system

Abstract

An object of a system according to an embodiment is to provide optimal advice to a golf player.SOLUTION: A system according to an embodiment includes a survey unit, an analysis unit, a weather information collection unit, and an advice providing unit. The survey unit is a system that provides a service as an excellent caddie to a golf player using the generated AI, and performs precise survey in centimeters in association with Michibiki's CLAS. The analysis part analyzes the past score data of the player and grasps a carry, peculiarity and the probability of a miss shot. The weather information collection unit collects weather information of a current location, and analyzes a wind direction and a temperature. The advice provision unit provides optimal advice to the player on the basis of data obtained by the survey unit, the analysis unit, and the weather information collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques make it difficult for golf players to receive optimal advice while playing, and there is room for improvement to improve the quality of play.

[0005] The system according to the embodiment aims to provide optimal advice to golf players. [Means for solving the problem]

[0006] The system according to the embodiment comprises a measurement unit, an analysis unit, a weather information collection unit, and an advice provision unit. The measurement unit is a system that uses a generation AI to provide golf players with the service of an excellent caddy, and works in conjunction with Michibiki's CLAS to perform precise measurements down to the centimeter. The analysis unit analyzes the player's past score data to determine their driving distance, habits, and the probability of mis-shots. The weather information collection unit collects weather information for the player's current location and analyzes wind direction and temperature. The advice provision unit provides the player with optimal advice based on the data obtained by the measurement unit, analysis unit, and weather information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal advice to a golf player. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The caddie service system according to an embodiment of the present invention uses generative AI to provide golf players with excellent caddie services. This system works in conjunction with Michibiki's CLAS to perform precise measurements down to the centimeter, analyze past score data, grasp the player's driving distance, habits, and probability of mis-shots, and then display the optimal club selection and shot direction on a smartphone screen. It also collects weather information for the player's current location and provides comprehensive advice that takes into account factors such as wind direction and temperature. This allows the caddie service system to provide comprehensive advice to players, enabling them to play more strategically.

[0029] The caddie service system according to the embodiment includes a measurement unit, an analysis unit, a weather information collection unit, and an advice provision unit. The measurement unit works in conjunction with the Michibiki CLAS to perform precise measurements down to the centimeter. For example, it can accurately determine the distance from the tee to the green and the distance to obstacles. The analysis unit analyzes a player's past score data to determine their driving distance, driving habits, and the probability of mishits. For example, it analyzes the player's average driving distance when hitting with a driver and the frequency of mishits with a particular club. The weather information collection unit collects weather information for the player's current location and analyzes wind direction and temperature. For example, it collects data such as wind speed, wind direction, temperature, and humidity and provides shot advice based on this information. The advice provision unit provides optimal advice to the player based on the data obtained by the measurement unit, analysis unit, and weather information collection unit. For example, it displays the optimal club selection for a particular hole, shot direction, and force level on a smartphone screen. This allows the caddie service system to provide comprehensive advice to the player, enabling more strategic play.

[0030] The surveying unit creates a 3D model of the golf course's topographical data, allowing the generation AI to suggest the optimal shot height and angle to the player in real time. For example, the surveying unit creates a 3D model of the golf course's topographical data, allowing the generation AI to suggest the optimal shot height and angle to the player in real time. For example, the optimal shot angle is calculated taking into account the elevation difference from the tee to the green. The 3D model is also used to provide a visual simulation of the player's shot. For example, a shot simulation is performed taking into account the position of obstacles and wind direction. Furthermore, the generation AI suggests shot height and angle to the player in real time based on the 3D model. For example, it calculates the optimal height and angle for a shot over a bunker and provides advice to the player. This allows the player to make more accurate shots by suggesting the optimal shot height and angle to the player in real time.

[0031] The analysis unit collects the player's swing data, and the generation AI can provide feedback on areas for swing improvement in real time. The analysis unit, for example, collects the player's swing data, and the generation AI provides feedback on areas for swing improvement in real time. For example, it analyzes the swing trajectory and speed and points out areas for improvement. Based on the swing data, the generation AI also suggests specific ways for the player to improve. For example, it gives advice on swing timing and weight transfer methods. Furthermore, the generation AI analyzes the swing data in real time and provides immediate feedback to the player. For example, if the swing trajectory is off, it points out areas for correction on the spot. This improves the accuracy of the swing by providing feedback on areas for swing improvement in real time.

