system

Abstract

The system according to this embodiment aims to provide golf players with optimal advice tailored to the course and conditions. [Solution] The system according to the embodiment comprises a course information acquisition unit, a condition information acquisition unit, a learning unit, an advice unit, and a feature consideration unit. The course information acquisition unit acquires course information. The condition information acquisition unit acquires condition information. The learning unit learns past score information and swing results based on the information acquired by the course information acquisition unit and the condition information acquisition unit. The advice unit provides advice based on the information learned by the learning unit. The feature consideration unit provides advice that takes into account the characteristics of the player.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a golfer to obtain optimal advice according to the course and conditions.

[0005] The system according to the embodiment aims to enable a golfer to obtain optimal advice according to the course and conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a course information acquisition unit, a condition information acquisition unit, a learning unit, an advice unit, and a feature consideration unit. The course information acquisition unit acquires course information. The condition information acquisition unit acquires condition information. The learning unit learns past score information and swing results based on the information acquired by the course information acquisition unit and the condition information acquisition unit. The advice unit provides advice based on the information learned by the learning unit. The feature consideration unit provides advice that takes into account the player's characteristics. [Effects of the Invention]

[0007] The system according to this embodiment allows golfers to receive optimal advice tailored to the course and conditions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The golf support system according to an embodiment of the present invention is a system that supports golf players in achieving better scores. This golf support system acquires course information, acquires condition information, learns past score information and swing results, provides optimal advice, and provides advice that takes into account the player's characteristics and habits. For example, the golf support system acquires course information in advance. Course information includes the distance of 18 holes, pin positions, feet, etc. It also acquires condition information such as weather, wind speed, and wind direction for each course. Next, the AI ​​learns past score information and the results of each individual swing. This allows it to understand the player's characteristics and habits and provide optimal advice. The AI ​​performs course management and provides advice. For example, based on the conditions of the day and the player's characteristics, it instructs which club to use and what precautions to take during the swing. The accuracy of the AI's advice improves with each use. Specifically, when the player arrives at the course, the AI ​​proposes the optimal playing strategy based on information such as the weather, wind direction, and wind speed for that day. For example, on a windy day, it provides advice on club selection and swing that takes the wind into consideration. Furthermore, based on the player's past scores and swing data, the AI ​​provides advice that takes into account the player's habits and characteristics. In addition, the AI ​​records the swing result each time the player takes a shot, accumulating data to be used for future play. As a result, the accuracy of the AI's advice improves with each play, strengthening support for achieving better scores. In this way, an AI app that acts as a golf caddy can support players in achieving better scores and enhance the enjoyment of golf. Thus, a golf support system can help players achieve better scores.

[0029] The golf support system according to this embodiment comprises a course information acquisition unit, a condition information acquisition unit, a learning unit, an advice unit, and a feature consideration unit. The course information acquisition unit acquires course information. Course information includes, but is not limited to, the distance of 18 holes, pin positions, feet, etc. The course information acquisition unit can acquire detailed information such as the layout of the golf course, the location of obstacles, and the condition of the greens. The condition information acquisition unit acquires condition information. Condition information includes, but is not limited to, the weather, wind speed, wind direction, humidity, etc. The condition information acquisition unit can, for example, acquire weather data in real time and provide it to the player. The learning unit learns past score information and swing results. The learning unit can, for example, analyze data from the player's scorecard and play history to understand the player's characteristics and habits. The learning unit can also use AI to learn data such as the player's swing speed and angle, and the distance the ball travels. The advice unit provides advice based on the information learned by the learning unit. The advice unit can, for example, instruct the player on which club to use and provide instructions on swing techniques, based on the player's condition and characteristics on the day. The advice unit can also use AI to suggest the optimal playing strategy for the player. The characteristics consideration unit provides advice that takes into account the player's characteristics and habits. For example, the characteristics consideration unit can provide advice considering the player's skill level, playing style, and physical characteristics. The characteristics consideration unit can also use AI to provide advice tailored to the player's individual needs. As a result, the golf support system according to this embodiment can support the player in achieving better scores.

