system

Abstract

The system according to this embodiment aims to advise golfers on the optimal club selection, direction, and strength. [Solution] The system according to the embodiment comprises a collection unit, a reception unit, an analysis unit, and a provision unit. The collection unit collects GPS data and terrain data. The reception unit receives the user's ball-hitting information. The analysis unit analyzes the data collected by the collection unit and the reception unit and calculates the optimal club selection, direction, and strength. The provision unit provides the user with advice based on the results calculated by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a golfer to determine the optimal club selection, hitting direction, and strength, and there is room for improvement.

[0005] The system according to the embodiment aims to advise a golfer on the optimal club selection, direction, and strength.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a reception unit, an analysis unit, and a provision unit. The data collection unit collects GPS data and terrain data. The reception unit receives information on the user's ball striking. The analysis unit analyzes the data collected by the data collection unit and the reception unit and calculates the optimal club selection, direction, and strength. The provision unit provides the user with advice based on the results calculated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can advise golfers on the optimal club selection, direction, and strength. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Golf Master Agent System according to an embodiment of the present invention is a system in which the AI ​​calculates the optimal club selection, direction, and strength for each shot by combining GPS and terrain data, taking into account wind speed, wind direction, and pin position, and provides advice to the user. This system acquires GPS and terrain data and collects the current wind speed, wind direction, and pin position in real time. Next, the AI ​​analyzes this data and calculates the optimal club selection, direction, and strength for each shot. The calculation results are provided to the user as advice. This system performs real-time environmental analysis and strategic suggestions and learns individual playing styles through deep learning. It also provides interactive feedback and ongoing game support. This helps improve the skills of all golfers, from amateurs to professionals, and is particularly useful for golfers seeking to improve their strategic gameplay. For example, when a user hits a shot on a particular hole, the AI ​​considers wind speed, wind direction, terrain topography, pin position, etc., to select the optimal club and calculate the direction and strength of the shot. By following this advice, the user can improve their score and enjoy the game more. This system can be used in golf courses, golf schools, and sporting goods stores, and is attracting attention as an innovative solution in today's golf market where the use of technology is increasing. This allows the Golf Master Agent system to provide users with real-time advice on optimal club selection, direction, and strength.

[0029] The golf master agent system according to this embodiment comprises a collection unit, a reception unit, an analysis unit, and a provision unit. The collection unit collects GPS data and terrain data. For example, the collection unit acquires location information using a GPS sensor and terrain data from a map database. The collection unit can also measure wind speed and wind direction using an anemometer and wind vane. For example, the collection unit identifies the user's current location using a GPS sensor and acquires terrain data corresponding to that location from a map database. The collection unit measures the current wind speed using an anemometer and identifies the wind direction using a wind vane. The reception unit receives information about the user's shots. For example, the reception unit provides an interface for receiving information about shots entered by the user. The reception unit can also measure the speed and angle of the shots using sensors. For example, the reception unit receives information about the club type and direction of the shots entered by the user. The reception unit measures the speed and angle of the shots using sensors and collects the data. The analysis unit analyzes the data collected by the collection unit and the reception unit to calculate the optimal club selection, direction, and strength. The analysis unit uses, for example, an AI algorithm to analyze the data and calculate the optimal club selection, direction, and strength. The analysis unit performs calculations considering wind speed, wind direction, and pin position. For example, the analysis unit uses an AI algorithm to analyze the collected wind speed and wind direction data and calculate the optimal club selection. The analysis unit calculates the direction and strength of the shot considering the pin position. The provision unit provides the results calculated by the analysis unit to the user as advice. The provision unit displays the advice, for example, through a user interface. The provision unit can also provide advice using a voice assistant. For example, the provision unit displays advice on the optimal club selection, direction, and strength through a user interface. The provision unit provides advice to the user by voice using a voice assistant. As a result, the golf master agent system according to the embodiment can provide the user with real-time advice on the optimal club selection, direction, and strength.

[0030] The data collection unit collects GPS data and terrain data. For example, the data collection unit obtains location information using a GPS sensor and terrain data from a map database. Specifically, the GPS sensor obtains highly accurate location information in real time to determine the user's current location. The terrain data obtained from the map database includes detailed layouts of golf courses and terrain information for each hole. This allows the data collection unit to accurately understand the user's location and the surrounding terrain. Furthermore, the data collection unit can also measure wind speed and wind direction using an anemometer and wind vane. The anemometer measures the current wind speed in real time, and the wind vane determines the wind direction. This data is extremely important because it greatly affects the distance and direction of the ball flight. For example, the data collection unit uses a GPS sensor to determine the user's current location and obtains terrain data corresponding to that location from a map database. The data collection unit measures the current wind speed using an anemometer and determines the wind direction using a wind vane. This allows the data collection unit to collect diverse data such as the user's location, surrounding terrain, wind speed, and wind direction in real time and provide it to the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The reception unit receives information about the user's shots. For example, the reception unit provides an interface for receiving information entered by the user. Specifically, it provides an interface for the user to input information such as the type of club used, the direction and strength of the shot. The reception unit can also measure the speed and angle of the shot using sensors. For example, it is equipped with sensors that measure the speed and angle of the club at the moment of impact with high precision, allowing for accurate determination of the initial speed and launch angle of the shot. The reception unit receives the club type and direction of the shot entered by the user. For example, if the user selects a driver and sets the direction of the shot to the right, that information is entered into the reception unit. Furthermore, the reception unit uses sensors to measure the speed and angle of the shot and collects the data. This allows the reception unit to collect detailed data about the user's shots and provide it to the analysis unit. The reception unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis unit and the data provision unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses tailored to specific situations and conditions become possible. This allows the reception department to collect data efficiently and effectively, improving the overall system performance.

[0032] The analysis unit analyzes the data collected by the collection and reception units to calculate the optimal club selection, direction, and strength. For example, the analysis unit uses AI algorithms to analyze the data and calculate the optimal club selection, direction, and strength. Specifically, the AI ​​algorithm analyzes the collected wind speed and wind direction data to calculate the optimal club selection. For example, if there is a strong headwind, the AI ​​takes this into account and advises selecting a club to hit the ball with a lower trajectory. It also calculates the direction and strength of the shot considering the pin position. For example, if the pin is on the left side of the green, the AI ​​takes this into account and calculates to set the direction of the shot to the right to minimize the effect of the wind. Based on these calculation results, the analysis unit provides the user with advice on the optimal club selection, direction, and strength. Furthermore, the analysis unit can also utilize past data and statistical information to improve long-term performance. For example, based on past shot data, it can analyze the user's swing tendencies and weaknesses and propose a training plan based on that. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to improve long-term performance and detect anomalies, thereby enhancing the reliability and safety of the entire system.