[0032] The advice providing unit can analyze the player's real-time performance data and instantly update the advice. In the advice providing unit, for example, the generation AI analyzes the player's real-time performance data and instantly updates the advice. For example, the advice for the next shot is adjusted based on the results of the shot. Furthermore, the generation AI instantly provides advice to the player based on the real-time performance data. For example, the swing trajectory and force level are analyzed in real time and advice for the next shot is provided. Furthermore, the generation AI analyzes the player's real-time performance data and instantly updates the advice. For example, the advice for the next putt is adjusted based on the results of the putt. In this way, advice can be instantly updated based on the player's real-time performance data, enabling more effective play.

[0033] The advice providing unit can compare the player's past performance data with the current situation and propose the optimal strategy. For example, the generation AI compares the player's past performance data with the current situation and proposes the optimal strategy. For example, it proposes the optimal club selection for the current hole based on past data. Furthermore, the generation AI proposes the player the optimal strategy for the current situation based on past performance data. For example, it proposes the direction of the current shot taking into account past trends in mis-shots. Furthermore, the generation AI compares the player's past performance data with the current situation and proposes the optimal strategy. For example, it proposes the optimal amount of force for the shot on the current hole based on past successes. In this way, by comparing the player's past performance data with the current situation, it is possible to propose a more effective strategy.

[0034] The surveying unit can use the golf course topography data to provide a virtual reality practice environment, allowing players to simulate an actual course. The surveying unit, for example, uses the golf course topography data to provide a virtual reality (VR) practice environment. For example, a player can practice while simulating an actual course. In addition, during practice in the VR environment, the generative AI provides advice in real time. For example, it suggests shot heights and angles while simulating them in VR. Furthermore, based on the golf course topography data, practice in the VR environment allows players to experience an actual course. For example, it simulates a specific hole. This allows players to practice while simulating an actual course, which is useful for actual play.

[0035] The analysis unit compares the player's score data with other players, and the generation AI can provide a benchmark. The analysis unit, for example, compares the player's score data with other players, and the generation AI can provide a benchmark. For example, by comparing with players of the same level, the player's driving distance and score trends are analyzed. The generation AI also provides a benchmark for the player based on the score data of other players. For example, by comparing with data from top players, areas for improvement are identified. Furthermore, the player's score data is compared with other players, and the generation AI can provide a performance benchmark. For example, by comparing scores on the same course, the player's strengths and weaknesses are analyzed. In this way, a performance benchmark can be provided by comparing the player's score data with other players.

[0036] The advice providing unit can create an individual training plan using the player's performance data. In the advice providing unit, for example, the generation AI creates an individual training plan based on the player's performance data. For example, it suggests the frequency and content of practice for a specific club. The generation AI also analyzes the performance data and creates an optimal training plan for the player. For example, it suggests a practice method for a club that results in a high frequency of mis-shots. Furthermore, the generation AI creates an individual training plan using the player's performance data. For example, it suggests a practice method to improve performance on a specific hole. In this way, more effective training can be achieved by creating an individual training plan based on the player's performance data.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The caddie service system may further include a health management unit. The health management unit collects biometric data such as the player's heart rate and body temperature to monitor the player's health condition. For example, if the player's heart rate is high, the unit suggests taking a break. Also, if the player's body temperature is high, the unit may advise the player to drink more fluids. Furthermore, the health management unit may suggest adjusting the pace of play based on the player's health condition. For example, if fatigue is accumulating, the unit may advise the player to slow down. In this way, the player's health condition can be monitored and appropriate advice provided, enabling safer and more effective play.

[0039] The caddie service system may further include a social media integration unit. The social media integration unit provides a function for sharing a player's score and playing status on social media in real time. For example, it may automatically post shot results and scores to Twitter or Facebook. It may also display supportive messages from the player's friends and followers in real time. The social media integration unit may also provide a score comparison function with other players and a ranking display function. For example, it may compare scores with other players playing on the same course and display the ranking. This allows players to interact with other players through social media and increase motivation.

[0040] The caddie service system may further include an ecology management unit. The ecology management unit collects environmental data on the golf course and suggests environmentally friendly play to the player. For example, it may provide advice to encourage grass protection in specific areas. It may also provide suggestions for reducing environmental impact when selecting clubs and balls to use. Furthermore, the ecology management unit may guide the player on how to separate and recycle garbage. For example, it may suggest the appropriate method for disposing of used balls and tees. This allows the player to play in an environmentally friendly manner and contribute to the sustainable use of the golf course.