[0030] The system includes a data storage unit. The data storage unit stores data. This data includes, but is not limited to, score data, swing data, and condition data. For example, the data storage unit can store data from a player's scorecard and play history. It can also store data such as the player's swing speed and angle, and the distance the ball travels. Furthermore, the data storage unit can store weather data and course condition data. This allows the data storage unit to store data that can be used to improve future play. Some or all of the above-described processing in the data storage unit may be performed using, for example, AI, or not using AI. For example, the data storage unit can input data from a player's scorecard into an AI, which can then analyze and store the data.

[0031] The system includes a swing result recording unit. The swing result recording unit records the swing results. These results include, but are not limited to, swing speed, angle, and ball distance. For example, the swing result recording unit can measure the player's swing speed and angle using sensors and record them as data. It can also measure the ball distance and record it as data. Furthermore, the swing result recording unit can record data such as the player's swing form and the amount of force applied. This allows the swing result recording unit to record swing results that can be used to improve future play. Some or all of the above processing in the swing result recording unit may be performed using, for example, AI, or without AI. For example, the swing result recording unit can input the player's swing data into an AI, which can then analyze and record the data.

[0032] The course information acquisition unit can acquire course information such as distance, pin position, and feet for 18 holes. For example, the course information acquisition unit can measure the distance of 18 holes and acquire it as data. For example, it can measure the distance from the tee to the green for each hole. The course information acquisition unit can also acquire pin positions. For example, it can measure the exact position of the pin on the green. Furthermore, the course information acquisition unit can acquire information in feet. For example, it can acquire data using feet as the unit of distance. This allows the course information acquisition unit to acquire detailed course information. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input course layout data into AI, and the AI ​​can analyze the data and acquire it.

[0033] The condition information acquisition unit can acquire condition information such as weather, wind speed, and wind direction for each course. The condition information acquisition unit can acquire weather data, for example, data such as temperature, humidity, and precipitation. The condition information acquisition unit can also measure wind speed and acquire it as data, for example, by using an anemometer. Furthermore, the condition information acquisition unit can also measure wind direction and acquire it as data, for example, by using a wind vane. This allows the condition information acquisition unit to acquire detailed condition information. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without using AI. For example, the condition information acquisition unit can input meteorological data into an AI, and the AI ​​can analyze the data and acquire the information.

[0034] The learning unit can learn past score information and the results of each swing. For example, the learning unit can learn data from the player's scorecard. For example, it can analyze the data from the player's scorecard to understand the player's characteristics and habits. The learning unit can also learn data such as the player's swing speed and angle, and the distance the ball travels. For example, it can analyze the data from the player's swing to understand the characteristics of the player's swing. Furthermore, the learning unit can learn the player's play history. For example, it can analyze the player's past play history to understand the player's playing style. As a result, the learning unit can provide the player with optimal advice by learning from past data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data from the player's scorecard into an AI, which can then analyze and learn from the data.

[0035] The advice system can instruct players on which clubs to use and provide swing tips, taking into account the day's conditions and the player's characteristics. For example, the advice system can instruct players on which clubs to use based on the day's weather data. For instance, on a windy day, it can instruct players to choose clubs that take the wind into consideration. The advice system can also provide swing tips based on the player's characteristics. For example, it can instruct players on how to correct their swing, taking into account their swing habits. Furthermore, the advice system can instruct players on strategic play methods, taking into account their skill level. For example, it can instruct beginners on simple strategies, intermediate players on detailed strategies, and advanced players on sophisticated strategies. In this way, the advice system can support players in improving their scores by providing them with the most suitable advice. Some or all of the above processes in the advice system may be performed using AI, or not. For example, the advice system can input data from the player's scorecard into an AI, which can then analyze the data and provide advice.

[0036] The feature-aware unit can provide advice that takes into account the player's characteristics. For example, it can provide advice considering the player's skill level. For instance, it can provide basic advice to beginners, more detailed advice to intermediate players, and advanced advice to advanced players. The feature-aware unit can also provide advice considering the player's playing style. For example, it can suggest risky strategies to aggressive players and safe strategies to defensive players. Furthermore, the feature-aware unit can provide advice considering the player's physical characteristics. For example, it can instruct the player on the optimal swing form and club selection based on physical characteristics such as height, weight, and muscle strength. In this way, the feature-aware unit can support score improvement by providing advice based on the player's individual characteristics. Some or all of the above processing in the feature-aware unit may be performed using AI, for example, or not. For example, the feature-aware unit can input data from the player's scorecard into an AI, which can then analyze the data and provide advice.

