System

Abstract

An object of a system according to an embodiment is to analyze data of a swing of a player and a carry and a direction of a ball and provide a recommendation of a play in real time.SOLUTION: A system includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data on a swing of a player or a flight distance and direction of a ball. The analyzing section analyzes the data collected by the collecting section and specifies a play style and a weak point of the player. The recommendation unit provides a play recommendation in real time on the basis of the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately analyze data on a player's swing, ball distance, and direction in real time to provide appropriate play recommendations, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data on a player's swing and the distance and direction of the ball, and provide play recommendations in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data on a player's swing or the distance and direction of the ball. The analysis unit analyzes the data collected by the collection unit to identify the player's playing style and weaknesses. The recommendation unit provides play recommendations in real time based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data on a player's swing and the distance and direction of the ball, and provide play recommendations in real time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A golf play support system according to an embodiment of the present invention uses sensors and a generation AI to automatically track a golf player's shots and provide play recommendations in real time. The golf play support system collects data such as the player's swing and the ball's distance and direction, analyzes the data, and provides play recommendations in real time. For example, in the golf play support system, a sensor automatically tracks shots during a round. The sensor collects data such as the player's swing and the ball's distance and direction. For example, the sensor can measure the player's swing speed and angle to determine the ball's distance and direction. Next, the golf play support system analyzes the collected data using a generation AI. The generation AI analyzes the player's swing data and shot data to identify the player's playing style and weaknesses. For example, the generation AI can analyze the player's swing habits and shot patterns and identify areas for improvement. Furthermore, in the golf play support system, the generation AI provides play recommendations in real time. For example, the generation AI can suggest optimal clubs and shot strategies based on the player's current situation. This allows players to receive appropriate advice in real time and improve the quality of their play. This allows the golf play support system to automatically track the player's shots and provide play recommendations in real time. For example, players can select the optimal club and strategy and work to improve their swing. Furthermore, by selecting the club suggested by the generative AI, players can hit more accurate shots. Furthermore, by incorporating the swing improvements suggested by the generative AI, players can improve the accuracy of their swing.

[0029] A golf play support system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data on a player's swing or ball distance and direction. The collection unit can collect, for example, data on a player's swing speed and angle, and data on a ball's distance and direction using a swing sensor or a ball tracking sensor. The collection unit can measure, for example, a player's swing speed using a swing sensor. The collection unit can also measure a ball's distance using a ball tracking sensor. The collection unit can also collect data on swing speed and ball distance simultaneously by combining a swing sensor and a ball tracking sensor. The analysis unit analyzes the data collected by the collection unit to identify a player's playing style and weaknesses. The analysis unit can analyze a player's swing data using, for example, a generation AI. The generation AI can analyze a player's swing habits and shot patterns to identify areas for improvement. The analysis unit can also analyze a player's shot data using the generation AI to identify a player's playing style. Furthermore, the analysis unit can use the generation AI to combine and analyze the player's swing data and shot data to identify the player's weaknesses. The recommendation unit provides play recommendations in real time based on the analysis results obtained by the analysis unit. For example, the recommendation unit can use the generation AI to suggest the optimal club based on the player's current situation. The generation AI can analyze the player's swing data and shot data and select the optimal club. The recommendation unit can also use the generation AI to suggest a shot strategy based on the player's current situation. Furthermore, the recommendation unit can use the generation AI to suggest areas for improvement in the player's swing. As a result, the golf play support system according to the embodiment can collect and analyze data such as the player's swing, ball distance, and direction, and provide play recommendations in real time.

[0030] The collection unit can collect the player's swing speed and angle, and the ball's flight distance or direction using a swing sensor or a ball tracking sensor. Examples of swing sensors include an acceleration sensor and a gyro sensor. The collection unit can measure the player's swing speed using an acceleration sensor, for example. The collection unit can also measure the player's swing angle using a gyro sensor. Furthermore, the collection unit can combine an acceleration sensor and a gyro sensor to simultaneously measure the swing speed and angle. Examples of ball tracking sensors include a radar sensor and a camera-based tracking system. The collection unit can measure the ball's flight distance using a radar sensor, for example. The collection unit can also measure the ball's direction using a camera-based tracking system. Furthermore, the collection unit can combine a radar sensor and a camera-based tracking system to simultaneously measure the ball's flight distance and direction. Thus, by using the swing sensor or the ball tracking sensor, the player's swing speed and angle, and the ball's flight distance and direction can be accurately collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data obtained from a swing sensor or a ball tracking sensor into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze a player's swing data or shot data to identify the player's playing style or weaknesses. The swing data includes, for example, swing speed, angle, and trajectory. The analysis unit can analyze the swing data using, for example, a generation AI to identify the player's swing habits. The analysis unit can also analyze the swing data using the generation AI to identify areas for swing improvement. The analysis unit can also analyze the swing data using the generation AI to identify the player's swing pattern. The shot data includes, for example, ball flight distance, direction, and spin. The analysis unit can analyze the shot data using, for example, a generation AI to evaluate the accuracy of the player's shot. The analysis unit can also analyze the shot data using the generation AI to identify areas for shot improvement. The analysis unit can also analyze the shot data using the generation AI to identify the player's shot pattern. Thus, by analyzing the player's swing data and shot data, the player's playing style and weaknesses can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input swing data and shot data into the generation AI and have the generation AI identify the player's playing style and weaknesses.