[0033] The service provider provides the user with advice based on the results calculated by the analysis unit. The service provider displays the advice, for example, through a user interface. Specifically, advice on optimal club selection, direction, and strength is displayed on the screen of the user's device. The service provider can also provide advice using a voice assistant. For example, if the user is wearing earphones, the voice assistant provides voice advice on optimal club selection, direction, and strength. This allows the user to receive not only visual but also auditory information, enabling them to understand the advice more intuitively. The service provider displays advice on optimal club selection, direction, and strength through a user interface. For example, if the user is using a smartphone or tablet, the advice is displayed on the screen, allowing the user to refer to it when hitting the ball. The service provider also provides advice to the user via voice using a voice assistant. For example, if the user is wearing earphones, the voice assistant provides voice advice on optimal club selection, direction, and strength. This allows the user to receive not only visual but also auditory information, enabling them to understand the advice more intuitively. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by inputting the results of a user's shots following the advice into the support unit as feedback, the unit can analyze the data and incorporate it into future advice. This allows the support unit to provide users with real-time advice on optimal club selection, direction, and power.

[0034] The analysis unit can calculate the optimal club selection, direction, and strength by considering wind speed, wind direction, and pin position. For example, the analysis unit calculates the optimal club selection by considering wind speed and wind direction based on data obtained from an anemometer and wind vane. The analysis unit can also calculate the direction and strength of the shot by considering the pin position. For example, the analysis unit changes the club selection if the wind speed is strong based on wind speed data obtained from an anemometer. The analysis unit adjusts the direction of the shot according to the wind direction based on wind direction data obtained from a wind vane. The analysis unit can also calculate the strength of the shot by considering the pin position. In this way, by calculating the optimal club selection, direction, and strength by considering wind speed, wind direction, and pin position, appropriate advice can be provided to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data obtained from an anemometer and wind vane into a generating AI and have the generating AI perform the calculation of the optimal club selection, direction, and strength.

[0035] The service provider can provide advice to the user in real time. For example, the service provider can display advice in real time through a user interface. The service provider can also provide advice in real time using a voice assistant. For example, the service provider can display advice on the optimal club selection, direction, and strength in real time through a user interface. The service provider can provide advice to the user in real time via voice using a voice assistant. This allows the user to take appropriate action immediately by providing advice in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input calculation results from the analysis unit into a generating AI and have the generating AI execute advice in real time.

[0036] The learning unit can learn individual playing styles through deep learning. The learning unit learns the user's playing style using, for example, a neural network. The learning unit learns based on the user's batting data and playing history. For example, the learning unit analyzes the user's batting data using a neural network and learns the playing style. The learning unit can also extract characteristics of the playing style based on the user's playing history. As a result, by learning individual playing styles through deep learning, it is possible to provide more personalized advice. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the user's batting data into a generating AI and have the generating AI perform the learning of the playing style.

[0037] The feedback unit can provide interactive feedback. For example, the feedback unit can provide feedback in the form of a dialogue with the user. The feedback unit can also provide feedback in real time. For example, the feedback unit can provide feedback on the results of the shots and areas for improvement in the form of a dialogue with the user. The feedback unit can also provide feedback in real time that is tailored to the user's playing situation. By providing interactive feedback, the user can receive feedback in real time. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's playing situation into a generating AI and have the generating AI execute interactive feedback.

[0038] The collection unit can analyze past collected data and select the optimal collection method. For example, the collection unit can increase the collection frequency during specific time periods based on past collected data. The collection unit can also analyze past collected data and optimize the collection method under specific weather conditions. The collection unit can refer to past collected data and adjust the collection method under specific topographic conditions. For example, the collection unit can increase the collection frequency during specific time periods based on past collected data. The collection unit can analyze past collected data and optimize the collection method under specific weather conditions. The collection unit can refer to past collected data and adjust the collection method under specific topographic conditions. This allows the collection unit to select the optimal collection method by analyzing past collected data. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter the data based on the user's current play status and skill level during collection. For example, the data collection unit can adjust the level of detail of the data it collects according to the user's skill level. The data collection unit can also collect only the necessary data based on the user's play status. The data collection unit can also select the data to collect by referring to the user's past play history. For example, the data collection unit can adjust the level of detail of the data it collects according to the user's skill level. The data collection unit collects only the necessary data based on the user's play status. The data collection unit selects the data to collect by referring to the user's past play history. This allows the data collection unit to collect only the necessary data by filtering the data according to the user's play status and skill level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's play status and skill level into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific hole, the data collection unit will prioritize the collection of data related to that hole. If the user is in a specific region, the data collection unit can also prioritize the collection of wind speed and wind direction data for that region. If the user is under specific terrain conditions, the data collection unit can also prioritize the collection of data related to those terrain conditions. For example, if the user is in a specific hole, the data collection unit will prioritize the collection of data related to that hole. If the user is in a specific region, the data collection unit will prioritize the collection of wind speed and wind direction data for that region. If the user is under specific terrain conditions, the data collection unit will prioritize the collection of data related to those terrain conditions. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect relevant data based on location information shared by the user on social media. The data collection unit can also collect data related to golf courses mentioned by the user on social media. The data collection unit can also prioritize collecting data on golf courses of interest from the user's social media activity. For example, the data collection unit can collect relevant data based on location information shared by the user on social media. The data collection unit can collect data related to golf courses mentioned by the user on social media. The data collection unit prioritizes collecting data on golf courses of interest from the user's social media activity. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The reception unit can analyze past ball-hit data and select the optimal reception method. For example, the reception unit can adjust the reception method based on past ball-hit data, taking into account the frequency of use of a particular club. The reception unit can also analyze past ball-hit data and select a reception method appropriate to a specific skill level. The reception unit can also refer to past ball-hit data and select a reception method appropriate to a specific playing style. For example, the reception unit can adjust the reception method based on past ball-hit data, taking into account the frequency of use of a particular club. The reception unit analyzes past ball-hit data and selects a reception method appropriate to a specific skill level. The reception unit refers to past ball-hit data and selects a reception method appropriate to a specific playing style. In this way, the optimal reception method can be selected by analyzing past ball-hit data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past ball-hit data into a generating AI and have the generating AI select the optimal reception method.

[0043] The reception unit can filter the data at the time of reception based on the user's current play status and skill level. For example, the reception unit can adjust the level of detail of the batted ball information it accepts according to the user's skill level. The reception unit can also accept only the necessary batted ball information based on the user's play status. The reception unit can also select the batted ball information to accept by referring to the user's past play history. For example, the reception unit can adjust the level of detail of the batted ball information it accepts according to the user's skill level. The reception unit can accept only the necessary batted ball information based on the user's play status. The reception unit can select the batted ball information to accept by referring to the user's past play history. This allows the reception unit to accept only the necessary information by filtering the batted ball information according to the user's play status and skill level. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's play status and skill level into a generating AI and have the generating AI perform the filtering.

[0044] The reception unit can prioritize receiving highly relevant shot information by considering the user's geographical location information at the time of reception. For example, if the user is at a specific hole, the reception unit will prioritize receiving shot information related to that hole. The reception unit can also prioritize receiving shot information related to a specific region if the user is in that region. The reception unit can also prioritize receiving shot information related to a specific terrain if the user is under specific terrain conditions. For example, if the reception unit is at a specific hole, the reception unit will prioritize receiving shot information related to that hole. If the user is in a specific region, the reception unit will prioritize receiving shot information related to that region. If the reception unit is under specific terrain conditions, the reception unit will prioritize receiving shot information related to that terrain. In this way, by considering the user's geographical location information, highly relevant shot information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the reception of highly relevant shot information.