[0037] The course information acquisition unit can select the optimal acquisition method by referring to past play data when acquiring course information. For example, the course information acquisition unit can prioritize acquiring information on courses that have been played in the past. For example, it can acquire detailed information on particularly important holes from past play data. The course information acquisition unit can also prioritize acquiring information on holes that the user struggles with, based on past play data. For example, it can analyze past play data and prioritize acquiring information on holes where the user tends to score poorly. In this way, the course information acquisition unit can acquire optimal course information by referring to past play data. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without using AI. For example, the course information acquisition unit can input past play data into AI, and the AI ​​can analyze the data and select the optimal acquisition method.

[0038] The course information acquisition unit can filter course information based on the player's current skill level. For example, it can prioritize acquiring simple course information for beginners. For example, it can provide beginners with information such as basic course layout and obstacle locations. The course information acquisition unit can also acquire detailed course information for intermediate players. For example, it can provide intermediate players with detailed information such as green conditions and wind direction. Furthermore, the course information acquisition unit can acquire course information including advanced strategic information for advanced players. For example, it can provide advanced players with information such as strategy for specific holes and advice on club selection. In this way, the course information acquisition unit can acquire course information that is appropriate for the player's skill level. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input the player's skill level data into AI, which can then analyze and filter the data.

[0039] The course information acquisition unit can prioritize acquiring highly relevant course information by considering the player's geographical location when acquiring course information. For example, the course information acquisition unit can prioritize acquiring information on courses close to the current location. For example, it can acquire appropriate course information based on the weather at the current location. The course information acquisition unit can also acquire optimal course information based on the wind direction and wind speed at the current location. For example, it can acquire course information best suited for play by considering the wind direction and wind speed at the current location. Furthermore, the course information acquisition unit can also acquire optimal course information based on the temperature and humidity at the current location. For example, it can acquire course information best suited for play by considering the temperature and humidity at the current location. In this way, the course information acquisition unit can acquire optimal course information based on the player's geographical location. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without using AI. For example, the course information acquisition unit can input the player's geographical location data into AI, and the AI ​​can analyze the data to prioritize acquiring highly relevant course information.

[0040] The course information acquisition unit can analyze the player's social media activity and acquire relevant course information when acquiring course information. For example, the course information acquisition unit can prioritize acquiring information on courses that the player has shared on social media. For example, it can acquire relevant course information based on information about courses that the player has shared in the past. The course information acquisition unit can also acquire information on courses played by the player's friends. For example, it can acquire relevant course information based on information about courses shared by the player's friends. Furthermore, the course information acquisition unit can acquire information on courses that are popular on social media. For example, it can acquire relevant course information based on information about courses that are trending on social media. In this way, the course information acquisition unit can acquire the most suitable course information based on the player's social media activity. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input the player's social media data into AI, and the AI ​​can analyze the data to acquire relevant course information.

[0041] The condition information acquisition unit can select the optimal acquisition method by referring to past weather data when acquiring condition information. For example, the condition information acquisition unit can prioritize acquiring particularly important weather information from past weather data. For example, it can focus on acquiring information about weather conditions that the user finds difficult, based on past weather data. The condition information acquisition unit can also acquire the weather information with the greatest impact by referring to past weather data. For example, it can analyze past weather data and acquire the weather information best suited for gameplay. In this way, the condition information acquisition unit can acquire optimal condition information by referring to past weather data. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without using AI. For example, the condition information acquisition unit can input past weather data into AI, and the AI ​​can analyze the data and select the optimal acquisition method.

[0042] The condition information acquisition unit can filter the acquired condition information based on the player's past gameplay results. For example, the condition information acquisition unit can prioritize acquiring weather information that had a particularly significant impact from past gameplay results. For example, it can focus on acquiring information about weather conditions that the user finds difficult, based on past gameplay results. The condition information acquisition unit can also refer to past gameplay results to acquire the weather information with the greatest impact. For example, it can analyze past gameplay results to acquire the optimal weather information for gameplay. This allows the condition information acquisition unit to acquire optimal condition information based on the player's past gameplay results. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without AI. For example, the condition information acquisition unit can input past gameplay result data into AI, which can then analyze and filter the data.

