System

Abstract

An object of a system according to an embodiment is to propose an optimal strategy individually to a golf player and provide an entertainment element.SOLUTION: A system includes an analysis unit, a proposal unit, a collection unit, an adjustment unit, and a provision unit. The analysis part analyzes the skill level of the player and the past play data. The proposal unit proposes a strategy based on the analysis result obtained by the analysis unit. The collection unit collects information on wind speed, wind direction, and topography in real time. The adjustment unit adjusts the strategy based on the information collected by the collection unit. The providing unit provides an entertainment element to a player.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately proposed optimal strategies to individual golf players and provided entertainment elements, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal strategy to each golf player individually and provide an entertainment element. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a suggestion unit, a collection unit, an adjustment unit, and a provision unit. The analysis unit analyzes the player's skill level and past play data. The suggestion unit proposes a strategy based on the analysis results obtained by the analysis unit. The collection unit collects information on wind speed, wind direction, and terrain in real time. The adjustment unit adjusts the strategy based on the information collected by the collection unit. The provision unit provides entertainment elements to the player. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal strategy to each golf player individually and provide an entertainment element. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides golf caddie services using AI. This system analyzes a player's skill level and past play data, proposes an optimal strategy, collects information on wind speed, wind direction, and terrain in real time, adjusts the strategy based on that information, and provides a fun element to the player. This system can enhance the golf experience by analyzing a player's skill level and past play data, proposing an optimal strategy, collecting information in real time, adjusting the strategy, and providing a fun element. For example, the system performs a detailed analysis of a player's skill level and past play data and has the AI ​​analyze it. During this analysis, the system collects the player's swing data and shot results to identify areas where improvement is needed. Next, based on the analysis results, the AI ​​proposes an optimal strategy to the player. Specifically, the AI ​​provides advice on which club to use and which direction to hit the ball. Furthermore, the AI ​​collects information on wind speed, wind direction, and terrain in real time and adjusts the strategy based on that information. Finally, the system also provides fun elements such as golf trivia and jokes based on the player's preferences. This allows the player to play in a relaxed atmosphere and enhances the golf experience.

[0029] A golf caddie system according to an embodiment includes an analysis unit, a suggestion unit, a collection unit, an adjustment unit, and a provision unit. The analysis unit analyzes a player's skill level and past play data. Examples of a player's skill level include, but are not limited to, scores, success rates, and years of experience. The analysis unit, for example, collects a player's swing data and shot results and identifies areas where improvement is needed. The analysis unit can also analyze data using AI to evaluate a player's skill level. The suggestion unit proposes a strategy based on the analysis results obtained by the analysis unit. The suggestion unit provides advice, for example, on which club to use and which direction to hit the ball. The suggestion unit can also propose an optimal shot taking into account wind speed, wind direction, and terrain information. The collection unit collects information on wind speed, wind direction, and terrain in real time. The collection unit collects information using, for example, a sensor that measures wind speed and wind direction. The collection unit can also use GPS data to collect terrain information. The adjustment unit adjusts the strategy based on the collected information. For example, when the wind is strong, the adjustment unit suggests a shot that minimizes the effect of the wind. The adjustment unit can also suggest an optimal route taking into account the undulations of the terrain and the location of obstacles. The provision unit provides fun elements to the player. The provision unit provides entertainment information such as golf trivia and jokes. The provision unit can also provide golf history and anecdotes about famous players according to the player's preferences. As a result, the golf caddie system according to the embodiment can analyze the player's skill level and past play data, suggest an optimal strategy, collect information in real time, adjust the strategy, and provide fun elements, thereby improving the golf experience.

[0030] The analysis unit can analyze a player's swing data and shot results. Examples of swing data include, but are not limited to, swing speed, angle, and trajectory. For example, the analysis unit can measure a player's swing speed and identify areas for improvement in the swing. The analysis unit can also analyze the swing angle and suggest an optimal swing angle. The analysis unit can also analyze the swing trajectory and provide advice for improving swing consistency. Examples of shot results include, but are not limited to, shot distance, direction, and accuracy. For example, the analysis unit can measure the shot distance and provide advice for improving the distance. The analysis unit can also analyze the shot direction and provide advice for hitting accurate shots. The analysis unit can also analyze the shot accuracy and provide advice for improving the success rate of the shot. Thus, by analyzing a player's swing data and shot results, more specific areas for improvement can be identified.

[0031] The suggestion unit can suggest an appropriate shot by taking into consideration information on wind speed, wind direction, and terrain. An appropriate shot includes, but is not limited to, for example, club selection, shot strength, and direction. The suggestion unit can select an optimal club by taking into consideration information on wind speed and wind direction, for example. The suggestion unit can also adjust the shot strength by taking into consideration information on terrain. The suggestion unit can also suggest an optimal shot direction by comprehensively taking into consideration information on wind speed, wind direction, and terrain. This makes it possible to suggest more accurate shots by taking into consideration information on wind speed, wind direction, and terrain.

[0032] The collection unit can collect information on wind speed, wind direction, and topography in real time. Examples of real-time collection include, but are not limited to, the frequency of data updates and the sensors used. For example, the collection unit collects information in real time using sensors that measure wind speed and wind direction. The collection unit can also use GPS data to collect topography information. Furthermore, the collection unit can instantly analyze the data collected in real time and reflect it in strategic adjustments. This allows for real-time information collection to always provide strategic proposals based on the latest information.

[0033] The adjustment unit can adjust the strategy based on the collected information. Examples of methods for adjusting the strategy include, but are not limited to, data analysis methods and adjustment algorithms. For example, when the wind is strong, the adjustment unit can suggest a shot that minimizes the effect of the wind. The adjustment unit can also suggest an optimal route taking into account the undulations of the terrain and the location of obstacles. Furthermore, the adjustment unit can adjust the strategy in real time based on the collected information. As a result, a more appropriate strategy can be provided by adjusting the strategy based on the collected information.

[0034] The providing unit may provide golf trivia and entertainment information according to the player's preferences. Examples of golf trivia include, but are not limited to, historical facts and technical tips. The providing unit may provide, for example, the history of golf and anecdotes about famous players. The providing unit may also provide technical tips and trivia according to the player's preferences. Examples of entertainment information include, but are not limited to, jokes, quizzes, videos, and the like. For example, the providing unit may provide jokes and quizzes to help the player relax. The providing unit may also provide golf-related videos to entertain the player. This may improve the golf experience by providing fun elements according to the player's preferences.

[0035] The analysis unit can analyze a player's past play data and optimize an analysis algorithm for a specific play style. Examples of play styles include, but are not limited to, offensive, defensive, and balanced styles. For example, if a player has an aggressive play style, the analysis unit can optimize an analysis algorithm for risky shots. Furthermore, if a player has a conservative play style, the analysis unit can optimize an analysis algorithm for safe shots. Furthermore, if a player has a balanced play style, the analysis unit can optimize an analysis algorithm that strikes a balance between risk and safety. Methods for optimizing the analysis algorithm include, but are not limited to, parameter adjustment and feedback loops. For example, the analysis unit can adjust the parameters of the analysis algorithm based on the player's past play data. Furthermore, the analysis unit can optimize the analysis algorithm by reflecting player feedback. By optimizing the analysis algorithm according to the player's play style, more accurate analysis results can be provided.

[0036] During analysis, the analysis unit can correct the analysis results by taking into account the player's physical condition data. Physical condition data includes, but is not limited to, heart rate, fatigue level, stress level, etc. For example, if the player's heart rate is high, the analysis unit can suggest a shot to relax the player. Furthermore, if the player is tired, the analysis unit can suggest a shot to conserve energy. Furthermore, if the player is in good physical condition, the analysis unit can provide a normal analysis result. Methods for correcting the analysis results include, but are not limited to, data weighting and correction algorithms. For example, the analysis unit can adjust the weighting of the analysis results based on the player's physical condition data. Furthermore, the analysis unit can use a correction algorithm to correct the analysis results based on the physical condition data. In this way, by taking the player's physical condition data into account, more appropriate analysis results can be provided.

[0037] The analysis unit may perform the analysis by taking into account the characteristics of the club used by the player. Examples of club characteristics include, but are not limited to, weight, balance, and material. For example, if the player is using a heavy club, the analysis unit may analyze the shot by taking into account the characteristics of the club. Furthermore, if the player is using a well-balanced club, the analysis unit may analyze the shot by taking into account the characteristics of the club. Furthermore, if the player is using a light club, the analysis unit may analyze the shot by taking into account the characteristics of the club. Examples of analysis methods include, but are not limited to, data analysis methods and algorithms used. For example, the analysis unit may analyze the shot based on the weight of the club. Furthermore, the analysis unit may analyze the shot based on the balance of the club. Furthermore, the analysis unit may analyze the shot based on the material of the club. This allows for more accurate analysis results to be provided by taking into account the characteristics of the club used by the player.