[0032] The recommendation unit can suggest an appropriate club or shot strategy based on the player's current situation. Current situations include, for example, the player's location, weather conditions, and course conditions. The recommendation unit can, for example, use a generation AI to suggest an optimal club based on the player's current location. The recommendation unit can also use a generation AI to suggest a shot strategy based on weather conditions. The recommendation unit can also use a generation AI to suggest a shot strategy based on course conditions. The appropriate club can include, for example, the club type, loft angle, shaft stiffness, and so on. The recommendation unit can, for example, use a generation AI to analyze the player's swing data and shot data to select the optimal club. The recommendation unit can also use a generation AI to adjust the club selection criteria based on the player's current situation. The shot strategy can include, for example, the shot direction, force, target point, and so on. The recommendation unit can, for example, use a generation AI to analyze the player's swing data and shot data to suggest the optimal shot direction. The recommendation unit can also use the generation AI to suggest the amount of force to use for a shot based on the player's current situation. Furthermore, the recommendation unit can also use the generation AI to analyze the player's swing data and shot data and suggest the optimal target point. This allows the player to receive appropriate advice in real time by suggesting the optimal club and shot strategy based on the player's current situation. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the player's current situation data into the generation AI and have the generation AI suggest the optimal club and shot strategy.

[0033] The recommendation unit can suggest improvements to the player's swing. Examples of improvements to the swing include correcting the swing trajectory and adjusting the amount of force applied. The recommendation unit can, for example, use a generation AI to analyze the player's swing data and suggest corrections to the swing trajectory. The recommendation unit can also use the generation AI to analyze the player's swing data and suggest adjustments to the amount of force applied. The recommendation unit can also analyze the player's swing data and suggest improvements to the timing of the swing using the generation AI. By suggesting improvements to the player's swing, the player can improve the accuracy of their swing. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the player's swing data into the generation AI and have the generation AI execute suggestions for improvements to the swing.

[0034] The collection unit can analyze the player's past swing data and select an appropriate collection method. For example, the collection unit can detect a specific swing pattern from the player's past swing data and collect data based on that pattern. The collection unit can also analyze the player's past swing data and identify the most effective collection timing. Furthermore, the collection unit can prioritize data collection when using a specific club based on the player's past swing data. This allows the optimal collection method to be selected by analyzing the player's past swing data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the player's past swing data to the generation AI and cause the generation AI to select the optimal collection method.

[0035] When collecting data, the collection unit can filter the data based on the player's current physical condition and environmental conditions. For example, the collection unit monitors the player's heart rate and body temperature, and suspends data collection if an abnormality is detected. The collection unit can also adjust the accuracy of data collection by taking environmental conditions (wind speed, temperature, etc.) into consideration. Furthermore, the collection unit can collect detailed data when the player's physical condition is good. This allows for more accurate data to be collected by filtering data collection based on the player's physical condition and environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the player's physical condition data and environmental condition data into the generation AI and have the generation AI perform data collection filtering.

[0036] When collecting data, the collection unit can select an appropriate collection means depending on the player's input method. For example, if the player uses voice input, the collection unit can collect data using voice recognition technology. Furthermore, if the player uses text input, the collection unit can also collect data using text analysis technology. Furthermore, if the player uses gesture input, the collection unit can also collect data using gesture recognition technology. This allows efficient data collection by selecting the optimal collection means depending on the player's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's input data to a generation AI and have the generation AI select the optimal collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data based on the player's geographical location information. For example, if the player is on a specific hole, the collection unit prioritizes collecting data related to that hole. Furthermore, if the player is playing in a specific area, the collection unit can also collect data taking into account the environmental conditions of the area. Furthermore, if the player is playing on a specific course, the collection unit can also collect data based on the characteristics of the course. Thus, by collecting data taking into account the player's geographical location information, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the player's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] When collecting data, the collection unit can analyze the player's social media activities and collect related data. For example, the collection unit can analyze swing videos shared by the player on social media and collect related data. The collection unit can also analyze the content of the player's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the player's friends on social media. In this way, related data can be collected by analyzing the player's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's social media data into the generation AI and cause the generation AI to collect related data.