[0045] The reception unit can analyze the user's social media activity and receive relevant golf ball information upon receiving the data. For example, the reception unit can receive relevant golf ball information based on golf ball information shared by the user on social media. The reception unit can also receive golf ball information related to golf courses mentioned by the user on social media. The reception unit can also prioritize receiving golf ball information of interest from the user's social media activity. For example, the reception unit can receive relevant golf ball information based on golf ball information shared by the user on social media. The reception unit can receive golf ball information related to golf courses mentioned by the user on social media. The reception unit prioritizes receiving golf ball information of interest from the user's social media activity. In this way, relevant golf ball information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant golf ball information.

[0046] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can adjust the analysis algorithm based on past analysis data, taking into account the frequency of use of a particular club. The analysis unit can also analyze past analysis data and optimize the analysis algorithm according to a specific skill level. The analysis unit can also refer to past analysis data and optimize the analysis algorithm according to a specific playing style. For example, the analysis unit can adjust the analysis algorithm based on past analysis data, taking into account the frequency of use of a particular club. The analysis unit can analyze past analysis data and optimize the analysis algorithm according to a specific skill level. The analysis unit can refer to past analysis data and optimize the analysis algorithm according to a specific playing style. In this way, the analysis algorithm can be optimized by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0047] The analysis unit can customize the analysis method based on the user's play style and skill level during analysis. For example, the analysis unit adjusts the analysis method according to the user's skill level. The analysis unit can also prioritize a specific analysis method based on the user's play style. The analysis unit can also customize the analysis method by referring to the user's past play history. For example, the analysis unit adjusts the analysis method according to the user's skill level. The analysis unit prioritizes a specific analysis method based on the user's play style. The analysis unit customizes the analysis method by referring to the user's past play history. This allows for appropriate analysis by customizing the analysis method according to the user's play style and skill level. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's play style and skill level into a generating AI and have the generating AI perform the customization of the analysis method.

[0048] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific hole, the analysis unit will perform analysis based on data related to that hole. If the user is in a specific region, the analysis unit can also perform analysis based on data for that region. If the user is under specific terrain conditions, the analysis unit can also perform analysis based on data related to those terrain conditions. For example, if the user is in a specific hole, the analysis unit will perform analysis based on data related to that hole. If the user is in a specific region, the analysis unit will perform analysis based on data for that region. If the user is under specific terrain conditions, the analysis unit will perform analysis based on data related to those terrain conditions. By considering the user's geographical location information, it is possible to perform analysis that is highly relevant. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform analysis that is highly relevant.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis based on relevant data. The analysis unit can also improve its analysis method by referring to relevant research results. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. The analysis unit can improve the accuracy of its analysis based on relevant data. The analysis unit can improve its analysis method by referring to relevant research results. In this way, the accuracy of the analysis can be improved by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0050] The service provider can adjust the level of detail of the advice based on the user's play style and skill level when providing it. For example, the service provider adjusts the level of detail of the advice according to the user's skill level. The service provider can also prioritize certain advice based on the user's play style. The service provider can also customize the level of detail of the advice by referring to the user's past play history. For example, the service provider adjusts the level of detail of the advice according to the user's skill level. The service provider prioritizes certain advice based on the user's play style. The service provider customizes the level of detail of the advice by referring to the user's past play history. This allows the service provider to provide appropriate advice by adjusting the level of detail of the advice according to the user's play style and skill level. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's play style and skill level into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0051] The service provider can customize the content of the advice based on the user's current gameplay status at the time of delivery. For example, the service provider adjusts the content of the advice according to the user's current gameplay status. The service provider can also customize the content of the advice based on the user's current skill level. The service provider can also optimize the content of the advice according to the user's current play style. For example, the service provider adjusts the content of the advice according to the user's current gameplay status. The service provider customizes the content of the advice based on the user's current skill level. The service provider optimizes the content of the advice according to the user's current play style. This allows the service provider to provide appropriate advice by customizing the content of the advice according to the user's current gameplay status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current gameplay status into a generating AI and have the generating AI perform the customization of the advice content.

[0052] The service provider can provide optimal advice by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific hole, the service provider can provide advice related to that hole. If the user is in a specific region, the service provider can also provide advice based on data for that region. If the user is under specific terrain conditions, the service provider can also provide advice related to those terrain conditions. For example, if the service provider is in a specific hole, the service provider can provide advice related to that hole. If the user is in a specific region, the service provider can provide advice based on data for that region. If the user is under specific terrain conditions, the service provider can provide advice related to those terrain conditions. This allows the service provider to provide highly relevant advice by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0053] The service provider can analyze the user's social media activity and adjust the content of the advice at the time of delivery. For example, the service provider can provide relevant advice based on information shared by the user on social media. The service provider can also provide advice related to golf courses mentioned by the user on social media. The service provider can also prioritize providing advice of interest based on the user's social media activity. For example, the service provider can provide relevant advice based on information shared by the user on social media. The service provider can provide advice related to golf courses mentioned by the user on social media. The service provider can prioritize providing advice of interest based on the user's social media activity. This allows the service provider to provide relevant advice by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the advice.

[0054] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the learning algorithm based on past learning data, taking into account the frequency of use of a particular club. The learning unit can also analyze past learning data and optimize the learning algorithm according to a specific skill level. The learning unit can also refer to past learning data and optimize the learning algorithm according to a specific play style. For example, the learning unit can adjust the learning algorithm based on past learning data, taking into account the frequency of use of a particular club. The learning unit can analyze past learning data and optimize the learning algorithm according to a specific skill level. The learning unit can refer to past learning data and optimize the learning algorithm according to a specific play style. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0055] The learning unit can weight the learning data based on the user's play style and skill level during learning. For example, the learning unit can adjust the weighting of the learning data according to the user's skill level. The learning unit can also prioritize certain learning data based on the user's play style. The learning unit can also customize the weighting of the learning data by referring to the user's past play history. For example, the learning unit adjusts the weighting of the learning data according to the user's skill level. The learning unit prioritizes certain learning data based on the user's play style. The learning unit customizes the weighting of the learning data by referring to the user's past play history. This allows for appropriate learning by weighting the learning data according to the user's play style and skill level. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the user's play style and skill level into a generating AI and have the generating AI perform the weighting of the learning data.