[0041] The caddie service system can further include a voice assistant unit. The voice assistant unit provides necessary information and advice in response to the player's voice commands. For example, if the player asks, "What is the distance of the next hole?", the distance will be announced by voice. Also, if the player asks, "What is the wind direction?", the current wind direction and wind speed can be announced by voice. Furthermore, the voice assistant unit can also suggest shot advice and club selection based on the player's voice commands. For example, if the player asks, "Which club should I use?", the optimal club will be suggested by voice. This allows the player to easily obtain the necessary information and play more effectively.

[0042] The caddie service system may further include a feedback collection unit. The feedback collection unit collects feedback from players and uses it to improve the system. For example, players may evaluate the content and accuracy of advice. Players may also provide opinions about the system's usability and functionality. Furthermore, the feedback collection unit may make suggestions to the generation AI to improve the accuracy and content of advice based on player feedback. For example, if advice on a particular hole is inappropriate, the feedback collection unit may analyze the cause and suggest improvements for the next play. In this way, player feedback can be utilized to improve the system's accuracy and usability.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The Surveying Department works with Michibiki's CLAS to carry out precise measurements down to the centimeter. For example, it can accurately determine the distance from the tee to the green and the distance to obstacles. Step 2: The analysis unit analyzes the player's past score data to determine the player's driving distance, driving habits, and the probability of mishits. For example, it analyzes the player's average driving distance when hitting with a driver, or the frequency of mishits with a particular club. Step 3: The weather information collection unit collects weather information for the current location and analyzes wind direction, temperature, etc. For example, it collects data such as wind speed, wind direction, temperature, and humidity, and provides advice on the shot based on this information. Step 4: The advice provider uses the data obtained by the surveying, analysis, and weather information collection units to provide the player with optimal advice, such as the best club to choose for a particular hole, shot direction, and power level, all of which are displayed on the smartphone screen.

[0045] (Example 2) The caddie service system according to an embodiment of the present invention uses generative AI to provide golf players with excellent caddie services. This system works in conjunction with Michibiki's CLAS to perform precise measurements down to the centimeter, analyze past score data, grasp the player's driving distance, habits, and probability of mis-shots, and then display the optimal club selection and shot direction on a smartphone screen. It also collects weather information for the player's current location and provides comprehensive advice that takes into account factors such as wind direction and temperature. This allows the caddie service system to provide comprehensive advice to players, enabling them to play more strategically.

[0046] The caddie service system according to the embodiment includes a measurement unit, an analysis unit, a weather information collection unit, and an advice provision unit. The measurement unit works in conjunction with the Michibiki CLAS to perform precise measurements down to the centimeter. For example, it can accurately determine the distance from the tee to the green and the distance to obstacles. The analysis unit analyzes a player's past score data to determine their driving distance, driving habits, and the probability of mishits. For example, it analyzes the player's average driving distance when hitting with a driver and the frequency of mishits with a particular club. The weather information collection unit collects weather information for the player's current location and analyzes wind direction and temperature. For example, it collects data such as wind speed, wind direction, temperature, and humidity and provides shot advice based on this information. The advice provision unit provides optimal advice to the player based on the data obtained by the measurement unit, analysis unit, and weather information collection unit. For example, it displays the optimal club selection for a particular hole, shot direction, and force level on a smartphone screen. This allows the caddie service system to provide comprehensive advice to the player, enabling more strategic play.

[0047] The surveying unit creates a 3D model of the golf course's topographical data, allowing the generation AI to suggest the optimal shot height and angle to the player in real time. For example, the surveying unit creates a 3D model of the golf course's topographical data, allowing the generation AI to suggest the optimal shot height and angle to the player in real time. For example, the optimal shot angle is calculated taking into account the elevation difference from the tee to the green. The 3D model is also used to provide a visual simulation of the player's shot. For example, a shot simulation is performed taking into account the position of obstacles and wind direction. Furthermore, the generation AI suggests shot height and angle to the player in real time based on the 3D model. For example, it calculates the optimal height and angle for a shot over a bunker and provides advice to the player. This allows the player to make more accurate shots by suggesting the optimal shot height and angle to the player in real time.

[0048] The analysis unit collects the player's swing data, and the generation AI can provide feedback on areas for swing improvement in real time. The analysis unit, for example, collects the player's swing data, and the generation AI provides feedback on areas for swing improvement in real time. For example, it analyzes the swing trajectory and speed and points out areas for improvement. Based on the swing data, the generation AI also suggests specific ways for the player to improve. For example, it gives advice on swing timing and weight transfer methods. Furthermore, the generation AI analyzes the swing data in real time and provides immediate feedback to the player. For example, if the swing trajectory is off, it points out areas for correction on the spot. This improves the accuracy of the swing by providing feedback on areas for swing improvement in real time.

