System

Abstract

An object of a system according to an embodiment is to provide a golf player with optimal advice according to the situation on the spot.SOLUTION: A system according to an embodiment includes a course information acquisition unit, a ball position acquisition unit, a weather information acquisition unit, a score status acquisition unit, an advice generation unit, and a video generation unit. The course information acquisition unit acquires course information of a golf course. The ball position acquisition unit acquires a current position of a ball. The weather information acquisition unit acquires weather information. The score status acquisition unit acquires a score status of a player. The advice generation unit generates advice based on these pieces of information. The video generation unit generates a short video.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 make it difficult for golfers to obtain optimal advice suited to the situation at hand, and there is room for improvement in terms of improving the quality of play.

[0005] The system according to the embodiment aims to provide the golf player with the most appropriate advice according to the situation at hand. [Means for solving the problem]

[0006] The system according to the embodiment includes a course information acquisition unit, a ball position acquisition unit, a weather information acquisition unit, a score status acquisition unit, an advice generation unit, and a video generation unit. The course information acquisition unit acquires golf course information. The ball position acquisition unit acquires the current position of the ball. The weather information acquisition unit acquires weather information. The score status acquisition unit acquires the player's score status. The advice generation unit generates advice based on this information. The video generation unit generates short video. [Effects of the Invention]

[0007] The system according to the embodiment can provide the golf player with the most appropriate advice according to the situation at hand. [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) A golf advice system according to an embodiment of the present invention is a system that provides optimal advice and visual support to a player. This allows the golf advice system to provide optimal advice and visual support to a player.

[0029] A golf advice system according to an embodiment includes a course information acquisition unit, a ball position acquisition unit, a weather information acquisition unit, a score status acquisition unit, an advice generation unit, and a video generation unit. The course information acquisition unit acquires golf course information. For example, the course information acquisition unit obtains detailed information about the layout of each hole on the golf course, the location of obstacles, the shape of the green, and so on. The ball position acquisition unit acquires the current position of the ball. For example, the ball position acquisition unit analyzes an image taken by the player with a smartphone to determine the exact position of the ball. The weather information acquisition unit acquires weather information. For example, the weather information acquisition unit acquires and analyzes information such as wind speed, wind direction, temperature, and humidity. The score status acquisition unit acquires the player's score status. For example, the score status acquisition unit acquires and analyzes the player's score for the day and past scores. The advice generation unit generates advice based on this information. For example, the advice generation unit generates specific instructions such as, "Swing lightly with a 7-iron up to waist height. Aim toward the right side of the tree in front of you." The video generation unit generates a video of the short shot. For example, the video generation unit generates a short video that serves as a model, such as "Imagine hitting the ball like this," and presents it to the player. This allows the golf advice system according to the embodiment to provide optimal advice and visual support to the player.

[0030] The course information acquisition unit uses a drone to perform 3D mapping of the course in real time, and the generation AI analyzes the data to provide the latest course information. For example, the course information acquisition unit uses a drone to scan the entire golf course in real time and perform 3D mapping. The generation AI analyzes the data to obtain detailed information such as the layout of each hole, the location of obstacles, and the shape of the greens. This provides basic information for players to determine what strategy they should adopt. This makes it possible to provide the latest course information in real time.

[0031] The course information acquisition unit can analyze past player play data and statistically analyze the success and failure rates on specific holes. For example, the course information acquisition unit collects past player play data, and the generation AI analyzes that data. For example, it statistically analyzes the success and failure rates on specific holes and suggests the optimal strategy to the player. This makes it possible to provide the optimal strategy based on past data.

[0032] The ball position acquisition unit uses a LiDAR sensor in addition to image analysis technology to scan the ball's position and the terrain with high precision, and the generation AI can analyze the data. The ball position acquisition unit, for example, uses a LiDAR sensor in addition to image analysis technology to scan the ball's position and the surrounding terrain with high precision. The generation AI analyzes the data and determines the exact position of the ball and the surrounding situation. This allows the ball's position and the surrounding terrain to be determined with high precision.

[0033] The ball position acquisition unit analyzes not only ball position information but also club selection and swing history, allowing the generation AI to provide more accurate advice. For example, the ball position acquisition unit analyzes club selection and swing history in addition to ball position information, allowing the generation AI to provide more accurate advice. For example, it suggests the optimal club selection based on past swing data. This makes it possible to provide more accurate advice based on club selection and swing history.