[0056] The feedback unit can provide optimal feedback by referring to the user's past play history. For example, the feedback unit can provide feedback based on the user's past play history, taking into account the frequency of use of a particular club. The feedback unit can also analyze the user's past play history and provide feedback tailored to a specific skill level. The feedback unit can also refer to the user's past play history and provide feedback tailored to a specific play style. For example, the feedback unit can provide feedback based on the user's past play history, taking into account the frequency of use of a particular club. The feedback unit can analyze the user's past play history and provide feedback tailored to a specific skill level. The feedback unit can refer to the user's past play history and provide feedback tailored to a specific play style. This allows the feedback unit to provide optimal feedback by referring to the user's past play history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past play history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0057] The feedback unit can provide optimal feedback by considering the user's geographical location information. For example, if the user is in a specific hole, the feedback unit will provide feedback related to that hole. If the user is in a specific region, the feedback unit can also provide feedback based on data for that region. If the user is under specific terrain conditions, the feedback unit can also provide feedback related to those terrain conditions. For example, if the user is in a specific hole, the feedback unit will provide feedback related to that hole. If the user is in a specific region, the feedback unit will provide feedback based on data for that region. If the user is under specific terrain conditions, the feedback unit will provide feedback related to those terrain conditions. By considering the user's geographical location information, it is possible to provide highly relevant feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

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

[0059] The data collection unit can analyze past data and select the optimal data collection method. For example, based on past data, it can increase the collection frequency during specific time periods and optimize the data collection method under specific weather conditions. Furthermore, it can adjust the data collection method under specific topographical conditions. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data into a generating AI and have the generating AI select the optimal data collection method.

[0060] The reception unit can analyze past ball-hit data and select the optimal reception method. For example, based on past ball-hit data, it can adjust the reception method considering the frequency of use of a particular club and select a reception method appropriate to a specific skill level. Furthermore, it can also select a reception method appropriate to a specific playing style. In this way, the optimal reception method can be selected by analyzing past ball-hit data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past ball-hit data into a generating AI and have the generating AI select the optimal reception method.

[0061] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, based on past analysis data, it can adjust the analysis algorithm considering the frequency of use of a particular club and optimize the analysis algorithm according to a specific skill level. Furthermore, it can also optimize the analysis algorithm according to a specific playing style. In this way, the analysis algorithm can be optimized by referring to past analysis data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0062] The service provider can adjust the level of detail of the advice based on the user's play style and skill level when providing it. For example, it can adjust the level of detail of the advice according to the user's skill level and prioritize certain advice based on the user's play style. Furthermore, it can customize the level of detail of the advice by referring to the user's past play history. This allows for the provision of appropriate advice by adjusting the level of detail according to the user's play style and skill level. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's play style and skill level into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0063] The feedback unit can provide optimal feedback by referring to the user's past play history. For example, based on the user's past play history, it can provide feedback considering the frequency of use of a particular club, and provide feedback tailored to a specific skill level. Furthermore, it can also provide feedback tailored to a specific play style. In this way, optimal feedback can be provided by referring to the user's past play history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past play history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects GPS data and terrain data. For example, the data collection unit obtains location information using a GPS sensor and terrain data from a map database. The data collection unit can also measure wind speed and wind direction using an anemometer or wind vane. Step 2: The reception unit receives the user's batted ball information. The reception unit provides, for example, an interface for receiving batted ball information entered by the user. The reception unit can also measure the speed and angle of the batted ball using sensors. Step 3: The analysis unit analyzes the data collected by the collection unit and reception unit to calculate the optimal club selection, direction, and strength. The analysis unit analyzes the data using, for example, an AI algorithm, and performs calculations taking into account wind speed, wind direction, and pin position. Step 4: The service provider provides the user with advice based on the results calculated by the analysis unit. The service provider can, for example, display the advice through a user interface or provide it using a voice assistant.

[0066] (Example of form 2) The Golf Master Agent System according to an embodiment of the present invention is a system in which the AI ​​calculates the optimal club selection, direction, and strength for each shot by combining GPS and terrain data, taking into account wind speed, wind direction, and pin position, and provides advice to the user. This system acquires GPS and terrain data and collects the current wind speed, wind direction, and pin position in real time. Next, the AI ​​analyzes this data and calculates the optimal club selection, direction, and strength for each shot. The calculation results are provided to the user as advice. This system performs real-time environmental analysis and strategic suggestions and learns individual playing styles through deep learning. It also provides interactive feedback and ongoing game support. This helps improve the skills of all golfers, from amateurs to professionals, and is particularly useful for golfers seeking to improve their strategic gameplay. For example, when a user hits a shot on a particular hole, the AI ​​considers wind speed, wind direction, terrain topography, pin position, etc., to select the optimal club and calculate the direction and strength of the shot. By following this advice, the user can improve their score and enjoy the game more. This system can be used in golf courses, golf schools, and sporting goods stores, and is attracting attention as an innovative solution in today's golf market where the use of technology is increasing. This allows the Golf Master Agent system to provide users with real-time advice on optimal club selection, direction, and strength.

[0067] The golf master agent system according to this embodiment comprises a collection unit, a reception unit, an analysis unit, and a provision unit. The collection unit collects GPS data and terrain data. For example, the collection unit acquires location information using a GPS sensor and terrain data from a map database. The collection unit can also measure wind speed and wind direction using an anemometer and wind vane. For example, the collection unit identifies the user's current location using a GPS sensor and acquires terrain data corresponding to that location from a map database. The collection unit measures the current wind speed using an anemometer and identifies the wind direction using a wind vane. The reception unit receives information about the user's shots. For example, the reception unit provides an interface for receiving information about shots entered by the user. The reception unit can also measure the speed and angle of the shots using sensors. For example, the reception unit receives information about the club type and direction of the shots entered by the user. The reception unit measures the speed and angle of the shots using sensors and collects the data. The analysis unit analyzes the data collected by the collection unit and the reception unit to calculate the optimal club selection, direction, and strength. The analysis unit uses, for example, an AI algorithm to analyze the data and calculate the optimal club selection, direction, and strength. The analysis unit performs calculations considering wind speed, wind direction, and pin position. For example, the analysis unit uses an AI algorithm to analyze the collected wind speed and wind direction data and calculate the optimal club selection. The analysis unit calculates the direction and strength of the shot considering the pin position. The provision unit provides the results calculated by the analysis unit to the user as advice. The provision unit displays the advice, for example, through a user interface. The provision unit can also provide advice using a voice assistant. For example, the provision unit displays advice on the optimal club selection, direction, and strength through a user interface. The provision unit provides advice to the user by voice using a voice assistant. As a result, the golf master agent system according to the embodiment can provide the user with real-time advice on the optimal club selection, direction, and strength.