[0039] The collection unit can customize the collection method by reflecting the player's past feedback when collecting data. For example, the collection unit can adjust the accuracy of data collection based on feedback provided by the player in the past. The collection unit can also prioritize a specific data collection method by referring to the player's past feedback. Furthermore, the collection unit can adjust the timing of data collection by reflecting the player's past feedback. In this way, the collection method can be customized by reflecting the player's past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important swing data. The analysis unit can also perform a concise analysis on basic swing data. Furthermore, the analysis unit can focus on analyzing specific shot data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a swing analysis algorithm to swing data. The analysis unit can also apply a shot analysis algorithm to shot data. Furthermore, the analysis unit can apply a flight distance analysis algorithm to ball flight distance data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the player's past analysis results. The analysis unit, for example, corrects the current analysis result based on the player's past analysis results. The analysis unit can also improve the analysis accuracy of specific swing data by referring to the player's past analysis results. Furthermore, the analysis unit can also improve the analysis accuracy of shot data by using the player's past analysis results. In this way, the accuracy of the current analysis result can be improved by referring to the player's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the player's past analysis results into the generation AI and cause the generation AI to improve the analysis accuracy.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when data was collected. For example, the analysis unit prioritizes analysis of the most recent swing data. The analysis unit can also prioritize analysis of important shot data. Furthermore, the analysis unit can prioritize analysis of data collected during a specific period. In this way, by determining the priority of analysis based on the time when data was collected, the most recent data or important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of highly relevant swing data. The analysis unit can also prioritize analysis of highly relevant shot data. Furthermore, the analysis unit can also prioritize analysis of highly relevant ball distance data. In this way, by adjusting the order of analysis based on the relevance of data, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the player's level of expertise. For example, if the player is a beginner, the analysis unit can provide analysis results using simple terminology. If the player is an intermediate player, the analysis unit can also provide analysis results using appropriate technical terminology. If the player is an advanced player, the analysis unit can also provide analysis results using detailed technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the player's level of expertise, analysis results that are easy for the player to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the player's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the player's current situation. For example, if the player is relaxed, the recommendation unit can provide a detailed recommendation. Furthermore, if the player is nervous, the recommendation unit can provide a concise and to-the-point recommendation. Furthermore, if the player is concentrating, the recommendation unit can provide a recommendation that focuses on specific swing data. In this way, by adjusting the level of detail of the recommendation based on the player's current situation, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input data on the player's current situation to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0047] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the player's past recommendation results. The recommendation unit, for example, corrects the current recommendation based on the player's past recommendation results. The recommendation unit can also improve the accuracy of recommendations for specific swing data by referring to the player's past recommendation results. Furthermore, the recommendation unit can also improve the accuracy of recommendations for shot data by using the player's past recommendation results. In this way, the accuracy of current recommendations can be improved by referring to the player's past recommendation results. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the player's past recommendation results into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0048] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the player's playing style. For example, if the player has an aggressive playing style, the recommendation unit can suggest a risky shot. Furthermore, if the player has a conservative playing style, the recommendation unit can also suggest a safe shot. Furthermore, if the player has a balanced playing style, the recommendation unit can also suggest the optimal shot depending on the situation. By applying different recommendation algorithms depending on the player's playing style, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input the player's playing style data into the generation AI and cause the generation AI to apply an appropriate recommendation algorithm.

[0049] When making recommendations, the recommendation unit can determine the priority of recommendations based on the player's current physical condition and environmental conditions. For example, if the player is tired, the recommendation unit can prioritize recommendations to conserve energy. The recommendation unit can also suggest aggressive shots if the player is in good physical condition. Furthermore, the recommendation unit can also suggest optimal shots taking environmental conditions (wind speed, temperature, etc.) into consideration. This allows for more appropriate recommendations to be provided by determining the priority of recommendations based on the player's current physical condition and environmental conditions. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the player's physical condition data and environmental condition data into the generation AI and have the generation AI determine the priority of recommendations.

[0050] When making recommendations, the recommendation unit can provide optimal recommendations by taking into account the player's geographical location information. For example, if the player is on a specific hole, the recommendation unit can suggest the optimal club or shot for that hole. In addition, if the player is playing in a specific region, the recommendation unit can provide recommendations by taking into account the characteristics of that region. Furthermore, if the player is playing on a specific course, the recommendation unit can provide recommendations based on the characteristics of that course. In this way, by taking into account the player's geographical location information, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the player's geographical location information into the generation AI and cause the generation AI to provide optimal recommendations.

[0051] When making recommendations, the recommendation unit can analyze the player's social media activity and provide relevant recommendations. For example, the recommendation unit can analyze swing videos shared by the player on social media and provide relevant recommendations. The recommendation unit can also analyze the content of the player's social media posts and provide relevant recommendations. Furthermore, the recommendation unit can provide relevant recommendations by referring to the activity of the player's friends on social media. In this way, relevant recommendations can be provided by analyzing the player's social media activity. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the player's social media data into the generation AI and cause the generation AI to provide relevant recommendations.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The golf play support system may further include a health management unit that monitors the player's health condition. The health management unit collects vital data such as the player's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if the player's heart rate is abnormally high, the analysis unit may suggest that the player take a break. If the player's body temperature is high, the analysis unit may also recommend that the player hydrate. Furthermore, if the player's blood pressure is stable, the analysis unit may recommend that the player play more actively. This makes it possible to provide appropriate advice that takes the player's health condition into consideration.

[0054] The golf play support system can also be equipped with a 3D analysis unit that analyzes a player's swing form in 3D. The 3D analysis unit uses multiple cameras to capture the player's swing from multiple angles and generate a 3D model. For example, it can visualize the trajectory of the player's swing in 3D and provide the model to the analysis unit. It can also analyze the movement of each part of the player's body in detail to identify areas for improvement in the swing. It can also compare the player's swing form with that of other professional golfers and suggest specific ways to improve. This makes it easier for players to visually understand problems with their swing.

[0055] The golf play support system can further include a practice management unit that manages the player's practice history. The practice management unit collects the player's past practice data and provides it to the analysis unit. For example, the practice management unit can record the player's practice frequency and practice content, and the analysis unit can evaluate the effectiveness of practice based on that data. The system can also analyze the player's practice history and identify areas for improvement in specific swing patterns and shots. Furthermore, the system can suggest points that the player should focus on in the next practice session based on the player's practice history. This allows the player to practice more efficiently.