[0038] During analysis, the analysis unit can reflect a region-specific play style by taking into account the player's geographical location information. Geographical location information includes, but is not limited to, GPS data and region-specific information. For example, if a player is playing in a mountainous area, the analysis unit can reflect a region-specific play style. Furthermore, if a player is playing in a coastal area, the analysis unit can reflect a region-specific play style. Furthermore, if a player is playing in an urban area, the analysis unit can reflect a region-specific play style. Region-specific play styles include, but are not limited to, the climate and cultural background of the region. For example, the analysis unit can adjust the play style by taking into account the climate of the region. Furthermore, the analysis unit can adjust the play style by taking into account the cultural background of the region. In this way, by taking into account the player's geographical location information, it is possible to provide analysis results that reflect a region-specific play style.

[0039] During the analysis, the analysis unit can analyze the player's social media activity and acquire related play data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The analysis unit performs analysis based on, for example, play data shared by the player on social media. The analysis unit can also infer a player's play style from the player's social media activity and perform analysis. The analysis unit can also perform analysis based on the play data of the player's social media friends. Related play data includes, but is not limited to, past play history and shot details. The analysis unit performs analysis based on, for example, the player's past play history. The analysis unit can also perform analysis based on shot details. This allows more relevant play data to be acquired by analyzing the player's social media activity.

[0040] During analysis, the analysis unit can customize the analysis algorithm by reflecting the player's past feedback. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The analysis unit customizes the analysis algorithm based on, for example, feedback provided by the player in the past. The analysis unit can also optimize a specific analysis algorithm based on the player's past feedback. Furthermore, the analysis unit can improve the accuracy of the analysis results by reflecting the player's feedback. Customizing the analysis algorithm can include, but is not limited to, parameter adjustment, a feedback loop, and the like. For example, the analysis unit adjusts the parameters of the analysis algorithm based on the feedback. The analysis unit can also optimize the analysis algorithm using a feedback loop. This allows the analysis algorithm to be customized by reflecting the player's past feedback, thereby providing more accurate analysis results.

[0041] When making a suggestion, the suggestion unit may adjust the level of detail of the suggestion based on the player's past success rate. Examples of success rate include, but are not limited to, shot success rate and score improvement rate. For example, the suggestion unit may provide detailed suggestions if the player's past success rate is high. Alternatively, the suggestion unit may provide simple suggestions if the player's past success rate is low. Furthermore, the suggestion unit may gradually adjust the level of detail of the suggestion depending on the player's success rate. Examples of ways to adjust the level of detail of the suggestion include, but are not limited to, the depth of information and the level of specificity. For example, the suggestion unit may provide detailed information to a player with a high success rate. Alternatively, the suggestion unit may provide simple, easy-to-understand information to a player with a low success rate. In this way, by adjusting the level of detail of the suggestion based on the player's past success rate, more appropriate suggestions can be provided.

[0042] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the player's play style. Examples of play styles include, but are not limited to, offensive, defensive, and balanced styles. For example, the suggestion unit can provide risky suggestions to a player with an aggressive play style. The suggestion unit can also provide safe suggestions to a player with a conservative play style. Furthermore, the suggestion unit can provide suggestions that balance risk and safety to a player with a balanced play style. Examples of suggestion algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the suggestion unit can use a rule-based algorithm to make suggestions depending on the player's play style. Furthermore, the suggestion unit can use a machine learning-based algorithm to make suggestions depending on the player's play style. In this way, by applying a suggestion algorithm depending on the player's play style, more appropriate suggestions can be provided.

[0043] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the player's past suggestion results. Examples of the suggestion results include, but are not limited to, the success rate of the suggestion and the user's satisfaction. The suggestion unit can improve the accuracy of the suggestion based on, for example, the player's past suggestion results. The suggestion unit can also optimize a specific suggestion algorithm based on the player's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by reflecting player feedback. Examples of ways to improve the accuracy of the suggestion include, but are not limited to, weighting data and adjusting algorithm parameters. The suggestion unit can adjust algorithm parameters based on data of the suggestion results. The suggestion unit can also optimize the suggestion algorithm using a feedback loop. This allows the accuracy of the suggestion to be improved by referring to the player's past suggestion results.

[0044] When making a suggestion, the suggestion unit may determine the priority of the suggestion based on the player's playing time. Examples of the playing time include, but are not limited to, the total playing time and the playing time of each round. For example, when a player has limited time to play, the suggestion unit may prioritize important suggestions. Furthermore, when a player has sufficient time, the suggestion unit may provide detailed suggestions. Furthermore, the suggestion unit may gradually adjust the priority of the suggestion based on the player's playing time. Examples of ways to determine the priority of the suggestion include, but are not limited to, importance and urgency. For example, when playing time is limited, the suggestion unit may prioritize important information. Furthermore, when a player has sufficient playing time, the suggestion unit may provide detailed information. Thus, by determining the priority of the suggestion based on the player's playing time, more efficient suggestions can be provided.

[0045] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the player's relevance. Examples of relevance include, but are not limited to, past play data and the player's interests. For example, the suggestion unit can prioritize suggestions that are most relevant to the player's current situation. The suggestion unit can also provide highly relevant suggestions based on the player's past play data. Furthermore, the suggestion unit can adjust the order of suggestions by reflecting player feedback. Examples of ways to adjust the order of suggestions include, but are not limited to, importance and relevance. For example, the suggestion unit prioritizes providing information that is most relevant to the current situation. The suggestion unit can also provide highly relevant information based on the past play data. By adjusting the order of suggestions based on the player's relevance, more appropriate suggestions can be provided.

[0046] When making a suggestion, the suggestion unit may adjust the use of technical terminology in the suggestion according to the player's level of expertise. Examples of the level of expertise include, but are not limited to, years of experience, qualifications, and past play data. For example, if the player is a beginner, the suggestion unit may provide simple suggestions that avoid technical terminology. For example, if the player is an intermediate player, the suggestion unit may provide suggestions that use technical terminology appropriately. For example, if the player is an advanced player, the suggestion unit may provide detailed suggestions that use a lot of technical terminology. Examples of ways to adjust the use of technical terminology include, but are not limited to, the difficulty level of the terminology and the level of detail in the explanation. For example, the suggestion unit may use simple, easy-to-understand words for beginners. For example, the suggestion unit may use technical terminology appropriately for intermediate players. In this way, by adjusting the use of technical terminology in the suggestion according to the player's level of expertise, it is possible to provide more understandable suggestions.

[0047] The collection unit can improve the accuracy of the collected data by referring to the player's past play data when collecting the data. Examples of past play data include, but are not limited to, score history, shot details, and play time. The collection unit can improve the accuracy of the collected data based on, for example, the player's past play data. The collection unit can also prioritize collecting specific data from the player's past play data. Furthermore, the collection unit can improve the accuracy of the collected data by reflecting player feedback. Methods for improving the accuracy of the collected data include, but are not limited to, data weighting and filtering. For example, the collection unit can adjust the weighting of the collected data based on the past play data. The collection unit can also improve the accuracy of the collected data using a filtering method. Thus, the accuracy of the collected data can be improved by referring to the player's past play data.

[0048] When collecting data, the collection unit can optimize data collection by taking into account the characteristics of the device used by the player. Device characteristics include, but are not limited to, the type of device, performance, and sensor accuracy. For example, if the player is using a smartphone, the collection unit can optimize data collection by taking into account the characteristics of the device. Furthermore, if the player is using a tablet, the collection unit can optimize data collection by taking into account the characteristics of the tablet. Furthermore, if the player is using a smartwatch, the collection unit can optimize data collection by taking into account the characteristics of the tablet. Examples of optimization of data collection include, but are not limited to, weighting of data and adjusting the frequency of collection. For example, the collection unit can adjust the weighting of data depending on the type of device. Furthermore, the collection unit can adjust the frequency of collection depending on the performance of the device. In this way, data collection can be optimized by taking into account the characteristics of the device used by the player.

[0049] The collection unit may collect data taking into account the player's current environment when collecting data. Examples of the current environment include, but are not limited to, weather, time of day, and surrounding conditions. For example, if a player is playing in the rain, the collection unit may collect data taking into account the environment. Furthermore, if a player is playing at night, the collection unit may collect data taking into account the environment. Furthermore, if a player is playing on a windy day, the collection unit may collect data taking into account the environment. Examples of data collection include, but are not limited to, the sensors to be used and the frequency of collection. For example, the collection unit may select the sensors to be used depending on the weather. Furthermore, the collection unit may adjust the frequency of collection depending on the time of day. This allows for more appropriate data collection by taking into account the player's current environment.