[0068] The data collection unit collects GPS data and terrain data. For example, the data collection unit obtains location information using a GPS sensor and terrain data from a map database. Specifically, the GPS sensor obtains highly accurate location information in real time to determine the user's current location. The terrain data obtained from the map database includes detailed layouts of golf courses and terrain information for each hole. This allows the data collection unit to accurately understand the user's location and the surrounding terrain. Furthermore, the data collection unit can also measure wind speed and wind direction using an anemometer and wind vane. The anemometer measures the current wind speed in real time, and the wind vane determines the wind direction. This data is extremely important because it greatly affects the distance and direction of the ball flight. For example, the data collection unit uses a GPS sensor to determine the user's current location and obtains terrain data corresponding to that location from a map database. The data collection unit measures the current wind speed using an anemometer and determines the wind direction using a wind vane. This allows the data collection unit to collect diverse data such as the user's location, surrounding terrain, wind speed, and wind direction in real time and provide it to the analysis unit. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The reception unit receives information about the user's shots. For example, the reception unit provides an interface for receiving information entered by the user. Specifically, it provides an interface for the user to input information such as the type of club used, the direction and strength of the shot. The reception unit can also measure the speed and angle of the shot using sensors. For example, it is equipped with sensors that measure the speed and angle of the club at the moment of impact with high precision, allowing for accurate determination of the initial speed and launch angle of the shot. The reception unit receives the club type and direction of the shot entered by the user. For example, if the user selects a driver and sets the direction of the shot to the right, that information is entered into the reception unit. Furthermore, the reception unit uses sensors to measure the speed and angle of the shot and collects the data. This allows the reception unit to collect detailed data about the user's shots and provide it to the analysis unit. The reception unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis unit and the data provision unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses tailored to specific situations and conditions become possible. This allows the reception department to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis unit analyzes the data collected by the collection and reception units to calculate the optimal club selection, direction, and strength. For example, the analysis unit uses AI algorithms to analyze the data and calculate the optimal club selection, direction, and strength. Specifically, the AI ​​algorithm analyzes the collected wind speed and wind direction data to calculate the optimal club selection. For example, if there is a strong headwind, the AI ​​takes this into account and advises selecting a club to hit the ball with a lower trajectory. It also calculates the direction and strength of the shot considering the pin position. For example, if the pin is on the left side of the green, the AI ​​takes this into account and calculates to set the direction of the shot to the right to minimize the effect of the wind. Based on these calculation results, the analysis unit provides the user with advice on the optimal club selection, direction, and strength. Furthermore, the analysis unit can also utilize past data and statistical information to improve long-term performance. For example, based on past shot data, it can analyze the user's swing tendencies and weaknesses and propose a training plan based on that. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to improve long-term performance and detect anomalies, thereby enhancing the reliability and safety of the entire system.

[0071] The service provider provides the user with advice based on the results calculated by the analysis unit. The service provider displays the advice, for example, through a user interface. Specifically, advice on optimal club selection, direction, and strength is displayed on the screen of the user's device. The service provider can also provide advice using a voice assistant. For example, if the user is wearing earphones, the voice assistant provides voice advice on optimal club selection, direction, and strength. This allows the user to receive not only visual but also auditory information, enabling them to understand the advice more intuitively. The service provider displays advice on optimal club selection, direction, and strength through a user interface. For example, if the user is using a smartphone or tablet, the advice is displayed on the screen, allowing the user to refer to it when hitting the ball. The service provider also provides advice to the user via voice using a voice assistant. For example, if the user is wearing earphones, the voice assistant provides voice advice on optimal club selection, direction, and strength. This allows the user to receive not only visual but also auditory information, enabling them to understand the advice more intuitively. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by inputting the results of a user's shots following the advice into the support unit as feedback, the unit can analyze the data and incorporate it into future advice. This allows the support unit to provide users with real-time advice on optimal club selection, direction, and power.

[0072] The analysis unit can calculate the optimal club selection, direction, and strength by considering wind speed, wind direction, and pin position. For example, the analysis unit calculates the optimal club selection by considering wind speed and wind direction based on data obtained from an anemometer and wind vane. The analysis unit can also calculate the direction and strength of the shot by considering the pin position. For example, the analysis unit changes the club selection if the wind speed is strong based on wind speed data obtained from an anemometer. The analysis unit adjusts the direction of the shot according to the wind direction based on wind direction data obtained from a wind vane. The analysis unit can also calculate the strength of the shot by considering the pin position. In this way, by calculating the optimal club selection, direction, and strength by considering wind speed, wind direction, and pin position, appropriate advice can be provided to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data obtained from an anemometer and wind vane into a generating AI and have the generating AI perform the calculation of the optimal club selection, direction, and strength.

[0073] The service provider can provide advice to the user in real time. For example, the service provider can display advice in real time through a user interface. The service provider can also provide advice in real time using a voice assistant. For example, the service provider can display advice on the optimal club selection, direction, and strength in real time through a user interface. The service provider can provide advice to the user in real time via voice using a voice assistant. This allows the user to take appropriate action immediately by providing advice in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input calculation results from the analysis unit into a generating AI and have the generating AI execute advice in real time.

[0074] The learning unit can learn individual playing styles through deep learning. The learning unit learns the user's playing style using, for example, a neural network. The learning unit learns based on the user's batting data and playing history. For example, the learning unit analyzes the user's batting data using a neural network and learns the playing style. The learning unit can also extract characteristics of the playing style based on the user's playing history. As a result, by learning individual playing styles through deep learning, it is possible to provide more personalized advice. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the user's batting data into a generating AI and have the generating AI perform the learning of the playing style.

[0075] The feedback unit can provide interactive feedback. For example, the feedback unit can provide feedback in the form of a dialogue with the user. The feedback unit can also provide feedback in real time. For example, the feedback unit can provide feedback on the results of the shots and areas for improvement in the form of a dialogue with the user. The feedback unit can also provide feedback in real time that is tailored to the user's playing situation. By providing interactive feedback, the user can receive feedback in real time. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's playing situation into a generating AI and have the generating AI execute interactive feedback.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of GPS and terrain data collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions, the data collection unit can adjust the collection timing. For example, if the user is relaxed, the data collection unit will collect GPS and terrain data regularly. If the user is stressed, the data collection unit will collect GPS and terrain data frequently and update it in real time. If the user is focused, the data collection unit will collect GPS and terrain data only at critical moments. This allows for the collection of more appropriate data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0077] The collection unit can analyze past collected data and select the optimal collection method. For example, the collection unit can increase the collection frequency during specific time periods based on past collected data. The collection unit can also analyze past collected data and optimize the collection method under specific weather conditions. The collection unit can refer to past collected data and adjust the collection method under specific topographic conditions. For example, the collection unit can increase the collection frequency during specific time periods based on past collected data. The collection unit can analyze past collected data and optimize the collection method under specific weather conditions. The collection unit can refer to past collected data and adjust the collection method under specific topographic conditions. This allows the collection unit to select the optimal collection method by analyzing past collected data. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0078] The data collection unit can filter the data based on the user's current play status and skill level during collection. For example, the data collection unit can adjust the level of detail of the data it collects according to the user's skill level. The data collection unit can also collect only the necessary data based on the user's play status. The data collection unit can also select the data to collect by referring to the user's past play history. For example, the data collection unit can adjust the level of detail of the data it collects according to the user's skill level. The data collection unit collects only the necessary data based on the user's play status. The data collection unit selects the data to collect by referring to the user's past play history. This allows the data collection unit to collect only the necessary data by filtering the data according to the user's play status and skill level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's play status and skill level into a generating AI and have the generating AI perform the filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions, the data collection unit can determine the priority of data to collect. For example, if the user is relaxed, the data collection unit will prioritize collecting wind speed and wind direction data. If the user is tense, the data collection unit will prioritize collecting pin location data. If the user is focused, the data collection unit will prioritize collecting terrain data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific hole, the data collection unit will prioritize the collection of data related to that hole. If the user is in a specific region, the data collection unit can also prioritize the collection of wind speed and wind direction data for that region. If the user is under specific terrain conditions, the data collection unit can also prioritize the collection of data related to those terrain conditions. For example, if the user is in a specific hole, the data collection unit will prioritize the collection of data related to that hole. If the user is in a specific region, the data collection unit will prioritize the collection of wind speed and wind direction data for that region. If the user is under specific terrain conditions, the data collection unit will prioritize the collection of data related to those terrain conditions. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect relevant data based on location information shared by the user on social media. The data collection unit can also collect data related to golf courses mentioned by the user on social media. The data collection unit can also prioritize collecting data on golf courses of interest from the user's social media activity. For example, the data collection unit can collect relevant data based on location information shared by the user on social media. The data collection unit can collect data related to golf courses mentioned by the user on social media. The data collection unit prioritizes collecting data on golf courses of interest from the user's social media activity. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The reception unit can estimate the user's emotions and adjust the method of receiving batting information based on the estimated emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the reception unit can adjust the method of receiving batting information. For example, if the user is relaxed, the reception unit will receive detailed batting information. If the user is tense, the reception unit will receive simplified batting information. If the user is focused, the reception unit will receive only specific batting information. By adjusting the reception method according to the user's emotions, appropriate batting information can be received. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI, which can then perform an estimation of the user's emotions.