[0049] The weather information collection unit can use the emotion estimation function to measure the player's level of tension and concentration, and suggest the timing of a shot based on that. The weather information collection unit, for example, uses the emotion estimation function to measure the player's level of tension and concentration, and suggests the timing of a shot based on that. For example, if the player is highly tense, the weather information collection unit provides advice to relax. The weather information collection unit also analyzes the player's emotional state in real time and suggests the optimal timing of a shot. For example, the weather information collection unit provides advice to hit a shot when the player is highly concentrated. Furthermore, the weather information collection unit provides advice to the player to adjust the timing of the shot based on the emotion estimation data. For example, the weather information collection unit provides advice to hit a shot when the player is less tense. In this way, by suggesting the timing of a shot based on the player's level of tension and concentration, more effective shots can be achieved.

[0050] The advice providing unit can analyze the player's real-time performance data and instantly update the advice. In the advice providing unit, for example, the generation AI analyzes the player's real-time performance data and instantly updates the advice. For example, the advice for the next shot is adjusted based on the results of the shot. Furthermore, the generation AI instantly provides advice to the player based on the real-time performance data. For example, the swing trajectory and force level are analyzed in real time and advice for the next shot is provided. Furthermore, the generation AI analyzes the player's real-time performance data and instantly updates the advice. For example, the advice for the next putt is adjusted based on the results of the putt. In this way, advice can be instantly updated based on the player's real-time performance data, enabling more effective play.

[0051] The advice providing unit can compare the player's past performance data with the current situation and propose the optimal strategy. For example, the generation AI compares the player's past performance data with the current situation and proposes the optimal strategy. For example, it proposes the optimal club selection for the current hole based on past data. Furthermore, the generation AI proposes the player the optimal strategy for the current situation based on past performance data. For example, it proposes the direction of the current shot taking into account past trends in mis-shots. Furthermore, the generation AI compares the player's past performance data with the current situation and proposes the optimal strategy. For example, it proposes the optimal amount of force for the shot on the current hole based on past successes. In this way, by comparing the player's past performance data with the current situation, it is possible to propose a more effective strategy.

[0052] The advice providing unit uses the emotion estimation function to provide advice according to the player's emotional state, thereby maintaining the player's motivation. The advice providing unit, for example, uses the emotion estimation function to provide advice according to the player's emotional state, thereby maintaining motivation. For example, if the player is highly tense, advice to relax is provided. The advice providing unit also analyzes the player's emotional state in real time and provides advice to maintain motivation. For example, if the player's level of concentration is low, an encouraging message is sent. Furthermore, based on the emotion estimation data, advice according to the player's emotional state is provided to maintain motivation. For example, a positive message according to the emotional state is sent. In this way, by providing advice according to the player's emotional state, motivation can be maintained and more effective play can be achieved.

[0053] The surveying unit can use the golf course topography data to provide a virtual reality practice environment, allowing players to simulate an actual course. The surveying unit, for example, uses the golf course topography data to provide a virtual reality (VR) practice environment. For example, a player can practice while simulating an actual course. In addition, during practice in the VR environment, the generative AI provides advice in real time. For example, it suggests shot heights and angles while simulating them in VR. Furthermore, based on the golf course topography data, practice in the VR environment allows players to experience an actual course. For example, it simulates a specific hole. This allows players to practice while simulating an actual course, which is useful for actual play.

[0054] The analysis unit compares the player's score data with other players, and the generation AI can provide a benchmark. The analysis unit, for example, compares the player's score data with other players, and the generation AI can provide a benchmark. For example, by comparing with players of the same level, the player's driving distance and score trends are analyzed. The generation AI also provides a benchmark for the player based on the score data of other players. For example, by comparing with data from top players, areas for improvement are identified. Furthermore, the player's score data is compared with other players, and the generation AI can provide a performance benchmark. For example, by comparing scores on the same course, the player's strengths and weaknesses are analyzed. In this way, a performance benchmark can be provided by comparing the player's score data with other players.