[0043] The condition information acquisition unit can prioritize acquiring highly relevant condition information by considering the player's geographical location when acquiring condition information. For example, the condition information acquisition unit can prioritize acquiring weather information for the current location. For example, it can acquire optimal condition information based on the wind direction and wind speed at the current location. The condition information acquisition unit can also acquire optimal condition information based on the temperature and humidity at the current location. For example, it can acquire condition information that is optimal for gameplay by considering the temperature and humidity at the current location. In this way, the condition information acquisition unit can acquire optimal condition information based on the player's geographical location. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without using AI. For example, the condition information acquisition unit can input the player's geographical location data into AI, and the AI ​​can analyze the data and prioritize acquiring highly relevant condition information.

[0044] The condition information acquisition unit can analyze the player's social media activity and acquire relevant condition information when acquiring condition information. For example, the condition information acquisition unit can prioritize acquiring weather information shared by the player on social media. For example, it can acquire relevant condition information based on weather information previously shared by the player. The condition information acquisition unit can also acquire weather information shared by the player's friends. For example, it can acquire relevant condition information based on weather information shared by the player's friends. Furthermore, the condition information acquisition unit can acquire weather information that is popular on social media. For example, it can acquire relevant condition information based on weather information that is trending on social media. In this way, the condition information acquisition unit can acquire optimal condition information based on the player's social media activity. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without AI. For example, the condition information acquisition unit can input the player's social media data into AI, and the AI ​​can analyze the data and acquire relevant condition information.

[0045] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective algorithm from past learning data. For example, it can adjust the algorithm parameters based on past learning data. The learning unit can also select the optimal learning algorithm by referring to past learning data. For example, it can analyze past learning data and select the learning algorithm best suited for gameplay. In this way, the learning unit can select the optimal learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI, and the AI ​​can analyze the data to optimize the learning algorithm.

[0046] The learning unit can weight the learning data based on the player's skill level during the learning process. For example, the learning unit can prioritize learning data for beginners. For example, it might prioritize basic swing data and score data for beginners. The learning unit can also prioritize learning data for intermediate players. For example, it might prioritize detailed swing data and score data for intermediate players. Furthermore, the learning unit can also prioritize learning data for advanced players. For example, it might prioritize advanced swing data and score data for advanced players. This allows the learning unit to weight the learning data according to the player's skill level. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the player's skill level data into an AI, which can then analyze the data and weight the learning data.

[0047] The learning unit can weight the training data based on the player's past gameplay results during training. For example, the learning unit may prioritize particularly important data from past gameplay results. For instance, it may weight the training data based on past gameplay results. The learning unit can also select the optimal training data by referring to past gameplay results. For example, it may analyze past gameplay results and select the optimal training data for gameplay. This allows the learning unit to weight the training data based on the player's past gameplay results. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past gameplay data into an AI, which can then analyze the data and weight the training data.

[0048] The learning unit can analyze the player's social media activity during training and incorporate relevant data into its learning process. For example, the learning unit can incorporate gameplay results shared by the player on social media into its learning process. For example, it can incorporate relevant data based on gameplay results previously shared by the player. The learning unit can also incorporate gameplay results shared by the player's friends into its learning process. For example, it can incorporate relevant data based on gameplay results shared by the player's friends. Furthermore, the learning unit can incorporate popular gameplay results on social media into its learning process. For example, it can incorporate relevant data based on gameplay results that are trending on social media. In this way, the learning unit can incorporate relevant data based on the player's social media activity into its learning process. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the player's social media data into an AI, which can then analyze the data and incorporate relevant data into its learning process.

[0049] The advice unit can adjust the level of detail in its advice based on the player's skill level. For example, it can provide detailed advice for beginners, such as basic swing corrections and club selection advice. It can also provide specific advice for intermediate players, such as detailed swing corrections and strategic play advice. Furthermore, it can provide advanced advice for advanced players, such as advanced swing corrections and strategic play advice. This allows the advice unit to adjust the level of detail in its advice according to the player's skill level. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the player's skill level data into an AI, which can then analyze the data and adjust the level of detail in its advice.