[0083] The reception unit can analyze past ball-hit data and select the optimal reception method. For example, the reception unit can adjust the reception method based on past ball-hit data, taking into account the frequency of use of a particular club. The reception unit can also analyze past ball-hit data and select a reception method appropriate to a specific skill level. The reception unit can also refer to past ball-hit data and select a reception method appropriate to a specific playing style. For example, the reception unit can adjust the reception method based on past ball-hit data, taking into account the frequency of use of a particular club. The reception unit analyzes past ball-hit data and selects a reception method appropriate to a specific skill level. The reception unit refers to past ball-hit data and selects a reception method appropriate to a specific playing style. In this way, the optimal reception method can be selected by analyzing past ball-hit data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past ball-hit data into a generating AI and have the generating AI select the optimal reception method.

[0084] The reception unit can filter the data at the time of reception based on the user's current play status and skill level. For example, the reception unit can adjust the level of detail of the batted ball information it accepts according to the user's skill level. The reception unit can also accept only the necessary batted ball information based on the user's play status. The reception unit can also select the batted ball information to accept by referring to the user's past play history. For example, the reception unit can adjust the level of detail of the batted ball information it accepts according to the user's skill level. The reception unit can accept only the necessary batted ball information based on the user's play status. The reception unit can select the batted ball information to accept by referring to the user's past play history. This allows the reception unit to accept only the necessary information by filtering the batted ball information according to the user's play status and skill level. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's play status and skill level into a generating AI and have the generating AI perform the filtering.

[0085] The reception unit can estimate the user's emotions and determine the priority of the batting information to receive based on the estimated emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the reception unit can determine the priority of the batting information to receive. For example, if the user is relaxed, the reception unit will prioritize receiving detailed batting information. If the user is tense, the reception unit will prioritize receiving simplified batting information. If the user is focused, the reception unit will prioritize receiving specific batting information. In this way, by prioritizing batting information according to the user's emotions, important information can be received preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI, which can then perform an estimation of the user's emotions.

[0086] The reception unit can prioritize receiving highly relevant shot information by considering the user's geographical location information at the time of reception. For example, if the user is at a specific hole, the reception unit will prioritize receiving shot information related to that hole. The reception unit can also prioritize receiving shot information related to a specific region if the user is in that region. The reception unit can also prioritize receiving shot information related to a specific terrain if the user is under specific terrain conditions. For example, if the reception unit is at a specific hole, the reception unit will prioritize receiving shot information related to that hole. If the user is in a specific region, the reception unit will prioritize receiving shot information related to that region. If the reception unit is under specific terrain conditions, the reception unit will prioritize receiving shot information related to that terrain. In this way, by considering the user's geographical location information, highly relevant shot information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the reception of highly relevant shot information.

[0087] The reception unit can analyze the user's social media activity and receive relevant golf ball information upon receiving the data. For example, the reception unit can receive relevant golf ball information based on golf ball information shared by the user on social media. The reception unit can also receive golf ball information related to golf courses mentioned by the user on social media. The reception unit can also prioritize receiving golf ball information of interest from the user's social media activity. For example, the reception unit can receive relevant golf ball information based on golf ball information shared by the user on social media. The reception unit can receive golf ball information related to golf courses mentioned by the user on social media. The reception unit prioritizes receiving golf ball information of interest from the user's social media activity. In this way, relevant golf ball information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant golf ball information.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the analysis unit can adjust the analysis criteria. For example, if the user is relaxed, the analysis unit performs a detailed analysis. If the user is tense, the analysis unit performs a simplified analysis. If the user is focused, the analysis unit prioritizes certain analysis criteria. This allows for appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can adjust the analysis algorithm based on past analysis data, taking into account the frequency of use of a particular club. The analysis unit can also analyze past analysis data and optimize the analysis algorithm according to a specific skill level. The analysis unit can also refer to past analysis data and optimize the analysis algorithm according to a specific playing style. For example, the analysis unit can adjust the analysis algorithm based on past analysis data, taking into account the frequency of use of a particular club. The analysis unit can analyze past analysis data and optimize the analysis algorithm according to a specific skill level. The analysis unit can refer to past analysis data and optimize the analysis algorithm according to a specific playing style. In this way, the analysis algorithm can be optimized by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0090] The analysis unit can customize the analysis method based on the user's play style and skill level during analysis. For example, the analysis unit adjusts the analysis method according to the user's skill level. The analysis unit can also prioritize a specific analysis method based on the user's play style. The analysis unit can also customize the analysis method by referring to the user's past play history. For example, the analysis unit adjusts the analysis method according to the user's skill level. The analysis unit prioritizes a specific analysis method based on the user's play style. The analysis unit customizes the analysis method by referring to the user's past play history. This allows for appropriate analysis by customizing the analysis method according to the user's play style and skill level. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's play style and skill level into a generating AI and have the generating AI perform the customization of the analysis method.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the analysis unit can adjust the display method of the analysis results. For example, if the user is relaxed, the analysis unit will display detailed analysis results. If the user is tense, the analysis unit will display simplified analysis results. If the user is focused, the analysis unit will prioritize displaying specific analysis results. In this way, appropriate information can be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0092] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific hole, the analysis unit will perform analysis based on data related to that hole. If the user is in a specific region, the analysis unit can also perform analysis based on data for that region. If the user is under specific terrain conditions, the analysis unit can also perform analysis based on data related to those terrain conditions. For example, if the user is in a specific hole, the analysis unit will perform analysis based on data related to that hole. If the user is in a specific region, the analysis unit will perform analysis based on data for that region. If the user is under specific terrain conditions, the analysis unit will perform analysis based on data related to those terrain conditions. By considering the user's geographical location information, it is possible to perform analysis that is highly relevant. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform analysis that is highly relevant.