[0056] The golf play support system may further include a sharing unit that shares a player's swing data in real time. The sharing unit can upload the player's swing data to the cloud and share it with other players and coaches. For example, a player can send swing data to a coach and receive feedback in real time. Players can also compare swing data with other players and exchange advice. Furthermore, the sharing unit can post the player's swing data on social media to widely solicit opinions. This allows players to find areas for improvement in their swing from a variety of perspectives.

[0057] The golf play support system may further include a virtual reality (VR) training unit based on the player's swing data. The VR training unit provides training in a virtual environment using the player's swing data. For example, the player can wear VR goggles and practice their swing on a virtual golf course. The VR training unit can also reflect the player's swing data in real time and provide feedback in the virtual environment. Furthermore, the player can play against other players in the virtual environment to enhance their competitive spirit. This allows the player to effectively train in an environment that closely resembles a real golf course.

[0058] The golf play support system can also be equipped with an AI coaching unit that uses the player's swing data. The AI ​​coaching unit analyzes the player's swing data and provides specific coaching advice. For example, it can analyze the player's swing trajectory and point out areas for improvement. It can also suggest the optimal swing form based on the player's swing speed and angle. Furthermore, the AI ​​coaching unit can compare the player's current swing data with their past swing data and evaluate their progress. This allows the player to receive specific, practical advice that can be used to improve their swing.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects data on the player's swing or the distance and direction of the ball. The collection unit can collect the player's swing speed and angle, and the ball's distance and direction, using, for example, a swing sensor or a ball tracking sensor. The collection unit can also measure the player's swing speed using a swing sensor and measure the ball's distance using a ball tracking sensor. Furthermore, the collection unit can combine the swing sensor and the ball tracking sensor to simultaneously collect the swing speed and the ball's distance. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the player's playing style and weaknesses. The analysis unit can use the generation AI to analyze the player's swing data and analyze the player's swing habits and shot patterns to identify areas for improvement. The generation AI can also analyze the player's shot data to identify the player's playing style. Furthermore, the generation AI can also combine and analyze the player's swing data and shot data to identify the player's weaknesses. Step 3: The recommendation unit provides play recommendations in real time based on the analysis results obtained by the analysis unit. The recommendation unit uses generation AI to suggest the optimal club based on the player's current situation. The generation AI can analyze the player's swing data and shot data and select the optimal club. The recommendation unit can also use generation AI to suggest a shot strategy based on the player's current situation. Furthermore, the generation AI can also suggest areas for improvement to the player's swing.

[0061] (Example 2) A golf play support system according to an embodiment of the present invention uses sensors and a generation AI to automatically track a golf player's shots and provide play recommendations in real time. The golf play support system collects data such as the player's swing and the ball's distance and direction, analyzes the data, and provides play recommendations in real time. For example, in the golf play support system, a sensor automatically tracks shots during a round. The sensor collects data such as the player's swing and the ball's distance and direction. For example, the sensor can measure the player's swing speed and angle to determine the ball's distance and direction. Next, the golf play support system analyzes the collected data using a generation AI. The generation AI analyzes the player's swing data and shot data to identify the player's playing style and weaknesses. For example, the generation AI can analyze the player's swing habits and shot patterns and identify areas for improvement. Furthermore, in the golf play support system, the generation AI provides play recommendations in real time. For example, the generation AI can suggest optimal clubs and shot strategies based on the player's current situation. This allows players to receive appropriate advice in real time and improve the quality of their play. This allows the golf play support system to automatically track the player's shots and provide play recommendations in real time. For example, players can select the optimal club and strategy and work to improve their swing. Furthermore, by selecting the club suggested by the generative AI, players can hit more accurate shots. Furthermore, by incorporating the swing improvements suggested by the generative AI, players can improve the accuracy of their swing.

[0062] A golf play support system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data on a player's swing or ball distance and direction. The collection unit can collect, for example, data on a player's swing speed and angle, and data on a ball's distance and direction using a swing sensor or a ball tracking sensor. The collection unit can measure, for example, a player's swing speed using a swing sensor. The collection unit can also measure a ball's distance using a ball tracking sensor. The collection unit can also collect data on swing speed and ball distance simultaneously by combining a swing sensor and a ball tracking sensor. The analysis unit analyzes the data collected by the collection unit to identify a player's playing style and weaknesses. The analysis unit can analyze a player's swing data using, for example, a generation AI. The generation AI can analyze a player's swing habits and shot patterns to identify areas for improvement. The analysis unit can also analyze a player's shot data using the generation AI to identify a player's playing style. Furthermore, the analysis unit can use the generation AI to combine and analyze the player's swing data and shot data to identify the player's weaknesses. The recommendation unit provides play recommendations in real time based on the analysis results obtained by the analysis unit. For example, the recommendation unit can use the generation AI to suggest the optimal club based on the player's current situation. The generation AI can analyze the player's swing data and shot data and select the optimal club. The recommendation unit can also use the generation AI to suggest a shot strategy based on the player's current situation. Furthermore, the recommendation unit can use the generation AI to suggest areas for improvement in the player's swing. As a result, the golf play support system according to the embodiment can collect and analyze data such as the player's swing, ball distance, and direction, and provide play recommendations in real time.