[0034] The weather information acquisition unit can analyze past weather data in addition to weather information and predict playing performance under specific weather conditions. The weather information acquisition unit, for example, collects past weather data in addition to weather information, and the generation AI analyzes the data. For example, it predicts playing performance under specific weather conditions and suggests an optimal strategy to the player. This makes it possible to predict playing performance based on past weather data.

[0035] The weather information acquisition unit can predict the player's physical condition and fatigue level based on the weather information, and adjust advice based on that. The weather information acquisition unit, for example, predicts the player's physical condition and fatigue level based on the weather information, and the generation AI analyzes that data. For example, if the temperature or humidity is high, advice for managing physical condition is provided. This makes it possible to predict the player's physical condition and fatigue level based on the weather information, and adjust advice accordingly.

[0036] The score status acquisition unit can analyze the player's past play data in addition to the score status and grasp the long-term performance trend. For example, the score status acquisition unit collects the player's past play data in addition to the score status, and the generation AI analyzes that data. For example, it grasps the long-term performance trend and proposes the optimal strategy to the player. This makes it possible to grasp the long-term performance trend and provide the optimal strategy.

[0037] The score status acquisition unit can perform a simulation for risk management based on the player's strategy and propose an optimal strategy. The score status acquisition unit, for example, performs a simulation for risk management based on the player's strategy, and the generation AI proposes an optimal strategy. For example, it proposes club selection and hitting method to avoid risk. In this way, it is possible to perform a simulation for risk management and propose an optimal strategy.

[0038] In addition to giving advice on how to hit the ball, the advice generation unit can analyze the player's swing form in real time and point out areas for improvement in their form. For example, in addition to giving advice on how to hit the ball, the advice generation unit can analyze the player's swing form in real time and the generation AI can point out areas for improvement in their form. For example, the trajectory and speed of the swing can be analyzed and specific areas for improvement can be indicated. This makes it possible to analyze the player's swing form in real time and point out areas for improvement.

[0039] The advice generation unit can customize the advice on how to hit the ball based on the physique and muscle strength of the player. For example, the advice generation unit customizes the advice on how to hit the ball based on the physique and muscle strength of the player. For example, the advice generation unit suggests the optimal swing method taking into account the player's height, weight, and muscle strength. This makes it possible to customize the advice on how to hit the ball based on the player's physique and muscle strength.

[0040] In addition to the short video, the video generation unit can reproduce the player's swing as a 3D model and provide different perspectives. For example, in addition to the short video, the video generation unit can reproduce the player's swing as a 3D model and the generation AI can provide perspectives from different angles. For example, the swing trajectory and form can be displayed as a 3D model. This makes it possible to reproduce the player's swing as a 3D model and provide visual support from different perspectives.

[0041] The video generation unit can compare the short video with the player's past play video and visually show areas for improvement. For example, the video generation unit compares the short video with the player's past play video, and the generation AI visually shows areas for improvement. For example, the video of the past swing and the video of the current swing are displayed side by side to specifically show areas for improvement. This makes it possible to visually show areas for improvement by comparing with the player's past play video.

[0042] The video generation unit can apply the short video generation technology to other sports and provide a similar advice system. For example, the video generation unit applies the short video generation technology to dance, and the generation AI analyzes the data to provide optimal advice. For example, it can provide specific instructions on dance steps and form. This allows the short video generation technology to be applied to other sports and provide a similar advice system.

[0043] The video generation unit can add a function that enables a player to select an optimal practice method based on the short video. The video generation unit can add a function that enables a player to select an optimal practice method based on the short video, for example. For example, the video generation unit can suggest a practice method that corresponds to a specific shot. This makes it possible to select an optimal practice method based on the short video.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The golf advice system can further include a biometric data acquisition unit. The biometric data acquisition unit acquires biometric information such as the player's heart rate, body temperature, and sweat rate. For example, by analyzing heart rate fluctuations, it is possible to grasp the player's level of tension and concentration. In addition, by monitoring body temperature and sweat rate, it is possible to support the player's physical condition management. This makes it possible to provide more personalized advice based on the player's biometric information.