[0050] When collecting data, the collection unit can prioritize collecting region-specific data by taking into account the geographical location information of the player. Geographical location information includes, but is not limited to, GPS data and region-specific information. For example, if the player is playing in a mountainous area, the collection unit can prioritize collecting region-specific data. Furthermore, if the player is playing in a coastal area, the collection unit can prioritize collecting region-specific data. Furthermore, if the player is playing in an urban area, the collection unit can prioritize collecting region-specific data. Region-specific data includes, but is not limited to, the climate and cultural background of the region. For example, the collection unit collects data by taking into account the climate of the region. Furthermore, the collection unit can collect data by taking into account the cultural background of the region. In this way, region-specific data can be prioritized by taking into account the geographical location information of the player.

[0051] During collection, the collection unit may analyze the player's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit may, for example, collect data based on play data shared by the player on social media. The collection unit may also collect related data from the player's social media activity. Furthermore, the collection unit may collect data based on the play data of the player's social media friends. Related data may, for example, include, but is not limited to, past play history and shot details. The collection unit may, for example, collect data based on the player's past play history. The collection unit may also collect data based on shot details. This allows for more relevant data to be collected by analyzing the player's social media activity.

[0052] The collection unit can customize the collection method by reflecting the player's past feedback when collecting data. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The collection unit customizes the collection method based on, for example, feedback provided by the player in the past. The collection unit can also prioritize collecting specific data from the player's past feedback. Furthermore, the collection unit can improve the accuracy of the collected data by reflecting the player's feedback. Examples of ways to customize the collection method include, but are not limited to, parameter adjustment and feedback loops. For example, the collection unit adjusts parameters of the collection method based on the feedback. The collection unit can also optimize the collection method using a feedback loop. This allows the collection method to be customized by reflecting the player's past feedback, thereby collecting more accurate data.

[0053] During adjustment, the adjustment unit can improve the accuracy of strategy adjustment by referring to the player's past play data. Past play data includes, but is not limited to, examples of score history, shot details, and play time. The adjustment unit improves the accuracy of strategy adjustment based on, for example, the player's past play data. The adjustment unit can also prioritize and adjust a specific strategy based on the player's past play data. Furthermore, the adjustment unit can also improve the accuracy of strategy adjustment by reflecting player feedback. Methods for improving the adjustment accuracy include, but are not limited to, data weighting and filtering methods. For example, the adjustment unit adjusts the weighting of a strategy based on the past play data. The adjustment unit can also improve the accuracy of strategy adjustment by using a filtering method. In this way, the accuracy of strategy adjustment can be improved by referring to the player's past play data.

[0054] During adjustment, the adjustment unit can adjust the strategy by taking into account the characteristics of the club used by the player. Examples of club characteristics include, but are not limited to, weight, balance, and material. For example, if the player uses a heavy club, the adjustment unit can adjust the strategy by taking into account the characteristics of the club. Furthermore, if the player uses a well-balanced club, the adjustment unit can adjust the strategy by taking into account the characteristics of the club. Furthermore, if the player uses a light club, the adjustment unit can adjust the strategy by taking into account the characteristics of the club. Methods for adjusting the strategy include, but are not limited to, data analysis methods and adjustment algorithms. For example, the adjustment unit can adjust the strategy based on the weight of the club. Furthermore, the adjustment unit can adjust the strategy based on the balance of the club. Furthermore, the adjustment unit can adjust the strategy based on the material of the club. This makes it possible to provide a more appropriate strategy by taking into account the characteristics of the club used by the player.

[0055] During adjustment, the adjustment unit can adjust the strategy taking into account the player's current physical condition data. Physical condition data includes, but is not limited to, for example, heart rate, fatigue level, stress level, etc. For example, if the player is tired, the adjustment unit can suggest a strategy to conserve energy. Furthermore, if the player's heart rate is high, the adjustment unit can also suggest a strategy to relax. Furthermore, if the player's physical condition is good, the adjustment unit can also suggest a normal strategy. Methods for adjusting the strategy include, but are not limited to, data analysis techniques and adjustment algorithms. For example, the adjustment unit can adjust the weighting of the strategy based on the physical condition data. Furthermore, the adjustment unit can also adjust the strategy based on the physical condition data using an adjustment algorithm. This makes it possible to provide a more appropriate strategy by taking into account the player's current physical condition data.

[0056] When making adjustments, the adjustment unit can reflect a region-specific strategy by taking into account the geographical location information of the player. Geographical location information includes, but is not limited to, GPS data, region-specific information, and the like. For example, if the player is playing in a mountainous area, the adjustment unit can reflect a region-specific strategy. Furthermore, if the player is playing in a coastal area, the adjustment unit can also reflect a region-specific strategy. Furthermore, if the player is playing in an urban area, the adjustment unit can also reflect a region-specific strategy. Region-specific strategies include, but are not limited to, the climate and cultural background of the region. For example, the adjustment unit can adjust the strategy by taking into account the climate of the region. Furthermore, the adjustment unit can adjust the strategy by taking into account the cultural background of the region. This makes it possible to make adjustments that reflect region-specific strategies by taking into account the geographical location information of the player.

[0057] When making the adjustment, the adjustment unit may analyze the player's social media activity and adjust the relevant strategy. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. The adjustment unit may adjust the strategy based on, for example, play data shared by the player on social media. The adjustment unit may also adjust the relevant strategy based on the player's social media activity. The adjustment unit may also adjust the strategy based on the play data of the player's friends on social media. The relevant strategy may include, but is not limited to, for example, past play data, the player's interests, etc. The adjustment unit may adjust the strategy based on, for example, the player's past play data. The adjustment unit may also adjust the strategy based on the player's interests. In this way, by analyzing the player's social media activity, a more relevant strategy may be provided.

[0058] During adjustment, the adjustment unit can customize the strategy by reflecting the player's past feedback. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The adjustment unit customizes the strategy based on, for example, feedback provided by the player in the past. The adjustment unit can also prioritize and adjust a specific strategy based on the player's past feedback. Furthermore, the adjustment unit can improve the accuracy of the strategy by reflecting the player's feedback. Examples of ways to customize the strategy include, but are not limited to, parameter adjustment and a feedback loop. For example, the adjustment unit adjusts the parameters of the strategy based on the feedback. The adjustment unit can also optimize the strategy using a feedback loop. This allows the strategy to be customized by reflecting the player's past feedback, thereby providing a more appropriate strategy.

[0059] The providing unit can improve the accuracy of the provided content by referring to the player's past play data when providing the content. Past play data includes, but is not limited to, examples of score history, shot details, and play time. The providing unit can improve the accuracy of the provided content based on, for example, the player's past play data. The providing unit can also prioritize providing specific content from the player's past play data. Furthermore, the providing unit can also improve the accuracy of the provided content by reflecting player feedback. Methods for improving the accuracy of the provided content include, but are not limited to, data weighting and filtering. For example, the providing unit can adjust the weighting of the provided content based on the past play data. The providing unit can also improve the accuracy of the provided content by using a filtering method. In this way, the accuracy of the provided content can be improved by referring to the player's past play data.

[0060] The providing unit can optimize the provided content by taking into account the player's current environment when providing the information. The current environment includes, but is not limited to, weather, time of day, and surrounding conditions. For example, if the player is playing in the rain, the providing unit can optimize the provided content by taking into account the environment. Furthermore, if the player is playing at night, the providing unit can optimize the provided content by taking into account the environment. Furthermore, if the player is playing on a windy day, the providing unit can optimize the provided content by taking into account the environment. Examples of optimization of the provided content include, but are not limited to, the amount of information, the level of detail, and the like. For example, the providing unit can adjust the amount of information to be provided depending on the weather. Furthermore, the providing unit can adjust the level of detail of the information to be provided depending on the time of day. This allows more appropriate information to be provided by taking into account the player's current environment.

[0061] The providing unit may adjust the providing method by taking into account the characteristics of the device used by the player when providing information. Device characteristics include, but are not limited to, the type of device, its performance, and the accuracy of its sensors. For example, if the player is using a smartphone, the providing unit may adjust the providing method by taking into account the characteristics of the device. Furthermore, if the player is using a tablet, the providing unit may adjust the providing method by taking into account the characteristics of the tablet. Furthermore, if the player is using a smartwatch, the providing unit may adjust the providing method by taking into account the characteristics of the tablet. Examples of adjusting the providing method include, but are not limited to, changing the display format or interface. For example, the providing unit may adjust the display format depending on the type of device. Furthermore, the providing unit may change the interface depending on the performance of the device. This allows more appropriate information to be provided by taking into account the characteristics of the device used by the player.

[0062] The providing unit may provide region-specific content by taking into account the geographical location information of the player. Geographical location information includes, but is not limited to, GPS data and region-specific information. For example, if the player is playing in a mountainous area, the providing unit may provide region-specific content. Furthermore, if the player is playing in a coastal area, the providing unit may provide region-specific content. Furthermore, if the player is playing in an urban area, the providing unit may provide region-specific content. Region-specific content may include, but is not limited to, the climate and cultural background of the region. For example, the providing unit may provide information by taking into account the climate of the region. Furthermore, the providing unit may provide information by taking into account the cultural background of the region. In this way, region-specific content can be provided by taking into account the geographical location information of the player.