[0063] The collection unit can collect the player's swing speed and angle, and the ball's flight distance or direction using a swing sensor or a ball tracking sensor. Examples of swing sensors include an acceleration sensor and a gyro sensor. The collection unit can measure the player's swing speed using an acceleration sensor, for example. The collection unit can also measure the player's swing angle using a gyro sensor. Furthermore, the collection unit can combine an acceleration sensor and a gyro sensor to simultaneously measure the swing speed and angle. Examples of ball tracking sensors include a radar sensor and a camera-based tracking system. The collection unit can measure the ball's flight distance using a radar sensor, for example. The collection unit can also measure the ball's direction using a camera-based tracking system. Furthermore, the collection unit can combine a radar sensor and a camera-based tracking system to simultaneously measure the ball's flight distance and direction. Thus, by using the swing sensor or the ball tracking sensor, the player's swing speed and angle, and the ball's flight distance and direction can be accurately collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data obtained from a swing sensor or a ball tracking sensor into the generation AI and have the generation AI analyze the data.

[0064] The analysis unit can analyze a player's swing data or shot data to identify the player's playing style or weaknesses. The swing data includes, for example, swing speed, angle, and trajectory. The analysis unit can analyze the swing data using, for example, a generation AI to identify the player's swing habits. The analysis unit can also analyze the swing data using the generation AI to identify areas for swing improvement. The analysis unit can also analyze the swing data using the generation AI to identify the player's swing pattern. The shot data includes, for example, ball flight distance, direction, and spin. The analysis unit can analyze the shot data using, for example, a generation AI to evaluate the accuracy of the player's shot. The analysis unit can also analyze the shot data using the generation AI to identify areas for shot improvement. The analysis unit can also analyze the shot data using the generation AI to identify the player's shot pattern. Thus, by analyzing the player's swing data and shot data, the player's playing style and weaknesses can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input swing data and shot data into the generation AI and have the generation AI identify the player's playing style and weaknesses.

[0065] The recommendation unit can suggest an appropriate club or shot strategy based on the player's current situation. Current situations include, for example, the player's location, weather conditions, and course conditions. The recommendation unit can, for example, use a generation AI to suggest an optimal club based on the player's current location. The recommendation unit can also use a generation AI to suggest a shot strategy based on weather conditions. The recommendation unit can also use a generation AI to suggest a shot strategy based on course conditions. The appropriate club can include, for example, the club type, loft angle, shaft stiffness, and so on. The recommendation unit can, for example, use a generation AI to analyze the player's swing data and shot data to select the optimal club. The recommendation unit can also use a generation AI to adjust the club selection criteria based on the player's current situation. The shot strategy can include, for example, the shot direction, force, target point, and so on. The recommendation unit can, for example, use a generation AI to analyze the player's swing data and shot data to suggest the optimal shot direction. The recommendation unit can also use the generation AI to suggest the amount of force to use for a shot based on the player's current situation. Furthermore, the recommendation unit can also use the generation AI to analyze the player's swing data and shot data and suggest the optimal target point. This allows the player to receive appropriate advice in real time by suggesting the optimal club and shot strategy based on the player's current situation. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the player's current situation data into the generation AI and have the generation AI suggest the optimal club and shot strategy.

[0066] The recommendation unit can suggest improvements to the player's swing. Examples of improvements to the swing include correcting the swing trajectory and adjusting the amount of force applied. The recommendation unit can, for example, use a generation AI to analyze the player's swing data and suggest corrections to the swing trajectory. The recommendation unit can also use the generation AI to analyze the player's swing data and suggest adjustments to the amount of force applied. The recommendation unit can also analyze the player's swing data and suggest improvements to the timing of the swing using the generation AI. By suggesting improvements to the player's swing, the player can improve the accuracy of their swing. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the player's swing data into the generation AI and have the generation AI execute suggestions for improvements to the swing.

[0067] The collection unit can estimate the player's emotions and adjust the timing of data collection based on the estimated player's emotions. For example, if the player is nervous, the collection unit temporarily suspends data collection until the player relaxes. Furthermore, if the player is concentrating, the collection unit can collect swing data at that timing. Furthermore, if the player is tired, the collection unit can resume data collection after a break. This allows for more appropriate data collection by adjusting the timing of data collection based on the player's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0068] The collection unit can analyze the player's past swing data and select an appropriate collection method. For example, the collection unit can detect a specific swing pattern from the player's past swing data and collect data based on that pattern. The collection unit can also analyze the player's past swing data and identify the most effective collection timing. Furthermore, the collection unit can prioritize data collection when using a specific club based on the player's past swing data. This allows the optimal collection method to be selected by analyzing the player's past swing data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the player's past swing data to the generation AI and cause the generation AI to select the optimal collection method.

[0069] When collecting data, the collection unit can filter the data based on the player's current physical condition and environmental conditions. For example, the collection unit monitors the player's heart rate and body temperature, and suspends data collection if an abnormality is detected. The collection unit can also adjust the accuracy of data collection by taking environmental conditions (wind speed, temperature, etc.) into consideration. Furthermore, the collection unit can collect detailed data when the player's physical condition is good. This allows for more accurate data to be collected by filtering data collection based on the player's physical condition and environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the player's physical condition data and environmental condition data into the generation AI and have the generation AI perform data collection filtering.

[0070] When collecting data, the collection unit can select an appropriate collection means depending on the player's input method. For example, if the player uses voice input, the collection unit can collect data using voice recognition technology. Furthermore, if the player uses text input, the collection unit can also collect data using text analysis technology. Furthermore, if the player uses gesture input, the collection unit can also collect data using gesture recognition technology. This allows efficient data collection by selecting the optimal collection means depending on the player's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's input data to a generation AI and have the generation AI select the optimal collection means.