[0050] The advice unit can customize the content of advice based on the player's past gameplay results. For example, the advice unit can provide particularly important advice based on past gameplay results. For example, it can customize the content of advice based on past gameplay results. The advice unit can also refer to past gameplay results to provide optimal advice. For example, it can analyze past gameplay results and provide advice that is best suited to the current game. In this way, the advice unit can customize the content of advice based on the player's past gameplay results. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past gameplay data into AI, and the AI ​​can analyze the data to customize the content of the advice.

[0051] The advice unit can provide optimal advice by considering the player's geographical location. For example, the advice unit can provide optimal advice based on the current weather and wind direction. For instance, it can advise on the best club selection and swing points for playing, taking into account the current weather and wind direction. The advice unit can also advise on the best club selection based on the current terrain. For example, it can advise on the best club selection for playing, taking into account the current terrain. Furthermore, the advice unit can also advise on swing points based on the current temperature and humidity. For example, it can advise on the best swing points for playing, taking into account the current temperature and humidity. In this way, the advice unit can provide optimal advice based on the player's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the player's geographical location data into AI, which can then analyze the data and provide optimal advice.

[0052] The advice unit can analyze the player's social media activity and provide relevant advice when giving advice. For example, the advice unit can provide advice based on gameplay results shared by the player on social media. For example, it can provide relevant advice based on gameplay results shared by the player in the past. The advice unit can also provide advice based on gameplay results shared by the player's friends. For example, it can provide relevant advice based on gameplay results shared by the player's friends. Furthermore, the advice unit can provide advice based on popular gameplay results on social media. For example, it can provide relevant advice based on gameplay results that are trending on social media. In this way, the advice unit can provide relevant advice based on the player's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the player's social media data into AI, which can then analyze the data and provide relevant advice.

[0053] The feature consideration unit can analyze the player's past gameplay results to select the optimal feature consideration method when considering features. For example, the feature consideration unit considers particularly important features from past gameplay results. For example, it weights features based on past gameplay results. The feature consideration unit can also select the optimal feature consideration method by referring to past gameplay results. For example, it analyzes past gameplay results and selects the feature consideration method best suited to the current game. This allows the feature consideration unit to select the optimal feature consideration method based on the player's past gameplay results. Some or all of the above processing in the feature consideration unit may be performed using AI, for example, or without AI. For example, the feature consideration unit can input past gameplay result data into AI, which can then analyze the data and select a feature consideration method.

[0054] The feature analysis unit can weight features based on the player's current skill level when considering features. For example, the feature analysis unit can prioritize features suitable for beginners. For example, it might prioritize basic swing features and score data for beginners. The feature analysis unit can also prioritize features suitable for intermediate players. For example, it might prioritize detailed swing features and score data for intermediate players. Furthermore, the feature analysis unit can also prioritize features suitable for advanced players. For example, it might prioritize advanced swing features and score data for advanced players. This allows the feature analysis unit to weight features according to the player's skill level. Some or all of the above processing in the feature analysis unit may be performed using AI, or not. For example, the feature analysis unit can input the player's skill level data into an AI, which can then analyze the data and weight the features.

[0055] The feature analysis unit can select the optimal feature analysis method by considering the player's geographical location information when analyzing features. For example, the feature analysis unit can select the optimal feature analysis method based on the weather and wind direction of the current location. For example, it can select the optimal feature analysis method for gameplay by considering the weather and wind direction of the current location. The feature analysis unit can also select the optimal feature analysis method based on the terrain of the current location. For example, it can select the optimal feature analysis method for gameplay by considering the terrain of the current location. Furthermore, the feature analysis unit can also select the optimal feature analysis method based on the temperature and humidity of the current location. For example, it can select the optimal feature analysis method for gameplay by considering the temperature and humidity of the current location. In this way, the feature analysis unit can select the optimal feature analysis method based on the player's geographical location information. Some or all of the above processing in the feature analysis unit may be performed using AI, for example, or without AI. For example, the feature analysis unit can input the player's geographical location data into AI, and the AI ​​can analyze the data to select the optimal feature analysis method.