[0063] The providing unit may analyze the player's social media activity at the time of providing the information and provide related content. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit may provide related information based on, for example, the content shared by the player on social media. The providing unit may also provide related content based on the player's social media activity. The providing unit may also provide related content based on the activity of the player's friends on social media. The related content may include, but is not limited to, for example, past play data and the player's interests. The providing unit may provide related information based on, for example, the player's past play data. The providing unit may also provide related information based on the player's interests. In this way, more relevant content can be provided by analyzing the player's social media activity.

[0064] The providing unit can customize the content to be provided by reflecting the player's past feedback when providing the content. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The providing unit customizes the content to be provided based on, for example, feedback provided by the player in the past. The providing unit can also prioritize providing specific content based on the player's past feedback. Furthermore, the providing unit can also improve the accuracy of the content to be provided by reflecting the player's feedback. Examples of ways to customize the content to be provided include, but are not limited to, parameter adjustment and feedback loops. For example, the providing unit adjusts parameters of the content to be provided based on the feedback. Furthermore, the providing unit can optimize the content to be provided by using a feedback loop. This allows the content to be customized by reflecting the player's past feedback, thereby providing more appropriate information.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can not only analyze the player's swing data and shot results, but also take into account the player's physical condition data. For example, it can measure the player's heart rate and fatigue level and identify areas for swing improvement based on this data. It can also analyze the player's stress level and provide advice on how to relax if stress levels are high. Furthermore, it can provide specific advice on improving shot accuracy based on the player's physical condition data. This makes it possible to analyze the player's swing data while taking their physical condition into account, allowing for more appropriate advice to be provided.

[0067] The collection unit can also collect data taking into account the characteristics of the club used by the player. For example, if the player uses a heavy club, the collection unit can collect swing data taking into account those characteristics. Also, if the player uses a well-balanced club, the collection unit can collect shot data taking into account those characteristics. Furthermore, if the player uses a light club, the collection unit can collect swing data taking into account those characteristics. This makes it possible to collect data taking into account the characteristics of the club used by the player, thereby providing more accurate data.

[0068] The adjustment unit can also adjust the strategy by reflecting the player's past feedback. For example, it can adjust the parameters of the strategy based on feedback provided by the player in the past. It can also prioritize and adjust a specific strategy based on the player's feedback. It can also improve the accuracy of the strategy by reflecting the player's feedback. This makes it possible to adjust the strategy by reflecting the player's past feedback, and provide a more appropriate strategy.

[0069] The analysis unit can also reflect a region-specific play style by taking into account the player's geographical location information. For example, if the player is playing in a mountainous area, the analysis unit can reflect a region-specific play style. Also, if the player is playing in a coastal area, the analysis unit can reflect a region-specific play style. Furthermore, if the player is playing in an urban area, the analysis unit can reflect a region-specific play style. This enables analysis that takes into account the player's geographical location information, allowing for more appropriate advice to be provided.

[0070] The suggestion unit can also adjust the level of detail of the suggestion based on the player's past success rate. For example, if the player's past success rate is high, detailed suggestions can be provided. Alternatively, if the player's past success rate is low, simple suggestions can be provided. Furthermore, the level of detail of the suggestion can be adjusted in stages according to the player's success rate. This makes it possible to adjust the suggestion based on the player's past success rate, thereby providing more appropriate advice.

[0071] The adjustment unit can also adjust the strategy taking into account the characteristics of the club used by the player. For example, if the player uses a heavy club, the adjustment unit can adjust the strategy taking into account the characteristics. Also, if the player uses a well-balanced club, the adjustment unit can adjust the strategy taking into account the characteristics. Furthermore, if the player uses a light club, the adjustment unit can adjust the strategy taking into account the characteristics. This makes it possible to adjust the strategy taking into account the characteristics of the club used by the player, and to provide a more appropriate strategy.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The analysis unit analyzes the player's skill level and past play data. A player's skill level includes their score, success rate, years of experience, etc. The analysis unit collects the player's swing data and shot results and identifies areas for improvement. It can also use AI to analyze the data and evaluate the player's skill level. Step 2: The proposal section proposes a strategy based on the analysis results obtained by the analysis section. The proposal section provides advice on which club to use, which direction to hit, etc. It can also suggest optimal shots taking into account wind speed, wind direction, and terrain information. Step 3: The collector collects wind speed, wind direction, and terrain information in real time. The collector collects information using sensors that measure wind speed and wind direction, and can also use GPS data to collect terrain information. Step 4: The Coordination Department adjusts the strategy based on the collected information. If the wind is strong, the Coordination Department can suggest shots to minimize the impact of the wind, and can also suggest the optimal route taking into account the terrain and the location of obstacles. Step 5: The provider provides entertainment elements to the player. The provider provides entertainment information such as golf trivia and jokes, and can also provide golf history and stories about famous players according to the player's preferences.

[0074] (Example 2) A system according to an embodiment of the present invention provides golf caddie services using AI. This system analyzes a player's skill level and past play data, proposes an optimal strategy, collects information on wind speed, wind direction, and terrain in real time, adjusts the strategy based on that information, and provides a fun element to the player. This system can enhance the golf experience by analyzing a player's skill level and past play data, proposing an optimal strategy, collecting information in real time, adjusting the strategy, and providing a fun element. For example, the system performs a detailed analysis of a player's skill level and past play data and has the AI ​​analyze it. During this analysis, the system collects the player's swing data and shot results to identify areas where improvement is needed. Next, based on the analysis results, the AI ​​proposes an optimal strategy to the player. Specifically, the AI ​​provides advice on which club to use and which direction to hit the ball. Furthermore, the AI ​​collects information on wind speed, wind direction, and terrain in real time and adjusts the strategy based on that information. Finally, the system also provides fun elements such as golf trivia and jokes based on the player's preferences. This allows the player to play in a relaxed atmosphere and enhances the golf experience.

[0075] A golf caddie system according to an embodiment includes an analysis unit, a suggestion unit, a collection unit, an adjustment unit, and a provision unit. The analysis unit analyzes a player's skill level and past play data. Examples of a player's skill level include, but are not limited to, scores, success rates, and years of experience. The analysis unit, for example, collects a player's swing data and shot results and identifies areas where improvement is needed. The analysis unit can also analyze data using AI to evaluate a player's skill level. The suggestion unit proposes a strategy based on the analysis results obtained by the analysis unit. The suggestion unit provides advice, for example, on which club to use and which direction to hit the ball. The suggestion unit can also propose an optimal shot taking into account wind speed, wind direction, and terrain information. The collection unit collects information on wind speed, wind direction, and terrain in real time. The collection unit collects information using, for example, a sensor that measures wind speed and wind direction. The collection unit can also use GPS data to collect terrain information. The adjustment unit adjusts the strategy based on the collected information. For example, when the wind is strong, the adjustment unit suggests a shot that minimizes the effect of the wind. The adjustment unit can also suggest an optimal route taking into account the undulations of the terrain and the location of obstacles. The provision unit provides fun elements to the player. The provision unit provides entertainment information such as golf trivia and jokes. The provision unit can also provide golf history and anecdotes about famous players according to the player's preferences. As a result, the golf caddie system according to the embodiment can analyze the player's skill level and past play data, suggest an optimal strategy, collect information in real time, adjust the strategy, and provide fun elements, thereby improving the golf experience.

[0076] The analysis unit can analyze a player's swing data and shot results. Examples of swing data include, but are not limited to, swing speed, angle, and trajectory. For example, the analysis unit can measure a player's swing speed and identify areas for improvement in the swing. The analysis unit can also analyze the swing angle and suggest an optimal swing angle. The analysis unit can also analyze the swing trajectory and provide advice for improving swing consistency. Examples of shot results include, but are not limited to, shot distance, direction, and accuracy. For example, the analysis unit can measure the shot distance and provide advice for improving the distance. The analysis unit can also analyze the shot direction and provide advice for hitting accurate shots. The analysis unit can also analyze the shot accuracy and provide advice for improving the success rate of the shot. Thus, by analyzing a player's swing data and shot results, more specific areas for improvement can be identified.

[0077] The suggestion unit can suggest an appropriate shot by taking into consideration information on wind speed, wind direction, and terrain. An appropriate shot includes, but is not limited to, for example, club selection, shot strength, and direction. The suggestion unit can select an optimal club by taking into consideration information on wind speed and wind direction, for example. The suggestion unit can also adjust the shot strength by taking into consideration information on terrain. The suggestion unit can also suggest an optimal shot direction by comprehensively taking into consideration information on wind speed, wind direction, and terrain. This makes it possible to suggest more accurate shots by taking into consideration information on wind speed, wind direction, and terrain.