[0093] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis based on relevant data. The analysis unit can also improve its analysis method by referring to relevant research results. For example, the analysis unit can optimize its analysis algorithm by referring to relevant literature. The analysis unit can improve the accuracy of its analysis based on relevant data. The analysis unit can improve its analysis method by referring to relevant research results. In this way, the accuracy of the analysis can be improved by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0094] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the service provider can adjust the way advice is presented. For example, if the user is relaxed, the service provider can provide detailed advice. If the user is tense, the service provider can provide simplified advice. If the user is focused, the service provider can prioritize providing specific advice. This allows the service provider to provide appropriate advice by adjusting the way advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The service provider can adjust the level of detail of the advice based on the user's play style and skill level when providing it. For example, the service provider adjusts the level of detail of the advice according to the user's skill level. The service provider can also prioritize certain advice based on the user's play style. The service provider can also customize the level of detail of the advice by referring to the user's past play history. For example, the service provider adjusts the level of detail of the advice according to the user's skill level. The service provider prioritizes certain advice based on the user's play style. The service provider customizes the level of detail of the advice by referring to the user's past play history. This allows the service provider to provide appropriate advice by adjusting the level of detail of the advice according to the user's play style and skill level. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's play style and skill level into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0096] The service provider can customize the content of the advice based on the user's current gameplay status at the time of delivery. For example, the service provider adjusts the content of the advice according to the user's current gameplay status. The service provider can also customize the content of the advice based on the user's current skill level. The service provider can also optimize the content of the advice according to the user's current play style. For example, the service provider adjusts the content of the advice according to the user's current gameplay status. The service provider customizes the content of the advice based on the user's current skill level. The service provider optimizes the content of the advice according to the user's current play style. This allows the service provider to provide appropriate advice by customizing the content of the advice according to the user's current gameplay status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's current gameplay status into a generating AI and have the generating AI perform the customization of the advice content.

[0097] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the service provider can determine the priority of advice. For example, if the user is relaxed, the service provider will prioritize detailed advice. If the user is tense, the service provider will prioritize simplified advice. If the user is focused, the service provider will prioritize specific advice. This allows for the priority of important advice by determining the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0098] The service provider can provide optimal advice by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific hole, the service provider can provide advice related to that hole. If the user is in a specific region, the service provider can also provide advice based on data for that region. If the user is under specific terrain conditions, the service provider can also provide advice related to those terrain conditions. For example, if the service provider is in a specific hole, the service provider can provide advice related to that hole. If the user is in a specific region, the service provider can provide advice based on data for that region. If the user is under specific terrain conditions, the service provider can provide advice related to those terrain conditions. This allows the service provider to provide highly relevant advice by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0099] The service provider can analyze the user's social media activity and adjust the content of the advice at the time of delivery. For example, the service provider can provide relevant advice based on information shared by the user on social media. The service provider can also provide advice related to golf courses mentioned by the user on social media. The service provider can also prioritize providing advice of interest based on the user's social media activity. For example, the service provider can provide relevant advice based on information shared by the user on social media. The service provider can provide advice related to golf courses mentioned by the user on social media. The service provider can prioritize providing advice of interest based on the user's social media activity. This allows the service provider to provide relevant advice by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the advice.

[0100] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the learning unit can select training data. For example, if the user is relaxed, the learning unit will select detailed training data. If the user is tense, the learning unit will select simplified training data. If the user is focused, the learning unit will prioritize selecting specific training data. This allows for appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0101] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the learning algorithm based on past learning data, taking into account the frequency of use of a particular club. The learning unit can also analyze past learning data and optimize the learning algorithm according to a specific skill level. The learning unit can also refer to past learning data and optimize the learning algorithm according to a specific play style. For example, the learning unit can adjust the learning algorithm based on past learning data, taking into account the frequency of use of a particular club. The learning unit can analyze past learning data and optimize the learning algorithm according to a specific skill level. The learning unit can refer to past learning data and optimize the learning algorithm according to a specific play style. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0102] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions, the learning unit can adjust the frequency of learning. For example, the learning unit will learn more frequently when the user is relaxed. The learning unit will reduce the frequency of learning when the user is tense. The learning unit will learn at specific times when the user is focused. This allows learning to occur at appropriate times by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0103] The learning unit can weight the learning data based on the user's play style and skill level during learning. For example, the learning unit can adjust the weighting of the learning data according to the user's skill level. The learning unit can also prioritize certain learning data based on the user's play style. The learning unit can also customize the weighting of the learning data by referring to the user's past play history. For example, the learning unit adjusts the weighting of the learning data according to the user's skill level. The learning unit prioritizes certain learning data based on the user's play style. The learning unit customizes the weighting of the learning data by referring to the user's past play history. This allows for appropriate learning by weighting the learning data according to the user's play style and skill level. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the user's play style and skill level into a generating AI and have the generating AI perform the weighting of the learning data.

[0104] The feedback unit can estimate the user's emotions and adjust the way it presents feedback based on the estimated emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions, the feedback unit can adjust the way it presents feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. If the user is tense, the feedback unit provides simplified feedback. If the user is focused, the feedback unit prioritizes providing specific feedback. This allows for the provision of appropriate feedback by adjusting the way it presents feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0105] The feedback unit can provide optimal feedback by referring to the user's past play history. For example, the feedback unit can provide feedback based on the user's past play history, taking into account the frequency of use of a particular club. The feedback unit can also analyze the user's past play history and provide feedback tailored to a specific skill level. The feedback unit can also refer to the user's past play history and provide feedback tailored to a specific play style. For example, the feedback unit can provide feedback based on the user's past play history, taking into account the frequency of use of a particular club. The feedback unit can analyze the user's past play history and provide feedback tailored to a specific skill level. The feedback unit can refer to the user's past play history and provide feedback tailored to a specific play style. This allows the feedback unit to provide optimal feedback by referring to the user's past play history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past play history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0106] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions, the feedback unit can determine the priority of feedback. For example, if the user is relaxed, the feedback unit will prioritize providing detailed feedback. If the user is tense, the feedback unit will prioritize providing simplified feedback. If the user is focused, the feedback unit will prioritize providing specific feedback. This allows important feedback to be prioritized by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0107] The feedback unit can provide optimal feedback by considering the user's geographical location information. For example, if the user is in a specific hole, the feedback unit will provide feedback related to that hole. If the user is in a specific region, the feedback unit can also provide feedback based on data for that region. If the user is under specific terrain conditions, the feedback unit can also provide feedback related to those terrain conditions. For example, if the user is in a specific hole, the feedback unit will provide feedback related to that hole. If the user is in a specific region, the feedback unit will provide feedback based on data for that region. If the user is under specific terrain conditions, the feedback unit will provide feedback related to those terrain conditions. By considering the user's geographical location information, it is possible to provide highly relevant feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed, and if the user is tense, a simplified analysis can be performed. Furthermore, if the user is focused, certain analysis criteria can be prioritized. This allows for appropriate analysis by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0110] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is relaxed, detailed advice can be provided; if the user is tense, simplified advice can be provided. Furthermore, if the user is focused, specific advice can be prioritized. This allows for the provision of appropriate advice by adjusting the way advice is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0111] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, wind speed and wind direction data can be prioritized for collection; if the user is tense, pin location data can be prioritized for collection. Furthermore, if the user is focused, terrain data can be prioritized for collection. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0112] The reception unit can estimate the user's emotions and adjust the method of receiving batting information based on the estimated emotions. For example, if the user is relaxed, it can receive detailed batting information, and if the user is tense, it can receive simplified batting information. Furthermore, if the user is focused, it can receive only specific batting information. In this way, appropriate batting information can be received by adjusting the reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0113] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, detailed training data can be selected; if the user is tense, simplified training data can be selected. Furthermore, if the user is focused, specific training data can be prioritized. This allows for appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0114] The data collection unit can analyze past data and select the optimal data collection method. For example, based on past data, it can increase the collection frequency during specific time periods and optimize the data collection method under specific weather conditions. Furthermore, it can adjust the data collection method under specific topographical conditions. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data into a generating AI and have the generating AI select the optimal data collection method.