[0056] The feature consideration unit can analyze the player's social media activity and consider relevant features when considering features. For example, the feature consideration unit can consider features shared by the player on social media. For example, it can consider relevant features based on features the player has shared in the past. The feature consideration unit can also consider features shared by the player's friends. For example, it can consider relevant features based on features shared by the player's friends. Furthermore, the feature consideration unit can also consider features that are popular on social media. For example, it can consider relevant features based on features that are trending on social media. In this way, the feature consideration unit can consider relevant features based on the player's social media activity. Some or all of the above processing in the feature consideration unit may be performed using AI, for example, or without AI. For example, the feature consideration unit can input the player's social media data into AI, which can analyze the data and consider relevant features.

[0057] The data storage unit can select the optimal storage method by referring to past data when storing data. For example, the data storage unit can prioritize storing particularly important data from past data. For example, it can weight data based on past data. The data storage unit can also select the optimal data storage method by referring to past data. For example, it can analyze past data and select the data storage method best suited for gameplay. In this way, the data storage unit can select the optimal data storage method by referring to past data. Some or all of the above-described processes in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input past data into AI, and the AI ​​can analyze the data and select the optimal storage method.

[0058] The data storage unit can weight data based on the player's past gameplay results when storing data. For example, the data storage unit may prioritize particularly important data from past gameplay results. For example, it may weight data based on past gameplay results. The data storage unit can also store optimal data by referring to past gameplay results. For example, it may analyze past gameplay results and store data that is optimal for gameplay. This allows the data storage unit to weight data based on the player's past gameplay results. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input past gameplay data into an AI, which can then analyze the data and weight it.

[0059] The swing result recording unit can select the optimal recording method by referring to past swing data when recording swing results. For example, the swing result recording unit can prioritize recording particularly important swing results from past swing data. For example, it can weight swing results based on past swing data. The swing result recording unit can also select the optimal swing result recording method by referring to past swing data. For example, it can analyze past swing data and select the optimal swing result recording method for play. In this way, the swing result recording unit can select the optimal swing result recording method by referring to past swing data. Some or all of the above processing in the swing result recording unit may be performed using AI, for example, or without using AI. For example, the swing result recording unit can input past swing data into AI, and the AI ​​can analyze the data and select the optimal recording method.

[0060] The swing result recording unit can weight the swing data based on the player's past play results when recording swing results. For example, the swing result recording unit may prioritize particularly important swing results from past play results. For example, it may weight the swing data based on past play results. The swing result recording unit can also record the optimal swing result by referring to past play results. For example, it may analyze past play results and record the optimal swing result for play. This allows the swing result recording unit to weight the swing data based on the player's past play results. Some or all of the above processing in the swing result recording unit may be performed using AI, for example, or without AI. For example, the swing result recording unit may input past play result data into AI, which can then analyze the data and weight the swing data.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The golf support system can also be equipped with a swing analysis unit that analyzes the player's swing form in 3D. The swing analysis unit uses multiple cameras and sensors to capture the player's swing in 3D and perform detailed analysis. For example, it can analyze the swing trajectory, angle, and speed, and specifically point out the player's swing habits and areas for improvement. The swing analysis unit can also compare current swing data with past swing data to track progress. Furthermore, it can compare current swing data with that of professional golfers to suggest an ideal swing form. This allows players to objectively evaluate their own swing and make effective improvements.

[0063] The golf support system can also be equipped with a health monitoring unit that monitors the player's health status. This unit can monitor the player's vital signs and activity level in real time and assess their health. For example, it can acquire data such as heart rate, blood pressure, and oxygen saturation, and issue alerts if abnormalities are detected. The health monitoring unit can also measure the player's activity level and calories burned, and provide advice to maintain an appropriate level of exercise. Furthermore, it can suggest the timing of breaks and hydration based on the player's health condition. In this way, the golf support system can support the player's health management and enable safe and effective play.

[0064] The golf support system can also include a comparative analysis unit that compares the player's swing data with that of other players. This unit can compare the player's swing data with that of other players and perform a relative evaluation. For example, it can evaluate data such as swing speed, angle, and distance compared to players of the same skill level. Furthermore, the comparative analysis unit can suggest an ideal swing form by comparing the player's data with that of professional golfers. In addition, the comparative analysis unit can track progress by comparing the player's current data with past data. This allows players to objectively evaluate their swing and make effective improvements.