[0071] The collection unit can estimate the player's emotions and determine the priority of data to be collected based on the estimated player's emotions. For example, if the player is relaxed, the collection unit can prioritize collecting detailed swing data. Furthermore, if the player is nervous, the collection unit can prioritize collecting basic swing data. Furthermore, if the player is concentrating, the collection unit can prioritize collecting specific shot data. By prioritizing data to be collected based on the player's emotions, more important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the player's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0072] When collecting data, the collection unit can prioritize collecting highly relevant data based on the player's geographical location information. For example, if the player is on a specific hole, the collection unit prioritizes collecting data related to that hole. Furthermore, if the player is playing in a specific area, the collection unit can also collect data taking into account the environmental conditions of the area. Furthermore, if the player is playing on a specific course, the collection unit can also collect data based on the characteristics of the course. Thus, by collecting data taking into account the player's geographical location information, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the player's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0073] When collecting data, the collection unit can analyze the player's social media activities and collect related data. For example, the collection unit can analyze swing videos shared by the player on social media and collect related data. The collection unit can also analyze the content of the player's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the player's friends on social media. In this way, related data can be collected by analyzing the player's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's social media data into the generation AI and cause the generation AI to collect related data.

[0074] The collection unit can customize the collection method by reflecting the player's past feedback when collecting data. For example, the collection unit can adjust the accuracy of data collection based on feedback provided by the player in the past. The collection unit can also prioritize a specific data collection method by referring to the player's past feedback. Furthermore, the collection unit can adjust the timing of data collection by reflecting the player's past feedback. In this way, the collection method can be customized by reflecting the player's past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0075] The analysis unit can estimate the player's emotions and adjust the presentation of the analysis based on the estimated player's emotions. For example, if the player is relaxed, the analysis unit can provide detailed analysis results. If the player is nervous, the analysis unit can also provide concise and concise analysis results. Furthermore, if the player is focused, the analysis unit can provide analysis results that focus on specific swing data. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the presentation of the analysis.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important swing data. The analysis unit can also perform a concise analysis on basic swing data. Furthermore, the analysis unit can focus on analyzing specific shot data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a swing analysis algorithm to swing data. The analysis unit can also apply a shot analysis algorithm to shot data. Furthermore, the analysis unit can apply a flight distance analysis algorithm to ball flight distance data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the player's past analysis results. The analysis unit, for example, corrects the current analysis result based on the player's past analysis results. The analysis unit can also improve the analysis accuracy of specific swing data by referring to the player's past analysis results. Furthermore, the analysis unit can also improve the analysis accuracy of shot data by using the player's past analysis results. In this way, the accuracy of the current analysis result can be improved by referring to the player's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the player's past analysis results into the generation AI and cause the generation AI to improve the analysis accuracy.

[0079] The analysis unit can estimate the player's emotions and adjust the length of the analysis based on the estimated player's emotions. For example, the analysis unit can perform a detailed analysis when the player is relaxed. The analysis unit can also perform a brief analysis when the player is nervous. Furthermore, the analysis unit can perform an analysis that focuses on specific swing data when the player is focused. This allows for more appropriate analysis results by adjusting the length of the analysis based on the player's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time when data was collected. For example, the analysis unit prioritizes analysis of the most recent swing data. The analysis unit can also prioritize analysis of important shot data. Furthermore, the analysis unit can prioritize analysis of data collected during a specific period. In this way, by determining the priority of analysis based on the time when data was collected, the most recent data or important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when data was collected into the generation AI and have the generation AI determine the priority of analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of highly relevant swing data. The analysis unit can also prioritize analysis of highly relevant shot data. Furthermore, the analysis unit can also prioritize analysis of highly relevant ball distance data. In this way, by adjusting the order of analysis based on the relevance of data, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0082] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the player's level of expertise. For example, if the player is a beginner, the analysis unit can provide analysis results using simple terminology. If the player is an intermediate player, the analysis unit can also provide analysis results using appropriate technical terminology. If the player is an advanced player, the analysis unit can also provide analysis results using detailed technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the player's level of expertise, analysis results that are easy for the player to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the player's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0083] The recommendation unit can estimate the player's emotions and adjust the way recommendations are presented based on the estimated player's emotions. For example, if the player is relaxed, the recommendation unit can provide detailed recommendations. Furthermore, if the player is nervous, the recommendation unit can provide concise, to-the-point recommendations. Furthermore, if the player is focused, the recommendation unit can provide recommendations that focus on specific swing data. This allows for more appropriate recommendations to be provided by adjusting the way recommendations are presented based on the player's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the way recommendations are presented.