[0078] The collection unit can collect information on wind speed, wind direction, and topography in real time. Examples of real-time collection include, but are not limited to, the frequency of data updates and the sensors used. For example, the collection unit collects information in real time using sensors that measure wind speed and wind direction. The collection unit can also use GPS data to collect topography information. Furthermore, the collection unit can instantly analyze the data collected in real time and reflect it in strategic adjustments. This allows for real-time information collection to always provide strategic proposals based on the latest information.

[0079] The adjustment unit can adjust the strategy based on the collected information. Examples of methods for adjusting the strategy include, but are not limited to, data analysis methods and adjustment algorithms. For example, when the wind is strong, the adjustment unit can suggest a shot that minimizes the effect of the wind. The adjustment unit can also suggest an optimal route taking into account the undulations of the terrain and the location of obstacles. Furthermore, the adjustment unit can adjust the strategy in real time based on the collected information. As a result, a more appropriate strategy can be provided by adjusting the strategy based on the collected information.

[0080] The providing unit may provide golf trivia and entertainment information according to the player's preferences. Examples of golf trivia include, but are not limited to, historical facts and technical tips. The providing unit may provide, for example, the history of golf and anecdotes about famous players. The providing unit may also provide technical tips and trivia according to the player's preferences. Examples of entertainment information include, but are not limited to, jokes, quizzes, videos, and the like. For example, the providing unit may provide jokes and quizzes to help the player relax. The providing unit may also provide golf-related videos to entertain the player. This may improve the golf experience by providing fun elements according to the player's preferences.

[0081] The analysis unit can estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the analysis unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The analysis unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the analysis unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Methods for adjusting the accuracy of the analysis include, but are not limited to, data weighting and algorithm parameter adjustment. For example, if the player is nervous, the analysis unit can increase the accuracy of the analysis and provide more specific advice. If the player is relaxed, the analysis unit can slightly relax the accuracy of the analysis and provide general advice. If the player is impatient, the analysis unit can increase the accuracy of the analysis and provide quick results. This allows the analysis accuracy to be adjusted according to the player's emotions, enabling more appropriate advice to be provided.

[0082] The analysis unit can analyze a player's past play data and optimize an analysis algorithm for a specific play style. Examples of play styles include, but are not limited to, offensive, defensive, and balanced styles. For example, if a player has an aggressive play style, the analysis unit can optimize an analysis algorithm for risky shots. Furthermore, if a player has a conservative play style, the analysis unit can optimize an analysis algorithm for safe shots. Furthermore, if a player has a balanced play style, the analysis unit can optimize an analysis algorithm that strikes a balance between risk and safety. Methods for optimizing the analysis algorithm include, but are not limited to, parameter adjustment and feedback loops. For example, the analysis unit can adjust the parameters of the analysis algorithm based on the player's past play data. Furthermore, the analysis unit can optimize the analysis algorithm by reflecting player feedback. By optimizing the analysis algorithm according to the player's play style, more accurate analysis results can be provided.

[0083] During analysis, the analysis unit can correct the analysis results by taking into account the player's physical condition data. Physical condition data includes, but is not limited to, heart rate, fatigue level, stress level, etc. For example, if the player's heart rate is high, the analysis unit can suggest a shot to relax the player. Furthermore, if the player is tired, the analysis unit can suggest a shot to conserve energy. Furthermore, if the player is in good physical condition, the analysis unit can provide a normal analysis result. Methods for correcting the analysis results include, but are not limited to, data weighting and correction algorithms. For example, the analysis unit can adjust the weighting of the analysis results based on the player's physical condition data. Furthermore, the analysis unit can use a correction algorithm to correct the analysis results based on the physical condition data. In this way, by taking the player's physical condition data into account, more appropriate analysis results can be provided.

[0084] The analysis unit may perform the analysis by taking into account the characteristics of the club used by the player. Examples of club characteristics include, but are not limited to, weight, balance, and material. For example, if the player is using a heavy club, the analysis unit may analyze the shot by taking into account the characteristics of the club. Furthermore, if the player is using a well-balanced club, the analysis unit may analyze the shot by taking into account the characteristics of the club. Furthermore, if the player is using a light club, the analysis unit may analyze the shot by taking into account the characteristics of the club. Examples of analysis methods include, but are not limited to, data analysis methods and algorithms used. For example, the analysis unit may analyze the shot based on the weight of the club. Furthermore, the analysis unit may analyze the shot based on the balance of the club. Furthermore, the analysis unit may analyze the shot based on the material of the club. This allows for more accurate analysis results to be provided by taking into account the characteristics of the club used by the player.

[0085] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the analysis unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The analysis unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the analysis unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Examples of ways to adjust the display method include, but are not limited to, changing the color, adjusting the font size, and changing the layout. For example, if the player is nervous, the analysis unit can provide a simple, highly visible display method. If the player is relaxed, the analysis unit can provide a display method that includes detailed information. If the player is impatient, the analysis unit can provide a display method that focuses on the key points. This allows the display method of the analysis results to be adjusted according to the player's emotions, providing more appropriate information.

[0086] During analysis, the analysis unit can reflect a region-specific play style by taking into account the player's geographical location information. Geographical location information includes, but is not limited to, GPS data and region-specific information. For example, if a player is playing in a mountainous area, the analysis unit can reflect a region-specific play style. Furthermore, if a player is playing in a coastal area, the analysis unit can reflect a region-specific play style. Furthermore, if a player is playing in an urban area, the analysis unit can reflect a region-specific play style. Region-specific play styles include, but are not limited to, the climate and cultural background of the region. For example, the analysis unit can adjust the play style by taking into account the climate of the region. Furthermore, the analysis unit can adjust the play style by taking into account the cultural background of the region. In this way, by taking into account the player's geographical location information, it is possible to provide analysis results that reflect a region-specific play style.

[0087] During the analysis, the analysis unit can analyze the player's social media activity and acquire related play data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The analysis unit performs analysis based on, for example, play data shared by the player on social media. The analysis unit can also infer a player's play style from the player's social media activity and perform analysis. The analysis unit can also perform analysis based on the play data of the player's social media friends. Related play data includes, but is not limited to, past play history and shot details. The analysis unit performs analysis based on, for example, the player's past play history. The analysis unit can also perform analysis based on shot details. This allows more relevant play data to be acquired by analyzing the player's social media activity.

[0088] During analysis, the analysis unit can customize the analysis algorithm by reflecting the player's past feedback. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The analysis unit customizes the analysis algorithm based on, for example, feedback provided by the player in the past. The analysis unit can also optimize a specific analysis algorithm based on the player's past feedback. Furthermore, the analysis unit can improve the accuracy of the analysis results by reflecting the player's feedback. Customizing the analysis algorithm can include, but is not limited to, parameter adjustment, a feedback loop, and the like. For example, the analysis unit adjusts the parameters of the analysis algorithm based on the feedback. The analysis unit can also optimize the analysis algorithm using a feedback loop. This allows the analysis algorithm to be customized by reflecting the player's past feedback, thereby providing more accurate analysis results.

[0089] The suggestion unit can estimate the player's emotions and adjust the way suggestions are expressed based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the suggestion unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The suggestion unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the suggestion unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Methods for adjusting the way suggestions are expressed include, but are not limited to, changing the wording or format. For example, if the player is nervous, the suggestion unit can provide a simple, highly visible suggestion. If the player is relaxed, the suggestion unit can provide a suggestion with detailed information. If the player is impatient, the suggestion unit can provide a suggestion that focuses on the key points. This allows the suggestion unit to adjust the way suggestions are expressed based on the player's emotions, thereby providing more appropriate suggestions.

[0090] When making a suggestion, the suggestion unit may adjust the level of detail of the suggestion based on the player's past success rate. Examples of success rate include, but are not limited to, shot success rate and score improvement rate. For example, the suggestion unit may provide detailed suggestions if the player's past success rate is high. Alternatively, the suggestion unit may provide simple suggestions if the player's past success rate is low. Furthermore, the suggestion unit may gradually adjust the level of detail of the suggestion depending on the player's success rate. Examples of ways to adjust the level of detail of the suggestion include, but are not limited to, the depth of information and the level of specificity. For example, the suggestion unit may provide detailed information to a player with a high success rate. Alternatively, the suggestion unit may provide simple, easy-to-understand information to a player with a low success rate. In this way, by adjusting the level of detail of the suggestion based on the player's past success rate, more appropriate suggestions can be provided.

[0091] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the player's play style. Examples of play styles include, but are not limited to, offensive, defensive, and balanced styles. For example, the suggestion unit can provide risky suggestions to a player with an aggressive play style. The suggestion unit can also provide safe suggestions to a player with a conservative play style. Furthermore, the suggestion unit can provide suggestions that balance risk and safety to a player with a balanced play style. Examples of suggestion algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the suggestion unit can use a rule-based algorithm to make suggestions depending on the player's play style. Furthermore, the suggestion unit can use a machine learning-based algorithm to make suggestions depending on the player's play style. In this way, by applying a suggestion algorithm depending on the player's play style, more appropriate suggestions can be provided.