[0065] The golf support system can also be equipped with a real-time analysis unit that analyzes the player's swing data in real time and provides immediate feedback. The real-time analysis unit can capture the player's swing in real time and perform immediate analysis. For example, it can analyze the swing trajectory, speed, angle, etc. in real time and provide immediate feedback to the player. In addition, the real-time analysis unit can update the data each time the player swings and provide advice based on the latest information. Furthermore, the real-time analysis unit can specifically point out areas for improvement in the player's swing and suggest effective practice methods. This allows the player to evaluate their swing in real time and make immediate improvements.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The course information acquisition unit acquires course information. This course information includes detailed information such as the distance of the 18 holes, pin positions, feet, golf course layout, location of obstacles, and green conditions. Step 2: The condition information acquisition unit acquires condition information. Condition information includes, for example, weather, wind speed, wind direction, humidity, and meteorological data. Step 3: The learning unit learns past score information and swing results based on the information acquired by the course information acquisition unit and the condition information acquisition unit. The learning unit analyzes data such as the player's scorecard data, play history, swing speed and angle, and ball distance to understand the player's characteristics and habits. Step 4: The advice team provides advice based on the information learned by the learning team. The advice team will instruct the player on which clubs to use, points to note during the swing, etc., taking into account the conditions on the day and the player's characteristics, and propose the optimal playing strategy for the player. Step 5: The characteristics analysis section provides advice that takes into account the player's characteristics and habits. The characteristics analysis section provides advice that takes into account the player's skill level, play style, physical characteristics, etc., and provides advice that is tailored to the player's individual needs.

[0068] (Example of form 2) The golf support system according to an embodiment of the present invention is a system that supports golf players in achieving better scores. This golf support system acquires course information, acquires condition information, learns past score information and swing results, provides optimal advice, and provides advice that takes into account the player's characteristics and habits. For example, the golf support system acquires course information in advance. Course information includes the distance of 18 holes, pin positions, feet, etc. It also acquires condition information such as weather, wind speed, and wind direction for each course. Next, the AI ​​learns past score information and the results of each individual swing. This allows it to understand the player's characteristics and habits and provide optimal advice. The AI ​​performs course management and provides advice. For example, based on the conditions of the day and the player's characteristics, it instructs which club to use and what precautions to take during the swing. The accuracy of the AI's advice improves with each use. Specifically, when the player arrives at the course, the AI ​​proposes the optimal playing strategy based on information such as the weather, wind direction, and wind speed for that day. For example, on a windy day, it provides advice on club selection and swing that takes the wind into consideration. Furthermore, based on the player's past scores and swing data, the AI ​​provides advice that takes into account the player's habits and characteristics. In addition, the AI ​​records the swing result each time the player takes a shot, accumulating data to be used for future play. As a result, the accuracy of the AI's advice improves with each play, strengthening support for achieving better scores. In this way, an AI app that acts as a golf caddy can support players in achieving better scores and enhance the enjoyment of golf. Thus, a golf support system can help players achieve better scores.

[0069] The golf support system according to this embodiment comprises a course information acquisition unit, a condition information acquisition unit, a learning unit, an advice unit, and a feature consideration unit. The course information acquisition unit acquires course information. Course information includes, but is not limited to, the distance of 18 holes, pin positions, feet, etc. The course information acquisition unit can acquire detailed information such as the layout of the golf course, the location of obstacles, and the condition of the greens. The condition information acquisition unit acquires condition information. Condition information includes, but is not limited to, the weather, wind speed, wind direction, humidity, etc. The condition information acquisition unit can, for example, acquire weather data in real time and provide it to the player. The learning unit learns past score information and swing results. The learning unit can, for example, analyze data from the player's scorecard and play history to understand the player's characteristics and habits. The learning unit can also use AI to learn data such as the player's swing speed and angle, and the distance the ball travels. The advice unit provides advice based on the information learned by the learning unit. The advice unit can, for example, instruct the player on which club to use and provide instructions on swing techniques, based on the player's condition and characteristics on the day. The advice unit can also use AI to suggest the optimal playing strategy for the player. The characteristics consideration unit provides advice that takes into account the player's characteristics and habits. For example, the characteristics consideration unit can provide advice considering the player's skill level, playing style, and physical characteristics. The characteristics consideration unit can also use AI to provide advice tailored to the player's individual needs. As a result, the golf support system according to this embodiment can support the player in achieving better scores.