[0115] The reception unit can analyze past ball-hit data and select the optimal reception method. For example, based on past ball-hit data, it can adjust the reception method considering the frequency of use of a particular club and select a reception method appropriate to a specific skill level. Furthermore, it can also select a reception method appropriate to a specific playing style. In this way, the optimal reception method can be selected by analyzing past ball-hit data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past ball-hit data into a generating AI and have the generating AI select the optimal reception method.

[0116] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, based on past analysis data, it can adjust the analysis algorithm considering the frequency of use of a particular club and optimize the analysis algorithm according to a specific skill level. Furthermore, it can also optimize the analysis algorithm according to a specific playing style. In this way, the analysis algorithm can be optimized by referring to past analysis data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0117] The service provider can adjust the level of detail of the advice based on the user's play style and skill level when providing it. For example, it can adjust the level of detail of the advice according to the user's skill level and prioritize certain advice based on the user's play style. Furthermore, it can customize the level of detail of the advice by referring to the user's past play history. This allows for the provision of appropriate advice by adjusting the level of detail according to the user's play style and skill level. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's play style and skill level into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0118] The feedback unit can provide optimal feedback by referring to the user's past play history. For example, based on the user's past play history, it can provide feedback considering the frequency of use of a particular club, and provide feedback tailored to a specific skill level. Furthermore, it can also provide feedback tailored to a specific play style. In this way, optimal feedback can be provided by referring to the user's past play history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past play history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects GPS data and terrain data. For example, the data collection unit obtains location information using a GPS sensor and terrain data from a map database. The data collection unit can also measure wind speed and wind direction using an anemometer or wind vane. Step 2: The reception unit receives the user's batted ball information. The reception unit provides, for example, an interface for receiving batted ball information entered by the user. The reception unit can also measure the speed and angle of the batted ball using sensors. Step 3: The analysis unit analyzes the data collected by the collection unit and reception unit to calculate the optimal club selection, direction, and strength. The analysis unit analyzes the data using, for example, an AI algorithm, and performs calculations taking into account wind speed, wind direction, and pin position. Step 4: The service provider provides the user with advice based on the results calculated by the analysis unit. The service provider can, for example, display the advice through a user interface or provide it using a voice assistant.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, provision unit, learning unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires location information and wind speed using the GPS sensor and anemometer of the smart device 14, and acquires terrain data from the database 24 of the data processing unit 12. The reception unit receives the user's ball-hitting information through the interface of the smart device 14. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and calculates the optimal club selection, direction, and strength. The provision unit provides advice using the display 40A and speaker 40B of the smart device 14. The learning unit learns the user's playing style using the identification processing unit 290 of the data processing unit 12. The feedback unit provides interactive feedback through the interface of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the data collection unit, reception unit, analysis unit, provision unit, learning unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires location information and wind speed using the GPS sensor and anemometer of the smart glasses 214, and acquires terrain data from the database 24 of the data processing unit 12. The reception unit receives the user's ball-hitting information through the interface of the smart glasses 214. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and calculates the optimal club selection, direction, and strength. The provision unit provides advice using the display and speaker of the smart glasses 214. The learning unit learns the user's playing style using the identification processing unit 290 of the data processing unit 12. The feedback unit provides interactive feedback through the interface of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the data collection unit, reception unit, analysis unit, provision unit, learning unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit acquires location information and wind speed using the GPS sensor and anemometer of the headset terminal 314, and acquires terrain data from the database 24 of the data processing unit 12. The reception unit receives the user's ball-hitting information through the interface of the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and calculates the optimal club selection, direction, and strength. The provision unit provides advice using the display and speaker of the headset terminal 314. The learning unit learns the user's playing style using the specific processing unit 290 of the data processing unit 12. The feedback unit provides interactive feedback through the interface of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, provision unit, learning unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires location information and wind speed using the robot 414's GPS sensor and anemometer, and obtains terrain data from the database 24 of the data processing unit 12. The reception unit receives the user's ball-hitting information through the robot 414's interface. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 and calculates the optimal club selection, direction, and strength. The provision unit provides advice using the robot 414's display and speaker. The learning unit learns the user's playing style using the specific processing unit 290 of the data processing unit 12. The feedback unit provides interactive feedback through the robot 414's interface. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A data collection unit that collects GPS data and terrain data, A reception desk that receives user batting information, An analysis unit analyzes the data collected by the collection unit and the reception unit and calculates the optimal club selection, direction, and strength. The system includes a provisioning unit that provides the results calculated by the analysis unit to the user as advice. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The optimal club selection, direction, and power are calculated considering wind speed, wind direction, and pin position. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Providing users with advice in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) It also features a learning unit that uses deep learning to learn individual play styles. The system described in Appendix 1, characterized by the features described herein. (Note 5) It also includes a feedback section that provides interactive feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of GPS and terrain data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current gameplay status and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving batted ball information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Analyze past batted ball data to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is During registration, filtering is performed based on the user's current gameplay status and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of incoming batting information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is During registration, the system prioritizes receiving highly relevant batted ball information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is At registration, the system analyzes the user's social media activity and accepts relevant batting information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's play style and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we refer to relevant literature and data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the level of detail in the advice will be adjusted based on the user's play style and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the advice, the content will be customized based on the user's current gameplay status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, we will take the user's geographical location into consideration to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust the content of the advice accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 30) The learning department is, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The learning department is, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The learning department is, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The learning department is, During training, the training data is weighted based on the user's play style and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 34) The feedback section is, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The feedback section is, When providing feedback, we refer to the user's past play history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 36) The feedback section is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The feedback section is, When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects GPS data and terrain data, A reception desk that receives user batting information, An analysis unit analyzes the data collected by the collection unit and the reception unit and calculates the optimal club selection, direction, and strength. The system includes a provisioning unit that provides the results calculated by the analysis unit to the user as advice. A system characterized by the following features.

2. The aforementioned analysis unit, The optimal club selection, direction, and power are calculated considering wind speed, wind direction, and pin position. The system according to feature 1.

3. The aforementioned supply unit is, Providing users with advice in real time. The system according to feature 1.

4. It also features a learning unit that uses deep learning to learn individual play styles. The system according to feature 1.

5. It also includes a feedback section that provides interactive feedback. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of GPS and terrain data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current gameplay status and skill level. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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