[0046] The golf advice system can also be equipped with a voice assistant unit. The voice assistant unit recognizes the player's voice commands and provides advice in real time. For example, if the player says, "Tell me how to play the next hole," the voice assistant unit will suggest the optimal strategy based on course and weather information. Also, if the player asks, "What's the wind speed?" the system can announce the current wind speed by voice. This allows the player to obtain the necessary information hands-free, allowing them to concentrate on playing.

[0047] The golf advice system may further include a social feedback unit. The social feedback unit collects feedback and advice from other players and provides it to the player. For example, it may display other players' opinions on strategies for a particular hole. It may also allow players to upload videos of their own play and receive comments and advice from other players. This allows players to take advantage of the experience and knowledge of other players and improve the quality of their play.

[0048] The golf advice system may further include a virtual reality (VR) section. The VR section allows players to experience and practice courses in a virtual space. For example, players can wear VR goggles and walk around a real course in a virtual space. They can also simulate shots on specific holes and practice optimal strokes. This allows players to experience a realistic practice environment without having to go to a real course.

[0049] The golf advice system may further include an ecological data acquisition unit. The ecological data acquisition unit collects environmental data about the golf course and provides it to the player. For example, the system may acquire data about the vegetation, animal habitats, and water quality of the golf course and provide the information to the player. The system may also provide advice to help the player play in an environmentally friendly manner. This allows the player to enjoy golf in an environmentally friendly manner.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The course information acquisition unit acquires course information about the golf course. For example, the course information acquisition unit obtains detailed information about the layout of each hole on the golf course, the location of obstacles, the shape of the green, and so on. Step 2: The ball position acquisition unit acquires the current position of the ball. For example, the ball position acquisition unit may analyze an image taken by the player with a smartphone and identify the exact position of the ball. Step 3: The weather information acquisition unit acquires weather information, such as wind speed, wind direction, temperature, and humidity, and analyzes the information. Step 4: The score status acquisition unit acquires the player's score status. For example, the score status acquisition unit acquires and analyzes the player's score for that day and past scores. Step 5: The advice generator generates advice based on this information. For example, the advice generator might generate specific instructions such as, "Swing the 7-iron lightly up to waist height. Aim toward the right side of the tree in front of you." Step 6: The video generation unit generates a short video. For example, the video generation unit generates a short video that serves as a model, such as "This is how you should hit the ball," and presents it to the player.

[0052] (Example 2) A golf advice system according to an embodiment of the present invention is a system that provides optimal advice and visual support to a player. This allows the golf advice system to provide optimal advice and visual support to a player.

[0053] A golf advice system according to an embodiment includes a course information acquisition unit, a ball position acquisition unit, a weather information acquisition unit, a score status acquisition unit, an advice generation unit, and a video generation unit. The course information acquisition unit acquires golf course information. For example, the course information acquisition unit obtains detailed information about the layout of each hole on the golf course, the location of obstacles, the shape of the green, and so on. The ball position acquisition unit acquires the current position of the ball. For example, the ball position acquisition unit analyzes an image taken by the player with a smartphone to determine the exact position of the ball. The weather information acquisition unit acquires weather information. For example, the weather information acquisition unit acquires and analyzes information such as wind speed, wind direction, temperature, and humidity. The score status acquisition unit acquires the player's score status. For example, the score status acquisition unit acquires and analyzes the player's score for the day and past scores. The advice generation unit generates advice based on this information. For example, the advice generation unit generates specific instructions such as, "Swing lightly with a 7-iron up to waist height. Aim toward the right side of the tree in front of you." The video generation unit generates a video of the short shot. For example, the video generation unit generates a short video that serves as a model, such as "Imagine hitting the ball like this," and presents it to the player. This allows the golf advice system according to the embodiment to provide optimal advice and visual support to the player.

[0054] The course information acquisition unit uses a drone to perform 3D mapping of the course in real time, and the generation AI analyzes the data to provide the latest course information. For example, the course information acquisition unit uses a drone to scan the entire golf course in real time and perform 3D mapping. The generation AI analyzes the data to obtain detailed information such as the layout of each hole, the location of obstacles, and the shape of the greens. This provides basic information for players to determine what strategy they should adopt. This makes it possible to provide the latest course information in real time.

[0055] The course information acquisition unit can analyze past player play data and statistically analyze the success and failure rates on specific holes. For example, the course information acquisition unit collects past player play data, and the generation AI analyzes that data. For example, it statistically analyzes the success and failure rates on specific holes and suggests the optimal strategy to the player. This makes it possible to provide the optimal strategy based on past data.