[0092] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the player's past suggestion results. Examples of the suggestion results include, but are not limited to, the success rate of the suggestion and the user's satisfaction. The suggestion unit can improve the accuracy of the suggestion based on, for example, the player's past suggestion results. The suggestion unit can also optimize a specific suggestion algorithm based on the player's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by reflecting player feedback. Examples of ways to improve the accuracy of the suggestion include, but are not limited to, weighting data and adjusting algorithm parameters. The suggestion unit can adjust algorithm parameters based on data of the suggestion results. The suggestion unit can also optimize the suggestion algorithm using a feedback loop. This allows the accuracy of the suggestion to be improved by referring to the player's past suggestion results.

[0093] The suggestion unit can estimate the player's emotions and adjust the length of the suggestions based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the suggestion unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The suggestion unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the suggestion unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Methods for adjusting the length of the suggestions include, but are not limited to, the amount of information and the level of detail. For example, if the player is nervous, the suggestion unit can provide a short and concise suggestion. If the player is relaxed, the suggestion unit can provide a detailed suggestion. If the player is impatient, the suggestion unit can provide a quick and concise suggestion. This allows the length of the suggestions to be adjusted according to the player's emotions, thereby providing more appropriate suggestions.

[0094] When making a suggestion, the suggestion unit may determine the priority of the suggestion based on the player's playing time. Examples of the playing time include, but are not limited to, the total playing time and the playing time of each round. For example, when a player has limited time to play, the suggestion unit may prioritize important suggestions. Furthermore, when a player has sufficient time, the suggestion unit may provide detailed suggestions. Furthermore, the suggestion unit may gradually adjust the priority of the suggestion based on the player's playing time. Examples of ways to determine the priority of the suggestion include, but are not limited to, importance and urgency. For example, when playing time is limited, the suggestion unit may prioritize important information. Furthermore, when a player has sufficient playing time, the suggestion unit may provide detailed information. Thus, by determining the priority of the suggestion based on the player's playing time, more efficient suggestions can be provided.

[0095] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the player's relevance. Examples of relevance include, but are not limited to, past play data and the player's interests. For example, the suggestion unit can prioritize suggestions that are most relevant to the player's current situation. The suggestion unit can also provide highly relevant suggestions based on the player's past play data. Furthermore, the suggestion unit can adjust the order of suggestions by reflecting player feedback. Examples of ways to adjust the order of suggestions include, but are not limited to, importance and relevance. For example, the suggestion unit prioritizes providing information that is most relevant to the current situation. The suggestion unit can also provide highly relevant information based on the past play data. By adjusting the order of suggestions based on the player's relevance, more appropriate suggestions can be provided.

[0096] When making a suggestion, the suggestion unit may adjust the use of technical terminology in the suggestion according to the player's level of expertise. Examples of the level of expertise include, but are not limited to, years of experience, qualifications, and past play data. For example, if the player is a beginner, the suggestion unit may provide simple suggestions that avoid technical terminology. For example, if the player is an intermediate player, the suggestion unit may provide suggestions that use technical terminology appropriately. For example, if the player is an advanced player, the suggestion unit may provide detailed suggestions that use a lot of technical terminology. Examples of ways to adjust the use of technical terminology include, but are not limited to, the difficulty level of the terminology and the level of detail in the explanation. For example, the suggestion unit may use simple, easy-to-understand words for beginners. For example, the suggestion unit may use technical terminology appropriately for intermediate players. In this way, by adjusting the use of technical terminology in the suggestion according to the player's level of expertise, it is possible to provide more understandable suggestions.

[0097] The collection unit can estimate the player's emotions and determine the priority of data to be collected based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the collection unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The collection unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the collection unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Data prioritization can include, but is not limited to, importance and urgency. For example, if the player is nervous, the collection unit can prioritize collecting important data. Furthermore, if the player is relaxed, the collection unit can also collect detailed data. Furthermore, if the player is impatient, the collection unit can prioritize data that can be collected quickly. Thus, by determining the priority of data to be collected based on the player's emotions, more important data can be collected preferentially.

[0098] The collection unit can improve the accuracy of the collected data by referring to the player's past play data when collecting the data. Examples of past play data include, but are not limited to, score history, shot details, and play time. The collection unit can improve the accuracy of the collected data based on, for example, the player's past play data. The collection unit can also prioritize collecting specific data from the player's past play data. Furthermore, the collection unit can improve the accuracy of the collected data by reflecting player feedback. Methods for improving the accuracy of the collected data include, but are not limited to, data weighting and filtering. For example, the collection unit can adjust the weighting of the collected data based on the past play data. The collection unit can also improve the accuracy of the collected data using a filtering method. Thus, the accuracy of the collected data can be improved by referring to the player's past play data.

[0099] When collecting data, the collection unit can optimize data collection by taking into account the characteristics of the device used by the player. Device characteristics include, but are not limited to, the type of device, performance, and sensor accuracy. For example, if the player is using a smartphone, the collection unit can optimize data collection by taking into account the characteristics of the device. Furthermore, if the player is using a tablet, the collection unit can optimize data collection by taking into account the characteristics of the tablet. Furthermore, if the player is using a smartwatch, the collection unit can optimize data collection by taking into account the characteristics of the tablet. Examples of optimization of data collection include, but are not limited to, weighting of data and adjusting the frequency of collection. For example, the collection unit can adjust the weighting of data depending on the type of device. Furthermore, the collection unit can adjust the frequency of collection depending on the performance of the device. In this way, data collection can be optimized by taking into account the characteristics of the device used by the player.

[0100] The collection unit may collect data taking into account the player's current environment when collecting data. Examples of the current environment include, but are not limited to, weather, time of day, and surrounding conditions. For example, if a player is playing in the rain, the collection unit may collect data taking into account the environment. Furthermore, if a player is playing at night, the collection unit may collect data taking into account the environment. Furthermore, if a player is playing on a windy day, the collection unit may collect data taking into account the environment. Examples of data collection include, but are not limited to, the sensors to be used and the frequency of collection. For example, the collection unit may select the sensors to be used depending on the weather. Furthermore, the collection unit may adjust the frequency of collection depending on the time of day. This allows for more appropriate data collection by taking into account the player's current environment.

[0101] The collection unit can estimate the player's emotions and adjust the display method of the collected data based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the collection unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The collection unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the collection unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Examples of adjustments to the display method include, but are not limited to, changing the color, adjusting the font size, and changing the layout. For example, if the player is nervous, the collection unit can provide a simple, highly visible display method. If the player is relaxed, the collection unit can provide a display method that includes detailed information. If the player is impatient, the collection unit can provide a display method that focuses on the main points. This allows the display method of the collected data to be adjusted according to the player's emotions, thereby providing more appropriate information.

[0102] When collecting data, the collection unit can prioritize collecting region-specific data by taking into account the geographical location information of the player. Geographical location information includes, but is not limited to, GPS data and region-specific information. For example, if the player is playing in a mountainous area, the collection unit can prioritize collecting region-specific data. Furthermore, if the player is playing in a coastal area, the collection unit can prioritize collecting region-specific data. Furthermore, if the player is playing in an urban area, the collection unit can prioritize collecting region-specific data. Region-specific data includes, but is not limited to, the climate and cultural background of the region. For example, the collection unit collects data by taking into account the climate of the region. Furthermore, the collection unit can collect data by taking into account the cultural background of the region. In this way, region-specific data can be prioritized by taking into account the geographical location information of the player.

[0103] During collection, the collection unit may analyze the player's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit may, for example, collect data based on play data shared by the player on social media. The collection unit may also collect related data from the player's social media activity. Furthermore, the collection unit may collect data based on the play data of the player's social media friends. Related data may, for example, include, but is not limited to, past play history and shot details. The collection unit may, for example, collect data based on the player's past play history. The collection unit may also collect data based on shot details. This allows for more relevant data to be collected by analyzing the player's social media activity.

[0104] The collection unit can customize the collection method by reflecting the player's past feedback when collecting data. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The collection unit customizes the collection method based on, for example, feedback provided by the player in the past. The collection unit can also prioritize collecting specific data from the player's past feedback. Furthermore, the collection unit can improve the accuracy of the collected data by reflecting the player's feedback. Examples of ways to customize the collection method include, but are not limited to, parameter adjustment and feedback loops. For example, the collection unit adjusts parameters of the collection method based on the feedback. The collection unit can also optimize the collection method using a feedback loop. This allows the collection method to be customized by reflecting the player's past feedback, thereby collecting more accurate data.

[0105] The adjustment unit can estimate the player's emotions and change the strategy adjustment method based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the adjustment unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The adjustment unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the adjustment unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Methods for changing the adjustment method include, but are not limited to, weighting data and adjusting algorithm parameters. For example, if the player is nervous, the adjustment unit can suggest a strategy to relax. If the player is relaxed, the adjustment unit can also suggest a standard strategy. If the player is impatient, the adjustment unit can also suggest a strategy that can be executed quickly. This allows the strategy adjustment method to be changed according to the player's emotions, providing a more appropriate strategy.

