by generating summaries that capture emotional nuances, emotional elements can also be reflected in the evaluation.

[0062] Multimodal AI can compare the analyzed data with other players and make a relative evaluation. Multimodal AI, for example, builds a system that compares the analyzed data with other players and makes a relative evaluation. For example, it identifies areas for improvement in form by comparing with players of the same level. This makes it possible to make a relative evaluation and identify areas for improvement by comparing with other players.

[0063] Multimodal AI can provide audio feedback on analysis results, facilitating understanding through both visual and auditory channels. For example, multimodal AI can build a system that provides audio feedback on analysis results, facilitating understanding through both visual and auditory channels. For example, it can explain swing improvements through audio. By providing audio feedback on analysis results, it can facilitate understanding through both visual and auditory channels.

[0064] Multimodal AI can provide feedback when players feel most motivated, maximizing the effectiveness of practice. For example, multimodal AI can use emotion estimation functions to build a system that provides feedback when players feel most motivated. For example, providing feedback when a player is relaxed maximizes the effectiveness of practice. This maximizes the effectiveness of practice by providing feedback when players feel most motivated.

[0065] The coaching department can provide an individually customized improvement plan based on the player's past practice data. For example, the coaching department could build a system in which AI analyzes the player's past practice data and provides an individually customized improvement plan. For example, it could suggest an optimal practice menu based on past swing data. This allows for effective form improvement by providing an individually customized improvement plan based on the player's past practice data.

[0066] Coaches can monitor players' movements in real time and immediately point out areas for improvement. Coaches can, for example, build a system in which AI monitors players' movements in real time and immediately points out areas for improvement. For example, it can point out problems with form in the middle of a swing and prompt immediate correction. This allows players' movements to be monitored in real time and immediately points out areas for improvement, enabling rapid form improvement.

[0067] The coaching unit can select a coaching method according to the player's emotional state and provide effective coaching while reducing stress. The coaching unit, for example, uses an emotion estimation function to build a system that selects a coaching method according to the player's emotional state. For example, when a player is nervous, the coaching unit provides advice on how to relax. This allows for effective coaching while reducing stress by selecting a coaching method according to the player's emotional state.

[0068] The coaching department can propose specific ways to improve based on the success stories of other players. For example, the coaching department will build a system in which AI analyzes the success stories of other players and proposes specific ways to improve based on that. For example, it will suggest areas for improvement based on the swing data of top players. This will enable effective form improvement by proposing specific ways to improve based on the success stories of other players.

[0069] Coaches can animate a player's swing form and show areas for improvement in a visually easy-to-understand way. For example, coaches can build a system in which AI animates a player's swing form and shows areas for improvement in a visually easy-to-understand way. For example, each stage of the swing is animated to highlight areas for improvement. This allows coaches to show areas for improvement in a visually easy-to-understand way by animating the swing form.

[0070] The instructor can provide instruction when the player is most relaxed, promoting effective learning. The instructor can, for example, use an emotion estimation function to build a system that provides instruction when the player is most relaxed. For example, feedback is provided when the player is relaxed, promoting effective learning. This allows effective learning to be promoted by providing instruction when the player is most relaxed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The golf practice support system can also be equipped with a sensor unit that acquires biometric data. The sensor unit acquires biometric data such as the player's heart rate, muscle movement, and electrodermal activity in real time. For example, it can monitor heart rate fluctuations to determine whether the player is tense or relaxed. It can also analyze muscle movement to evaluate the amount of force and balance applied during the swing. It can also measure electrodermal activity to understand the player's stress level. This allows for more accurate instruction based on the player's biometric data.

[0073] The golf practice support system can further include a weather acquisition unit that acquires weather data. The weather acquisition unit acquires weather data from the practice field in real time and reflects it in the practice content. For example, the weather acquisition unit provides swing advice taking wind speed and direction into account. It can also suggest club selection and swing adjustments based on temperature and humidity. Furthermore, it can provide advice to help players choose the optimal practice timing based on weather data. This enables practice that takes weather conditions into account, which is expected to improve performance on the actual course.

[0074] The golf practice support system can also be equipped with a function to estimate the player's emotions and customize the practice menu based on the estimated emotions. For example, if the player is nervous, it can suggest light stretching or breathing techniques to relax them. If the player is lacking concentration, it can provide mental training to improve their concentration. Furthermore, if the player is feeling motivated, it can suggest a challenging practice menu to maximize the effectiveness of the practice. This allows for effective practice by providing a practice menu that suits the player's emotional state.

[0075] The golf practice support system can also be equipped with a function that provides a long-term development plan based on a player's past practice data. For example, it can analyze past swing data and practice history to understand a player's development trends. It can also provide specific goals and areas for improvement based on past data. Furthermore, it can provide regular feedback and progress checks based on the long-term development plan to maintain the player's motivation. This supports the player's long-term development and is expected to lead to sustained performance improvements.