[0056] The course information acquisition unit uses the emotion estimation function to analyze the stress and satisfaction felt by past players on a particular hole, and can customize advice based on that information. The course information acquisition unit, for example, uses the emotion estimation function to analyze the stress and satisfaction felt by past players on a particular hole. For example, it analyzes the player's facial expressions and voice data to calculate an emotion score for a particular hole. This makes it possible to customize advice based on the player's emotions.

[0057] The ball position acquisition unit uses a LiDAR sensor in addition to image analysis technology to scan the ball's position and the terrain with high precision, and the generation AI can analyze the data. The ball position acquisition unit, for example, uses a LiDAR sensor in addition to image analysis technology to scan the ball's position and the surrounding terrain with high precision. The generation AI analyzes the data and determines the exact position of the ball and the surrounding situation. This allows the ball's position and the surrounding terrain to be determined with high precision.

[0058] The ball position acquisition unit analyzes not only ball position information but also club selection and swing history, allowing the generation AI to provide more accurate advice. For example, the ball position acquisition unit analyzes club selection and swing history in addition to ball position information, allowing the generation AI to provide more accurate advice. For example, it suggests the optimal club selection based on past swing data. This makes it possible to provide more accurate advice based on club selection and swing history.

[0059] The ball position acquisition unit uses the emotion estimation function to analyze the anxiety or confidence the player feels regarding the ball position, and can adjust advice based on the emotion. The ball position acquisition unit, for example, uses the emotion estimation function to analyze the anxiety or confidence the player feels regarding the ball position. For example, it analyzes the player's facial expression and voice data and calculates an emotion score. This makes it possible to adjust advice based on the player's emotion.

[0060] The weather information acquisition unit can analyze past weather data in addition to weather information and predict playing performance under specific weather conditions. The weather information acquisition unit, for example, collects past weather data in addition to weather information, and the generation AI analyzes the data. For example, it predicts playing performance under specific weather conditions and suggests an optimal strategy to the player. This makes it possible to predict playing performance based on past weather data.

[0061] The weather information acquisition unit can predict the player's physical condition and fatigue level based on the weather information, and adjust advice based on that. The weather information acquisition unit, for example, predicts the player's physical condition and fatigue level based on the weather information, and the generation AI analyzes that data. For example, if the temperature or humidity is high, advice for managing physical condition is provided. This makes it possible to predict the player's physical condition and fatigue level based on the weather information, and adjust advice accordingly.

[0062] The weather information acquisition unit uses the emotion estimation function to analyze the stress and satisfaction felt by the player under specific weather conditions, and can customize advice based on that information. The weather information acquisition unit, for example, uses the emotion estimation function to analyze the stress and satisfaction felt by the player under specific weather conditions. For example, it analyzes the player's facial expressions and voice data to calculate an emotion score. This makes it possible to customize advice based on the player's emotions.

[0063] The score status acquisition unit can analyze the player's past play data in addition to the score status and grasp the long-term performance trend. For example, the score status acquisition unit collects the player's past play data in addition to the score status, and the generation AI analyzes that data. For example, it grasps the long-term performance trend and proposes the optimal strategy to the player. This makes it possible to grasp the long-term performance trend and provide the optimal strategy.

[0064] The score status acquisition unit can perform a simulation for risk management based on the player's strategy and propose an optimal strategy. The score status acquisition unit, for example, performs a simulation for risk management based on the player's strategy, and the generation AI proposes an optimal strategy. For example, it proposes club selection and hitting method to avoid risk. In this way, it is possible to perform a simulation for risk management and propose an optimal strategy.

[0065] The score status acquisition unit uses the emotion estimation function to analyze the pressure and confidence that the player feels in relation to the score status, and can adjust advice based on the emotions. The score status acquisition unit, for example, uses the emotion estimation function to analyze the pressure and confidence that the player feels in relation to the score status. For example, it analyzes the player's facial expressions and voice data to calculate an emotion score. This makes it possible to adjust advice based on the player's emotions.

[0066] In addition to giving advice on how to hit the ball, the advice generation unit can analyze the player's swing form in real time and point out areas for improvement in their form. For example, in addition to giving advice on how to hit the ball, the advice generation unit can analyze the player's swing form in real time and the generation AI can point out areas for improvement in their form. For example, the trajectory and speed of the swing can be analyzed and specific areas for improvement can be indicated. This makes it possible to analyze the player's swing form in real time and point out areas for improvement.