[0106] During adjustment, the adjustment unit can improve the accuracy of strategy adjustment by referring to the player's past play data. Past play data includes, but is not limited to, examples of score history, shot details, and play time. The adjustment unit improves the accuracy of strategy adjustment based on, for example, the player's past play data. The adjustment unit can also prioritize and adjust a specific strategy based on the player's past play data. Furthermore, the adjustment unit can also improve the accuracy of strategy adjustment by reflecting player feedback. Methods for improving the adjustment accuracy include, but are not limited to, data weighting and filtering methods. For example, the adjustment unit adjusts the weighting of a strategy based on the past play data. The adjustment unit can also improve the accuracy of strategy adjustment by using a filtering method. In this way, the accuracy of strategy adjustment can be improved by referring to the player's past play data.

[0107] During adjustment, the adjustment unit can adjust the strategy by taking into account the characteristics of the club used by the player. Examples of club characteristics include, but are not limited to, weight, balance, and material. For example, if the player uses a heavy club, the adjustment unit can adjust the strategy by taking into account the characteristics of the club. Furthermore, if the player uses a well-balanced club, the adjustment unit can adjust the strategy by taking into account the characteristics of the club. Furthermore, if the player uses a light club, the adjustment unit can adjust the strategy by taking into account the characteristics of the club. Methods for adjusting the strategy include, but are not limited to, data analysis methods and adjustment algorithms. For example, the adjustment unit can adjust the strategy based on the weight of the club. Furthermore, the adjustment unit can adjust the strategy based on the balance of the club. Furthermore, the adjustment unit can adjust the strategy based on the material of the club. This makes it possible to provide a more appropriate strategy by taking into account the characteristics of the club used by the player.

[0108] During adjustment, the adjustment unit can adjust the strategy taking into account the player's current physical condition data. Physical condition data includes, but is not limited to, for example, heart rate, fatigue level, stress level, etc. For example, if the player is tired, the adjustment unit can suggest a strategy to conserve energy. Furthermore, if the player's heart rate is high, the adjustment unit can also suggest a strategy to relax. Furthermore, if the player's physical condition is good, the adjustment unit can also suggest a normal strategy. Methods for adjusting the strategy include, but are not limited to, data analysis techniques and adjustment algorithms. For example, the adjustment unit can adjust the weighting of the strategy based on the physical condition data. Furthermore, the adjustment unit can also adjust the strategy based on the physical condition data using an adjustment algorithm. This makes it possible to provide a more appropriate strategy by taking into account the player's current physical condition data.

[0109] The adjustment unit can estimate the player's emotions and adjust the display method of the strategies based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the adjustment unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The adjustment unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the adjustment unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Examples of adjustments to the display method include, but are not limited to, changing the color, adjusting the font size, and changing the layout. For example, if the player is nervous, the adjustment unit can provide a simple, highly visible display method. If the player is relaxed, the adjustment unit can provide a display method that includes detailed information. If the player is impatient, the adjustment unit can provide a display method that focuses on the main points. This allows the display method of the strategies to be adjusted according to the player's emotions, thereby providing more appropriate information.

[0110] When making adjustments, the adjustment unit can reflect a region-specific strategy by taking into account the geographical location information of the player. Geographical location information includes, but is not limited to, GPS data, region-specific information, and the like. For example, if the player is playing in a mountainous area, the adjustment unit can reflect a region-specific strategy. Furthermore, if the player is playing in a coastal area, the adjustment unit can also reflect a region-specific strategy. Furthermore, if the player is playing in an urban area, the adjustment unit can also reflect a region-specific strategy. Region-specific strategies include, but are not limited to, the climate and cultural background of the region. For example, the adjustment unit can adjust the strategy by taking into account the climate of the region. Furthermore, the adjustment unit can adjust the strategy by taking into account the cultural background of the region. This makes it possible to make adjustments that reflect region-specific strategies by taking into account the geographical location information of the player.

[0111] When making the adjustment, the adjustment unit may analyze the player's social media activity and adjust the relevant strategy. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. The adjustment unit may adjust the strategy based on, for example, play data shared by the player on social media. The adjustment unit may also adjust the relevant strategy based on the player's social media activity. The adjustment unit may also adjust the strategy based on the play data of the player's friends on social media. The relevant strategy may include, but is not limited to, for example, past play data, the player's interests, etc. The adjustment unit may adjust the strategy based on, for example, the player's past play data. The adjustment unit may also adjust the strategy based on the player's interests. In this way, by analyzing the player's social media activity, a more relevant strategy may be provided.

[0112] During adjustment, the adjustment unit can customize the strategy by reflecting the player's past feedback. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The adjustment unit customizes the strategy based on, for example, feedback provided by the player in the past. The adjustment unit can also prioritize and adjust a specific strategy based on the player's past feedback. Furthermore, the adjustment unit can improve the accuracy of the strategy by reflecting the player's feedback. Examples of ways to customize the strategy include, but are not limited to, parameter adjustment and a feedback loop. For example, the adjustment unit adjusts the parameters of the strategy based on the feedback. The adjustment unit can also optimize the strategy using a feedback loop. This allows the strategy to be customized by reflecting the player's past feedback, thereby providing a more appropriate strategy.

[0113] The providing unit can estimate the player's emotions and adjust the content to be provided based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the providing unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The providing unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the providing unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Methods for adjusting the content to be provided include, but are not limited to, the amount of information and the level of detail. For example, if the player is nervous, the providing unit can provide jokes and trivia to help the player relax. If the player is relaxed, the providing unit can provide detailed golf trivia. If the player is impatient, the providing unit can provide concise and fun elements. This allows the content to be adjusted according to the player's emotions, thereby providing more appropriate information.

[0114] The providing unit can improve the accuracy of the provided content by referring to the player's past play data when providing the content. Past play data includes, but is not limited to, examples of score history, shot details, and play time. The providing unit can improve the accuracy of the provided content based on, for example, the player's past play data. The providing unit can also prioritize providing specific content from the player's past play data. Furthermore, the providing unit can also improve the accuracy of the provided content by reflecting player feedback. Methods for improving the accuracy of the provided content include, but are not limited to, data weighting and filtering. For example, the providing unit can adjust the weighting of the provided content based on the past play data. The providing unit can also improve the accuracy of the provided content by using a filtering method. In this way, the accuracy of the provided content can be improved by referring to the player's past play data.

[0115] The providing unit can optimize the provided content by taking into account the player's current environment when providing the information. The current environment includes, but is not limited to, weather, time of day, and surrounding conditions. For example, if the player is playing in the rain, the providing unit can optimize the provided content by taking into account the environment. Furthermore, if the player is playing at night, the providing unit can optimize the provided content by taking into account the environment. Furthermore, if the player is playing on a windy day, the providing unit can optimize the provided content by taking into account the environment. Examples of optimization of the provided content include, but are not limited to, the amount of information, the level of detail, and the like. For example, the providing unit can adjust the amount of information to be provided depending on the weather. Furthermore, the providing unit can adjust the level of detail of the information to be provided depending on the time of day. This allows more appropriate information to be provided by taking into account the player's current environment.

[0116] The providing unit may adjust the providing method by taking into account the characteristics of the device used by the player when providing information. Device characteristics include, but are not limited to, the type of device, its performance, and the accuracy of its sensors. For example, if the player is using a smartphone, the providing unit may adjust the providing method by taking into account the characteristics of the device. Furthermore, if the player is using a tablet, the providing unit may adjust the providing method by taking into account the characteristics of the tablet. Furthermore, if the player is using a smartwatch, the providing unit may adjust the providing method by taking into account the characteristics of the tablet. Examples of adjusting the providing method include, but are not limited to, changing the display format or interface. For example, the providing unit may adjust the display format depending on the type of device. Furthermore, the providing unit may change the interface depending on the performance of the device. This allows more appropriate information to be provided by taking into account the characteristics of the device used by the player.

[0117] The providing unit can estimate the player's emotions and adjust the display method of the provided content based on the estimated player's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and biometrics. For example, the providing unit can capture the player's facial expressions with a camera and estimate the player's emotions using an emotion estimation algorithm. The providing unit can also record the player's voice and estimate the player's emotions using voice analysis technology. Furthermore, the providing unit can collect the player's biometric data (heart rate and electrodermal activity) with a sensor and estimate the player's emotions using an emotion estimation algorithm. Examples of ways to adjust the display method include, but are not limited to, changing the color, adjusting the font size, and changing the layout. For example, if the player is nervous, the providing unit can provide a simple, highly visible display method. If the player is relaxed, the providing unit can provide a display method that includes detailed information. If the player is impatient, the providing unit can provide a display method that focuses on the main points. This allows the display method of the provided content to be adjusted according to the player's emotions, thereby providing more appropriate information.