[0055] The weather information collection unit can use the emotion estimation function to analyze the relationship between the player's emotional state and weather conditions and provide emotion management advice. The weather information collection unit, for example, uses the emotion estimation function to analyze the relationship between the player's emotional state and weather conditions. For example, it analyzes the tendency for tension to increase when wind speed is strong. It also analyzes the relationship between the player's emotional state and weather conditions and provides emotion management advice. For example, it suggests ways to relax when the temperature is high. Furthermore, it analyzes the relationship between the player's emotional state and weather conditions based on the emotion estimation data and provides emotion management advice. For example, it suggests ways to concentrate when humidity is high. In this way, it is possible to provide emotion management advice by analyzing the relationship between the player's emotional state and weather conditions.

[0056] The advice providing unit can create an individual training plan using the player's performance data. In the advice providing unit, for example, the generation AI creates an individual training plan based on the player's performance data. For example, it suggests the frequency and content of practice for a specific club. The generation AI also analyzes the performance data and creates an optimal training plan for the player. For example, it suggests a practice method for a club that results in a high frequency of mis-shots. Furthermore, the generation AI creates an individual training plan using the player's performance data. For example, it suggests a practice method to improve performance on a specific hole. In this way, more effective training can be achieved by creating an individual training plan based on the player's performance data.

[0057] The advice providing unit can use the emotion estimation function to provide a message to improve motivation according to the player's emotional state. The advice providing unit, for example, uses the emotion estimation function to provide a message to improve motivation according to the player's emotional state. For example, if the player is highly tense, it sends a message to relax. The advice providing unit also analyzes the player's emotional state in real time and provides a message to improve motivation. For example, if the player's level of concentration is low, it sends an encouraging message. Furthermore, based on the emotion estimation data, it provides a message to improve motivation to the player. For example, it sends a positive message according to the emotional state. In this way, by providing a message to improve motivation according to the player's emotional state, it is possible to maintain the player's motivation and play more effectively.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The caddie service system may further include a health management unit. The health management unit collects biometric data such as the player's heart rate and body temperature to monitor the player's health condition. For example, if the player's heart rate is high, the unit suggests taking a break. Also, if the player's body temperature is high, the unit may advise the player to drink more fluids. Furthermore, the health management unit may suggest adjusting the pace of play based on the player's health condition. For example, if fatigue is accumulating, the unit may advise the player to slow down. In this way, the player's health condition can be monitored and appropriate advice provided, enabling safer and more effective play.

[0060] The caddie service system may further include a social media integration unit. The social media integration unit provides a function for sharing a player's score and playing status on social media in real time. For example, it may automatically post shot results and scores to Twitter or Facebook. It may also display supportive messages from the player's friends and followers in real time. The social media integration unit may also provide a score comparison function with other players and a ranking display function. For example, it may compare scores with other players playing on the same course and display the ranking. This allows players to interact with other players through social media and increase motivation.

[0061] The caddie service system may further include an ecology management unit. The ecology management unit collects environmental data on the golf course and suggests environmentally friendly play to the player. For example, it may provide advice to encourage grass protection in specific areas. It may also provide suggestions for reducing environmental impact when selecting clubs and balls to use. Furthermore, the ecology management unit may guide the player on how to separate and recycle garbage. For example, it may suggest the appropriate method for disposing of used balls and tees. This allows the player to play in an environmentally friendly manner and contribute to the sustainable use of the golf course.

[0062] The caddie service system can further include a voice assistant unit. The voice assistant unit provides necessary information and advice in response to the player's voice commands. For example, if the player asks, "What is the distance of the next hole?", the distance will be announced by voice. Also, if the player asks, "What is the wind direction?", the current wind direction and wind speed can be announced by voice. Furthermore, the voice assistant unit can also suggest shot advice and club selection based on the player's voice commands. For example, if the player asks, "Which club should I use?", the optimal club will be suggested by voice. This allows the player to easily obtain the necessary information and play more effectively.

[0063] The caddie service system may further include a feedback collection unit. The feedback collection unit collects feedback from players and uses it to improve the system. For example, players may evaluate the content and accuracy of advice. Players may also provide opinions about the system's usability and functionality. Furthermore, the feedback collection unit may make suggestions to the generation AI to improve the accuracy and content of advice based on player feedback. For example, if advice on a particular hole is inappropriate, the feedback collection unit may analyze the cause and suggest improvements for the next play. In this way, player feedback can be utilized to improve the system's accuracy and usability.