[0067] The advice generation unit can customize the advice on how to hit the ball based on the physique and muscle strength of the player. For example, the advice generation unit customizes the advice on how to hit the ball based on the physique and muscle strength of the player. For example, the advice generation unit suggests the optimal swing method taking into account the player's height, weight, and muscle strength. This makes it possible to customize the advice on how to hit the ball based on the player's physique and muscle strength.

[0068] The advice generation unit can use the emotion estimation function to analyze the confidence or anxiety that the player feels about their hitting style and adjust the advice based on that emotion. The advice generation unit, for example, uses the emotion estimation function to analyze the confidence or anxiety that the player feels about their hitting style. For example, it analyzes the player's facial expressions and voice data and calculates an emotion score. This makes it possible to adjust the advice based on the player's emotion.

[0069] In addition to the short video, the video generation unit can reproduce the player's swing as a 3D model and provide different perspectives. For example, in addition to the short video, the video generation unit can reproduce the player's swing as a 3D model and the generation AI can provide perspectives from different angles. For example, the swing trajectory and form can be displayed as a 3D model. This makes it possible to reproduce the player's swing as a 3D model and provide visual support from different perspectives.

[0070] The video generation unit can compare the short video with the player's past play video and visually show areas for improvement. For example, the video generation unit compares the short video with the player's past play video, and the generation AI visually shows areas for improvement. For example, the video of the past swing and the video of the current swing are displayed side by side to specifically show areas for improvement. This makes it possible to visually show areas for improvement by comparing with the player's past play video.

[0071] The video generation unit can use the emotion estimation function to analyze the player's emotional response when viewing the short video and adjust the video content based on the emotion. The video generation unit, for example, uses the emotion estimation function to analyze the player's emotional response when viewing the short video. For example, it analyzes the player's facial expressions and voice data and calculates an emotion score. This makes it possible to adjust the video content based on the player's emotional response.

[0072] The video generation unit can apply the short video generation technology to other sports and provide a similar advice system. For example, the video generation unit applies the short video generation technology to dance, and the generation AI analyzes the data to provide optimal advice. For example, it can provide specific instructions on dance steps and form. This allows the short video generation technology to be applied to other sports and provide a similar advice system.

[0073] The video generation unit can add a function that enables a player to select an optimal practice method based on the short video. The video generation unit can add a function that enables a player to select an optimal practice method based on the short video, for example. For example, the video generation unit can suggest a practice method that corresponds to a specific shot. This makes it possible to select an optimal practice method based on the short video.

[0074] The video generation unit uses the emotion estimation function to monitor the player's emotional response when viewing the short video in real time, and can provide mental support during play. The video generation unit, for example, uses the emotion estimation function to monitor the player's emotional response when viewing the short video in real time. For example, it analyzes the player's facial expressions and voice data and calculates an emotion score. This makes it possible to monitor the player's emotional response in real time and provide mental support.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The golf advice system can further include a biometric data acquisition unit. The biometric data acquisition unit acquires biometric information such as the player's heart rate, body temperature, and sweat rate. For example, by analyzing heart rate fluctuations, it is possible to grasp the player's level of tension and concentration. In addition, by monitoring body temperature and sweat rate, it is possible to support the player's physical condition management. This makes it possible to provide more personalized advice based on the player's biometric information.

[0077] The golf advice system can also be equipped with a voice assistant unit. The voice assistant unit recognizes the player's voice commands and provides advice in real time. For example, if the player says, "Tell me how to play the next hole," the voice assistant unit will suggest the optimal strategy based on course and weather information. Also, if the player asks, "What's the wind speed?" the system can announce the current wind speed by voice. This allows the player to obtain the necessary information hands-free, allowing them to concentrate on playing.

[0078] The golf advice system may further include a social feedback unit. The social feedback unit collects feedback and advice from other players and provides it to the player. For example, it may display other players' opinions on strategies for a particular hole. It may also allow players to upload videos of their own play and receive comments and advice from other players. This allows players to take advantage of the experience and knowledge of other players and improve the quality of their play.

[0079] The golf advice system may further include a virtual reality (VR) section. The VR section allows players to experience and practice courses in a virtual space. For example, players can wear VR goggles and walk around a real course in a virtual space. They can also simulate shots on specific holes and practice optimal strokes. This allows players to experience a realistic practice environment without having to go to a real course.