[0118] The providing unit may provide region-specific content by taking into account the geographical location information of the player. Geographical location information includes, but is not limited to, GPS data and region-specific information. For example, if the player is playing in a mountainous area, the providing unit may provide region-specific content. Furthermore, if the player is playing in a coastal area, the providing unit may provide region-specific content. Furthermore, if the player is playing in an urban area, the providing unit may provide region-specific content. Region-specific content may include, but is not limited to, the climate and cultural background of the region. For example, the providing unit may provide information by taking into account the climate of the region. Furthermore, the providing unit may provide information by taking into account the cultural background of the region. In this way, region-specific content can be provided by taking into account the geographical location information of the player.

[0119] The providing unit may analyze the player's social media activity at the time of providing the information and provide related content. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit may provide related information based on, for example, the content shared by the player on social media. The providing unit may also provide related content based on the player's social media activity. The providing unit may also provide related content based on the activity of the player's friends on social media. The related content may include, but is not limited to, for example, past play data and the player's interests. The providing unit may provide related information based on, for example, the player's past play data. The providing unit may also provide related information based on the player's interests. In this way, more relevant content can be provided by analyzing the player's social media activity.

[0120] The providing unit can customize the content to be provided by reflecting the player's past feedback when providing the content. Examples of feedback include, but are not limited to, survey results, user reviews, and comments. The providing unit customizes the content to be provided based on, for example, feedback provided by the player in the past. The providing unit can also prioritize providing specific content based on the player's past feedback. Furthermore, the providing unit can also improve the accuracy of the content to be provided by reflecting the player's feedback. Examples of ways to customize the content to be provided include, but are not limited to, parameter adjustment and feedback loops. For example, the providing unit adjusts parameters of the content to be provided based on the feedback. Furthermore, the providing unit can optimize the content to be provided by using a feedback loop. This allows the content to be customized by reflecting the player's past feedback, thereby providing more appropriate information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, collection unit, adjustment unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by a specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by a control unit 46A of the smart device 14 or a specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 or a sensor of the smart device 14 and processes the information by the control unit 46A. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides entertainment information through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, collection unit, adjustment unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by a specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by a control unit 46A of the smart glasses 214 or a specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 or a sensor of the smart glasses 214, and the information is processed by the control unit 46A. For example, the adjustment unit is realized by a specific processing unit 290 of the data processing device 12. For example, the provision unit provides entertainment information through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, collection unit, adjustment unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 or a sensor of the headset type terminal 314, and the information is processed by the control unit 46A. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides entertainment information through the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, collection unit, adjustment unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 or a sensor of the robot 414, and processes the information by the control unit 46A. For example, the adjustment unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit provides entertainment information through the speaker 240 of the robot 414.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The analysis unit can not only analyze the player's swing data and shot results, but also take into account the player's physical condition data. For example, it can measure the player's heart rate and fatigue level and identify areas for swing improvement based on this data. It can also analyze the player's stress level and provide advice on how to relax if stress levels are high. Furthermore, it can provide specific advice on improving shot accuracy based on the player's physical condition data. This makes it possible to analyze the player's swing data while taking their physical condition into account, allowing for more appropriate advice to be provided.

[0123] The suggestion unit can also estimate the player's emotions and adjust the content of the suggestion based on the estimated emotions. For example, if the player is nervous, the suggestion unit can suggest a shot that will help the player relax. If the player is relaxed, the suggestion unit can also suggest a more challenging shot. Furthermore, if the player is impatient, the suggestion unit can suggest a shot that can be executed quickly. This makes it possible to provide more appropriate advice based on the player's emotions.

[0124] The collection unit can also collect data taking into account the characteristics of the club used by the player. For example, if the player uses a heavy club, the collection unit can collect swing data taking into account those characteristics. Also, if the player uses a well-balanced club, the collection unit can collect shot data taking into account those characteristics. Furthermore, if the player uses a light club, the collection unit can collect swing data taking into account those characteristics. This makes it possible to collect data taking into account the characteristics of the club used by the player, thereby providing more accurate data.

[0125] The adjustment unit can also adjust the strategy by reflecting the player's past feedback. For example, it can adjust the parameters of the strategy based on feedback provided by the player in the past. It can also prioritize and adjust a specific strategy based on the player's feedback. It can also improve the accuracy of the strategy by reflecting the player's feedback. This makes it possible to adjust the strategy by reflecting the player's past feedback, and provide a more appropriate strategy.

[0126] The providing unit can also estimate the player's emotions and adjust the content to be provided based on the estimated emotions. For example, if the player is nervous, it can provide jokes and trivia to help the player relax. If the player is relaxed, it can provide detailed golf trivia. Furthermore, if the player is impatient, it can provide simple and fun elements. This makes it possible to adjust the content to be provided according to the player's emotions, and provide more appropriate information.

[0127] The analysis unit can also reflect a region-specific play style by taking into account the player's geographical location information. For example, if the player is playing in a mountainous area, the analysis unit can reflect a region-specific play style. Also, if the player is playing in a coastal area, the analysis unit can reflect a region-specific play style. Furthermore, if the player is playing in an urban area, the analysis unit can reflect a region-specific play style. This enables analysis that takes into account the player's geographical location information, allowing for more appropriate advice to be provided.

[0128] The suggestion unit can also adjust the level of detail of the suggestion based on the player's past success rate. For example, if the player's past success rate is high, detailed suggestions can be provided. Alternatively, if the player's past success rate is low, simple suggestions can be provided. Furthermore, the level of detail of the suggestion can be adjusted in stages according to the player's success rate. This makes it possible to adjust the suggestion based on the player's past success rate, thereby providing more appropriate advice.

[0129] The collection unit can also estimate the player's emotions and determine the priority of data to be collected based on the estimated emotions. For example, if the player is nervous, important data can be collected with priority. If the player is relaxed, detailed data can be collected. Furthermore, if the player is impatient, data that can be collected quickly can be prioritized. This makes it possible to prioritize data collection according to the player's emotions, allowing more important data to be collected with priority.

[0130] The adjustment unit can also adjust the strategy taking into account the characteristics of the club used by the player. For example, if the player uses a heavy club, the adjustment unit can adjust the strategy taking into account the characteristics. Also, if the player uses a well-balanced club, the adjustment unit can adjust the strategy taking into account the characteristics. Furthermore, if the player uses a light club, the adjustment unit can adjust the strategy taking into account the characteristics. This makes it possible to adjust the strategy taking into account the characteristics of the club used by the player, and to provide a more appropriate strategy.

[0131] The providing unit can also estimate the player's emotions and adjust the display method of the provided content based on the estimated emotions. For example, if the player is nervous, a simple, highly visible display method can be provided. If the player is relaxed, a display method including detailed information can be provided. Furthermore, if the player is impatient, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method according to the player's emotions, and provide more appropriate information.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The analysis unit analyzes the player's skill level and past play data. A player's skill level includes their score, success rate, years of experience, etc. The analysis unit collects the player's swing data and shot results and identifies areas for improvement. It can also use AI to analyze the data and evaluate the player's skill level. Step 2: The proposal section proposes a strategy based on the analysis results obtained by the analysis section. The proposal section provides advice on which club to use, which direction to hit, etc. It can also suggest optimal shots taking into account wind speed, wind direction, and terrain information. Step 3: The collector collects wind speed, wind direction, and terrain information in real time. The collector collects information using sensors that measure wind speed and wind direction, and can also use GPS data to collect terrain information. Step 4: The Coordination Department adjusts the strategy based on the collected information. If the wind is strong, the Coordination Department can suggest shots to minimize the impact of the wind, and can also suggest the optimal route taking into account the terrain and the location of obstacles. Step 5: The provider provides entertainment elements to the player. The provider provides entertainment information such as golf trivia and jokes, and can also provide golf history and stories about famous players according to the player's preferences.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis section that analyzes the player's skill level and past play data, a proposal unit that proposes a strategy based on the analysis result obtained by the analysis unit; A collection section that collects information on wind speed, wind direction, and topography in real time; an adjustment unit that adjusts a strategy based on the information collected by the collection unit; a providing unit that provides entertainment elements to players; A system characterized by:

2. The analysis unit Analyze players' swing data and shot results 2. The system of claim 1.

3. The proposal unit Suggests the best shot based on wind speed, direction, and terrain information 2. The system of claim 1.

4. The collecting unit Collecting wind speed, wind direction, and terrain information in real time 2. The system of claim 1.

5. The adjustment unit Adjust your strategy based on the information gathered 2. The system of claim 1.

6. The providing unit Providing golf trivia and entertainment information tailored to players' preferences 2. The system of claim 1.

7. The analysis unit Estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions.

2. The system of claim 1.

8. The analysis unit Analyzes players' past play data and optimizes analysis algorithms for specific play styles 2. The system of claim 1.

Images