[0084] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the player's current situation. For example, if the player is relaxed, the recommendation unit can provide a detailed recommendation. Furthermore, if the player is nervous, the recommendation unit can provide a concise and to-the-point recommendation. Furthermore, if the player is concentrating, the recommendation unit can provide a recommendation that focuses on specific swing data. In this way, by adjusting the level of detail of the recommendation based on the player's current situation, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input data on the player's current situation to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0085] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the player's past recommendation results. The recommendation unit, for example, corrects the current recommendation based on the player's past recommendation results. The recommendation unit can also improve the accuracy of recommendations for specific swing data by referring to the player's past recommendation results. Furthermore, the recommendation unit can also improve the accuracy of recommendations for shot data by using the player's past recommendation results. In this way, the accuracy of current recommendations can be improved by referring to the player's past recommendation results. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the player's past recommendation results into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0086] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the player's playing style. For example, if the player has an aggressive playing style, the recommendation unit can suggest a risky shot. Furthermore, if the player has a conservative playing style, the recommendation unit can also suggest a safe shot. Furthermore, if the player has a balanced playing style, the recommendation unit can also suggest the optimal shot depending on the situation. By applying different recommendation algorithms depending on the player's playing style, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI, for example. For example, the recommendation unit can input the player's playing style data into the generation AI and cause the generation AI to apply an appropriate recommendation algorithm.

[0087] The recommendation unit can estimate the player's emotions and adjust the length of the recommendation based on the estimated player's emotions. For example, if the player is relaxed, the recommendation unit can provide detailed recommendations. Furthermore, if the player is nervous, the recommendation unit can provide concise and to-the-point recommendations. Furthermore, if the player is focused, the recommendation unit can provide recommendations that focus on specific swing data. This allows for more appropriate recommendations to be provided by adjusting the length of the recommendation based on the player's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or without an AI. For example, the recommendation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the length of the recommendation.

[0088] When making recommendations, the recommendation unit can determine the priority of recommendations based on the player's current physical condition and environmental conditions. For example, if the player is tired, the recommendation unit can prioritize recommendations to conserve energy. The recommendation unit can also suggest aggressive shots if the player is in good physical condition. Furthermore, the recommendation unit can also suggest optimal shots taking environmental conditions (wind speed, temperature, etc.) into consideration. This allows for more appropriate recommendations to be provided by determining the priority of recommendations based on the player's current physical condition and environmental conditions. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the player's physical condition data and environmental condition data into the generation AI and have the generation AI determine the priority of recommendations.

[0089] When making recommendations, the recommendation unit can provide optimal recommendations by taking into account the player's geographical location information. For example, if the player is on a specific hole, the recommendation unit can suggest the optimal club or shot for that hole. In addition, if the player is playing in a specific region, the recommendation unit can provide recommendations by taking into account the characteristics of that region. Furthermore, if the player is playing on a specific course, the recommendation unit can provide recommendations based on the characteristics of that course. In this way, by taking into account the player's geographical location information, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the player's geographical location information into the generation AI and cause the generation AI to provide optimal recommendations.

[0090] When making recommendations, the recommendation unit can analyze the player's social media activity and provide relevant recommendations. For example, the recommendation unit can analyze swing videos shared by the player on social media and provide relevant recommendations. The recommendation unit can also analyze the content of the player's social media posts and provide relevant recommendations. Furthermore, the recommendation unit can provide relevant recommendations by referring to the activity of the player's friends on social media. In this way, relevant recommendations can be provided by analyzing the player's social media activity. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the player's social media data into the generation AI and cause the generation AI to provide relevant recommendations. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and recommendation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data on a player's swing and the ball's distance and direction using the camera 42 and microphone 38B of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and identifies the player's playing style and weaknesses. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides play recommendations in real time based on the analysis results. Each of the collection unit, analysis unit, and recommendation unit may also be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and recommendation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data on a player's swing and the ball's distance and direction using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and identifies the player's playing style and weaknesses. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides play recommendations in real time based on the analysis results. Each of the collection unit, analysis unit, and recommendation unit may also be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and recommendation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data on a player's swing and the ball's distance and direction using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the player's playing style and weaknesses. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides play recommendations in real time based on the analysis results. Each of the collection unit, analysis unit, and recommendation unit may also be realized, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and recommendation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data on the player's swing and the ball's distance and direction using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the player's playing style and weaknesses. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides play recommendations in real time based on the analysis results. Each of the collection unit, analysis unit, and recommendation unit may also be realized, for example, by the control unit 46A of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The golf play support system may further include a health management unit that monitors the player's health condition. The health management unit collects vital data such as the player's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if the player's heart rate is abnormally high, the analysis unit may suggest that the player take a break. If the player's body temperature is high, the analysis unit may also recommend that the player hydrate. Furthermore, if the player's blood pressure is stable, the analysis unit may recommend that the player play more actively. This makes it possible to provide appropriate advice that takes the player's health condition into consideration.

[0093] The golf play support system can also be equipped with a 3D analysis unit that analyzes a player's swing form in 3D. The 3D analysis unit uses multiple cameras to capture the player's swing from multiple angles and generate a 3D model. For example, it can visualize the trajectory of the player's swing in 3D and provide the model to the analysis unit. It can also analyze the movement of each part of the player's body in detail to identify areas for improvement in the swing. It can also compare the player's swing form with that of other professional golfers and suggest specific ways to improve. This makes it easier for players to visually understand problems with their swing.

[0094] The golf play support system can further include a psychological analysis unit that monitors the player's psychological state. The psychological analysis unit analyzes the player's facial expressions and tone of voice to estimate their emotions. For example, if the player is nervous, the analysis unit can suggest breathing techniques to help them relax. If the player is concentrating, the analysis unit can also provide advice to help them maintain their concentration. Furthermore, if the player is irritated, the analysis unit can suggest taking a short break to change their mood. This makes it possible to provide appropriate support according to the player's psychological state.