[0080] The golf advice system may further include an ecological data acquisition unit. The ecological data acquisition unit collects environmental data about the golf course and provides it to the player. For example, the system may acquire data about the vegetation, animal habitats, and water quality of the golf course and provide the information to the player. The system may also provide advice to help the player play in an environmentally friendly manner. This allows the player to enjoy golf in an environmentally friendly manner.

[0081] The golf advice system can further use emotion estimation to analyze a player's motivation and tailor advice based on that motivation. For example, if a player is tired, it can provide advice on how to relax. If a player is highly motivated, it can suggest a more challenging strategy. This allows it to provide optimal advice based on the player's motivation.

[0082] The golf advice system can further use emotion estimation to analyze the player's level of concentration and tailor advice based on that level. For example, if the player is concentrating, detailed technical advice can be provided. If the player is losing concentration, simple ways to refresh can be suggested. This allows the system to provide optimal advice according to the player's level of concentration.

[0083] The golf advice system can also use emotion estimation to analyze a player's stress level and tailor advice based on that stress level. For example, if a player is feeling high stress, the system can suggest breathing techniques or stretches to help them relax. If the player is relaxed, the system can provide advice to improve concentration. This allows the system to provide optimal advice based on the player's stress level.

[0084] The golf advice system can further use emotion estimation to analyze a player's confidence level and tailor advice based on that level. For example, if a player is confident, a more challenging shot can be suggested. On the other hand, if a player lacks confidence, a safer shot can be suggested. This allows the system to provide optimal advice based on the player's confidence level.

[0085] The golf advice system can further use the emotion estimation function to analyze the player's satisfaction level and adjust advice based on that satisfaction level. For example, if the player feels highly satisfied, the system can suggest further challenges. If the player is not satisfied, the system can provide advice to help the player experience a simple success. This allows the system to provide optimal advice based on the player's satisfaction level.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The course information acquisition unit acquires course information about the golf course. For example, the course information acquisition unit obtains detailed information about the layout of each hole on the golf course, the location of obstacles, the shape of the green, and so on. Step 2: The ball position acquisition unit acquires the current position of the ball. For example, the ball position acquisition unit may analyze an image taken by the player with a smartphone and identify the exact position of the ball. Step 3: The weather information acquisition unit acquires weather information, such as wind speed, wind direction, temperature, and humidity, and analyzes the information. Step 4: The score status acquisition unit acquires the player's score status. For example, the score status acquisition unit acquires and analyzes the player's score for that day and past scores. Step 5: The advice generator generates advice based on this information. For example, the advice generator might generate specific instructions such as, "Swing the 7-iron lightly up to waist height. Aim toward the right side of the tree in front of you." Step 6: The video generation unit generates a short video. For example, the video generation unit generates a short video that serves as a model, such as "This is how you should hit the ball," and presents it to the player.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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. 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 course information acquisition unit that acquires golf course course information; a ball position acquisition unit that acquires the current position of the ball; a weather information acquisition unit that acquires weather information; a score status acquisition unit that acquires the score status of a player; an advice generation unit that generates advice based on the information; a video generation unit that generates short videos; A system characterized by:

2. The course information acquisition unit Drones are used to perform 3D mapping of the course in real time, and generative AI analyzes the data to provide the latest information on the course.

2. The system of claim 1.

3. The ball position acquisition unit In addition to image analysis technology, LiDAR sensors are used to precisely scan the ball's position and terrain, and the generated AI analyzes the data.

2. The system of claim 1.

4. The weather information acquisition unit In addition to the weather information, past weather data is analyzed to predict playing performance under specific weather conditions.

2. The system of claim 1.

5. The score status acquisition unit In addition to the score, the past playing data of the player is analyzed to understand the long-term performance trends.

2. The system of claim 1.

6. The advice generation unit In addition to giving advice on how to hit the ball, the system analyzes the player's swing form in real time and points out areas for improvement.

2. The system of claim 1.

7. The image generation unit In addition to the short video, the player's swing is recreated in the 3D model to provide different perspectives.

2. The system of claim 1.

8. The course information acquisition unit Analyze the stress and satisfaction that the player has felt on the hole in the past and customize advice based on that information 2. The system of claim 1.

Images