[0076] The golf practice support system can also be equipped with a function to estimate the player's emotions and adjust the timing of feedback based on the estimated emotions. For example, when a player is concentrating, it can provide detailed technical advice. When a player is tired, it can suggest simple ways to refresh themselves or take a break. Furthermore, when a player is feeling motivated, it can emphasize positive feedback to increase their motivation to practice. This makes it possible to maximize the effectiveness of practice by providing feedback at the appropriate time according to the player's emotional state.

[0077] The golf practice support system can also be equipped with a function that creates a 3D model of the player's swing form and shows areas for improvement in a visually easy-to-understand format. For example, the player's swing can be displayed as a 3D model and checked from various angles. The 3D model can also be used to perform a detailed analysis of each stage of the swing and show specific areas for improvement. Furthermore, the 3D model can be used to compare the player's swing form with an ideal swing form and visually show which areas the player needs to improve. This allows players to understand their own swing form in three dimensions and make effective improvements.

[0078] The golf practice support system can also be equipped with a function to estimate the player's emotions and adjust the practice environment based on the estimated emotions. For example, if the player is relaxed, it can suggest practicing in a quiet environment. If the player wants to improve their concentration, it can provide music or environmental sounds that help them concentrate. Furthermore, if the player is feeling motivated, it can set up a challenging practice environment to maximize the effectiveness of their practice. This allows for effective practice by providing the optimal practice environment according to the player's emotional state.

[0079] The golf practice support system can also store a player's swing data in the cloud and be equipped with the ability to link with other devices. For example, by linking with a smartphone or tablet, practice data can be checked anytime, anywhere. Furthermore, based on the data stored in the cloud, a player's progress can be tracked in real time. Furthermore, by linking with other devices, data can be shared with coaches and trainers, allowing them to receive expert advice. This allows players to efficiently manage their practice data and practice more effectively.

[0080] The golf practice support system can also be equipped with a function to estimate the player's emotions and adjust the difficulty of practice based on the estimated emotions. For example, if the player is feeling stressed, a less difficult practice menu can be suggested. On the other hand, if the player is relaxed, a more difficult practice menu can be provided. Furthermore, if the player is feeling motivated, a more challenging practice menu can be suggested, maximizing the effectiveness of practice. This allows for effective practice by providing the optimal practice menu according to the player's emotional state.

[0081] The golf practice support system can also analyze a player's swing form and provide an individually customized training plan. For example, it can suggest a training menu to strengthen specific muscles based on the player's swing data. It can also provide specific stretches and exercises needed to improve the player's form. Furthermore, the training plan can be regularly updated to provide an optimal menu based on the player's progress. This allows players to effectively improve their swing form and expect sustained performance improvements.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The camera measures the player's swing form in real time. For example, a camera linked to a golf simulator can be used to capture the entire swing in detail, from start to finish. The camera can also capture the player's movements with high precision and record them as video data. Step 2: Multimodal AI analyzes the video data captured by the camera. For example, multimodal AI analyzes the swing trajectory, body movement, club angle, etc. to identify areas that need improvement. Multimodal AI can also use generative AI (e.g., text generation AI or multimodal generation AI) to analyze the video data. Step 3: Based on the results of the multimodal AI analysis, the instructor provides guidance on form improvements and optimal practice methods. For example, the instructor may provide specific advice such as, "Your club is leaning back too much at the top of your swing. Try to lean forward a little more." The instructor also provides instruction using both video and text, making it easy for players to understand visually.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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 robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 camera linked to a golf simulator measures the player's swing form in real time. a multimodal AI that analyzes the video data acquired by the camera; and a coaching unit that provides guidance on form improvements and optimal practice methods based on the results of analysis by the multimodal AI. A system characterized by:

2. The camera is A motorized camera mount is used to dynamically adjust the camera position to measure swings from the optimal angle according to the player's movements.

2. The system of claim 1.

3. The system comprises: It can also be applied to measuring form in sports other than golf.

2. The system of claim 1.

4. The multimodal AI is The analysis results of the swing form are visualized as a 3D model, allowing the player to check their movements from multiple angles.

2. The system of claim 1.

5. The leadership team: Providing an individually customized improvement plan based on the player's past practice data 2. The system of claim 1.

6. The camera is Equipped with an emotion estimation function that analyzes the player's facial expressions and physical tension to evaluate swings that take into account their psychological state.

2. The system of claim 1.

7. The multimodal AI is Provides analysis results based on the player's emotional state and suggests improvements that take psychological factors into account 2. The system of claim 1.

8. The leadership team: Select a teaching method that suits the player's emotional state and provide effective instruction while reducing stress.

2. The system of claim 1.

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