[0095] The golf play support system can further include a practice management unit that manages the player's practice history. The practice management unit collects the player's past practice data and provides it to the analysis unit. For example, the practice management unit can record the player's practice frequency and practice content, and the analysis unit can evaluate the effectiveness of practice based on that data. The system can also analyze the player's practice history and identify areas for improvement in specific swing patterns and shots. Furthermore, the system can suggest points that the player should focus on in the next practice session based on the player's practice history. This allows the player to practice more efficiently.

[0096] The golf play support system may further include a practice customization unit that estimates the player's emotions and customizes a practice menu based on the estimated emotions. The practice customization unit collects the player's emotional data and provides it to the analysis unit. For example, if the player is relaxed, the analysis unit can suggest a practice menu to improve concentration. If the player is tense, the analysis unit can suggest a light practice menu to help the player relax. Furthermore, if the player is tired, the analysis unit can suggest a short practice menu to conserve energy. This makes it possible to provide an optimal practice menu according to the player's emotions.

[0097] The golf play support system may further include a sharing unit that shares a player's swing data in real time. The sharing unit can upload the player's swing data to the cloud and share it with other players and coaches. For example, a player can send swing data to a coach and receive feedback in real time. Players can also compare swing data with other players and exchange advice. Furthermore, the sharing unit can post the player's swing data on social media to widely solicit opinions. This allows players to find areas for improvement in their swing from a variety of perspectives.

[0098] The golf play support system may further include a motivation management unit that estimates the player's emotions and provides feedback to increase motivation based on the estimated emotions. The motivation management unit collects the player's emotional data and provides it to the analysis unit. For example, if the player is feeling down, the analysis unit may display an encouraging message. Also, if the player hits a successful shot, the analysis unit may display a message of praise. Furthermore, if the player achieves a goal, the analysis unit may set the next goal and provide advice to maintain a sense of accomplishment. This can increase the player's motivation and encourage continuous practice.

[0099] The golf play support system may further include a virtual reality (VR) training unit based on the player's swing data. The VR training unit provides training in a virtual environment using the player's swing data. For example, the player can wear VR goggles and practice their swing on a virtual golf course. The VR training unit can also reflect the player's swing data in real time and provide feedback in the virtual environment. Furthermore, the player can play against other players in the virtual environment to enhance their competitive spirit. This allows the player to effectively train in an environment that closely resembles a real golf course.

[0100] The golf play support system may further include a difficulty adjustment unit that estimates the player's emotions and adjusts the difficulty of play based on the estimated emotions. The difficulty adjustment unit collects the player's emotional data and provides it to the analysis unit. For example, if the player is relaxed, the analysis unit can suggest a high-difficulty shot. Also, if the player is nervous, the analysis unit can suggest a low-difficulty shot. Furthermore, if the player is concentrating, the analysis unit can suggest a shot with an appropriate level of difficulty. This makes it possible to provide play at an optimal level of difficulty according to the player's emotions.

[0101] The golf play support system can also be equipped with an AI coaching unit that uses the player's swing data. The AI ​​coaching unit analyzes the player's swing data and provides specific coaching advice. For example, it can analyze the player's swing trajectory and point out areas for improvement. It can also suggest the optimal swing form based on the player's swing speed and angle. Furthermore, the AI ​​coaching unit can compare the player's current swing data with their past swing data and evaluate their progress. This allows the player to receive specific, practical advice that can be used to improve their swing.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects data on the player's swing or the distance and direction of the ball. The collection unit can collect the player's swing speed and angle, and the ball's distance and direction, using, for example, a swing sensor or a ball tracking sensor. The collection unit can also measure the player's swing speed using a swing sensor and measure the ball's distance using a ball tracking sensor. Furthermore, the collection unit can combine the swing sensor and the ball tracking sensor to simultaneously collect the swing speed and the ball's distance. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the player's playing style and weaknesses. The analysis unit can use the generation AI to analyze the player's swing data and analyze the player's swing habits and shot patterns to identify areas for improvement. The generation AI can also analyze the player's shot data to identify the player's playing style. Furthermore, the generation AI can also combine and analyze the player's swing data and shot data to identify the player's weaknesses. Step 3: The recommendation unit provides play recommendations in real time based on the analysis results obtained by the analysis unit. The recommendation unit uses generation AI to suggest the optimal club based on the player's current situation. The generation AI can analyze the player's swing data and shot data and select the optimal club. The recommendation unit can also use generation AI to suggest a shot strategy based on the player's current situation. Furthermore, the generation AI can also suggest areas for improvement to the player's swing.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on a player's swing or the distance and direction of a ball; an analysis unit that analyzes the data collected by the collection unit and identifies a player's playing style and weaknesses; a recommendation unit that provides play recommendations in real time based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Use swing or ball tracking sensors to collect the player's swing speed and angle, as well as the ball's flight distance or direction.

2. The system of claim 1.

3. The analysis unit Analyze a player's swing or shot data to identify their playing style or weaknesses 2. The system of claim 1.

4. The recommendation unit Suggest the appropriate club or shot strategy based on the player's current situation 2. The system of claim 1.

5. The recommendation unit Suggest improvements to the player's swing 2. The system of claim 1.

6. The collecting unit Estimate player emotions and adjust data collection timing based on the estimated player emotions 2. The system of claim 1.

7. The collecting unit Analyze the player's past swing data and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the player's current physical condition or environmental conditions 2. The system of claim 1.

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