[0064] The caddie service system can further use the emotion estimation function to adjust the pace of play based on the player's emotional state. For example, if the player is nervous, it can suggest taking a break to relax. Alternatively, if the player is concentrating, it can advise the player to continue playing. Furthermore, it can use the emotion estimation function to suggest pacing according to the player's emotional state. For example, if the player is feeling fatigued, it can advise the player to slow down. This allows the player to play more effectively by adjusting the pace of play based on their emotional state.

[0065] The caddie service system can further use the emotion estimation function to adjust the difficulty of the shot based on the player's emotional state. For example, if the player is nervous, a less difficult shot can be suggested. On the other hand, if the player is relaxed, a more difficult shot can be suggested. Furthermore, the emotion estimation function is used to suggest adjusting the difficulty of the shot according to the player's emotional state. For example, if the player is highly focused, a more challenging shot can be suggested. In this way, adjusting the difficulty of the shot based on the player's emotional state enables more effective play.

[0066] The caddie service system can further use the emotion estimation function to suggest a club to select based on the player's emotional state. For example, if the player is nervous, it can suggest a club that is easy to handle. Also, if the player is relaxed, it can suggest a club that will produce a long drive distance. Furthermore, it can use the emotion estimation function to suggest a club to select based on the player's emotional state. For example, if the player is highly focused, it can suggest a club that will enable a highly accurate shot. In this way, suggesting a club to select based on the player's emotional state enables more effective play.

[0067] The caddie service system can further use the emotion estimation function to suggest a course selection based on the player's emotional state. For example, if the player is nervous, a less difficult course can be suggested. On the other hand, if the player is relaxed, a more difficult course can be suggested. Furthermore, the emotion estimation function can be used to suggest a course selection according to the player's emotional state. For example, if the player is highly focused, a more challenging course can be suggested. This allows for more effective play by suggesting a course selection based on the player's emotional state.

[0068] The caddie service system can further use the emotion estimation function to adjust the timing of play based on the player's emotional state. For example, if the player is nervous, it can set aside time to relax. Alternatively, if the player is concentrating, it can advise the player to continue playing. Furthermore, the emotion estimation function can be used to make suggestions to adjust the timing of play according to the player's emotional state. For example, if fatigue accumulates, it can suggest taking a break. This allows the player to adjust the timing of play based on their emotional state, enabling more effective play.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The Surveying Department works with Michibiki's CLAS to carry out precise measurements down to the centimeter. For example, it can accurately determine the distance from the tee to the green and the distance to obstacles. Step 2: The analysis unit analyzes the player's past score data to determine the player's driving distance, driving habits, and the probability of mishits. For example, it analyzes the player's average driving distance when hitting with a driver, or the frequency of mishits with a particular club. Step 3: The weather information collection unit collects weather information for the current location and analyzes wind direction, temperature, etc. For example, it collects data such as wind speed, wind direction, temperature, and humidity, and provides advice on the shot based on this information. Step 4: The advice provider uses the data obtained by the surveying, analysis, and weather information collection units to provide the player with optimal advice, such as the best club to choose for a particular hole, shot direction, and power level, all of which are displayed on the smartphone screen.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 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 system that uses a generative AI to provide excellent caddy services to golf players, The Surveying Department works in cooperation with Michibiki's CLAS to carry out precise measurements down to the centimeter. An analysis section analyzes the player's past score data to understand the distance, habits, and probability of miss shots. a weather information collection unit that collects weather information for the current location and analyzes wind direction and temperature; an advice providing unit that provides optimal advice to the player based on the data obtained by the surveying unit, the analysis unit, and the weather information collecting unit. A system characterized by:

2. The surveying unit The topographical data of the golf course is converted into a 3D model, and the generative AI suggests the optimal shot height and angle for the player in real time.

2. The system of claim 1.

3. The analysis unit The system according to claim 1, wherein the system collects swing data of the player and the generating AI provides real-time feedback on areas for improving the swing.

4. The weather information collection unit 10. The system of claim 1, wherein weather data is combined with the player's performance data to suggest an optimal strategy under specific weather conditions.

5. The advice providing unit 2. The system of claim 1, wherein the system analyzes the player's real-time performance data and updates advice in real time.

6. The weather information collection unit Measure the player's level of tension and concentration and suggest shot timing based on that 2. The system of claim 1.

7. The advice providing unit 2. The system according to claim 1, wherein advice is provided according to the emotional state of the player to maintain the player's motivation.

8. The weather information collection unit The system of claim 1, further comprising: analyzing the relationship between the player's emotional state and weather conditions; and providing emotional management advice.

Images