[0070] The system includes a data storage unit. The data storage unit stores data. This data includes, but is not limited to, score data, swing data, and condition data. For example, the data storage unit can store data from a player's scorecard and play history. It can also store data such as the player's swing speed and angle, and the distance the ball travels. Furthermore, the data storage unit can store weather data and course condition data. This allows the data storage unit to store data that can be used to improve future play. Some or all of the above-described processing in the data storage unit may be performed using, for example, AI, or not using AI. For example, the data storage unit can input data from a player's scorecard into an AI, which can then analyze and store the data.

[0071] The system includes a swing result recording unit. The swing result recording unit records the swing results. These results include, but are not limited to, swing speed, angle, and ball distance. For example, the swing result recording unit can measure the player's swing speed and angle using sensors and record them as data. It can also measure the ball distance and record it as data. Furthermore, the swing result recording unit can record data such as the player's swing form and the amount of force applied. This allows the swing result recording unit to record swing results that can be used to improve future play. Some or all of the above processing in the swing result recording unit may be performed using, for example, AI, or without AI. For example, the swing result recording unit can input the player's swing data into an AI, which can then analyze and record the data.

[0072] The course information acquisition unit can acquire course information such as distance, pin position, and feet for 18 holes. For example, the course information acquisition unit can measure the distance of 18 holes and acquire it as data. For example, it can measure the distance from the tee to the green for each hole. The course information acquisition unit can also acquire pin positions. For example, it can measure the exact position of the pin on the green. Furthermore, the course information acquisition unit can acquire information in feet. For example, it can acquire data using feet as the unit of distance. This allows the course information acquisition unit to acquire detailed course information. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input course layout data into AI, and the AI ​​can analyze the data and acquire it.

[0073] The condition information acquisition unit can acquire condition information such as weather, wind speed, and wind direction for each course. The condition information acquisition unit can acquire weather data, for example, data such as temperature, humidity, and precipitation. The condition information acquisition unit can also measure wind speed and acquire it as data, for example, by using an anemometer. Furthermore, the condition information acquisition unit can also measure wind direction and acquire it as data, for example, by using a wind vane. This allows the condition information acquisition unit to acquire detailed condition information. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without using AI. For example, the condition information acquisition unit can input meteorological data into an AI, and the AI ​​can analyze the data and acquire the information.

[0074] The learning unit can learn past score information and the results of each swing. For example, the learning unit can learn data from the player's scorecard. For example, it can analyze the data from the player's scorecard to understand the player's characteristics and habits. The learning unit can also learn data such as the player's swing speed and angle, and the distance the ball travels. For example, it can analyze the data from the player's swing to understand the characteristics of the player's swing. Furthermore, the learning unit can learn the player's play history. For example, it can analyze the player's past play history to understand the player's playing style. As a result, the learning unit can provide the player with optimal advice by learning from past data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data from the player's scorecard into an AI, which can then analyze and learn from the data.

[0075] The advice system can instruct players on which clubs to use and provide swing tips, taking into account the day's conditions and the player's characteristics. For example, the advice system can instruct players on which clubs to use based on the day's weather data. For instance, on a windy day, it can instruct players to choose clubs that take the wind into consideration. The advice system can also provide swing tips based on the player's characteristics. For example, it can instruct players on how to correct their swing, taking into account their swing habits. Furthermore, the advice system can instruct players on strategic play methods, taking into account their skill level. For example, it can instruct beginners on simple strategies, intermediate players on detailed strategies, and advanced players on sophisticated strategies. In this way, the advice system can support players in improving their scores by providing them with the most suitable advice. Some or all of the above processes in the advice system may be performed using AI, or not. For example, the advice system can input data from the player's scorecard into an AI, which can then analyze the data and provide advice.

[0076] The feature-aware unit can provide advice that takes into account the player's characteristics. For example, it can provide advice considering the player's skill level. For instance, it can provide basic advice to beginners, more detailed advice to intermediate players, and advanced advice to advanced players. The feature-aware unit can also provide advice considering the player's playing style. For example, it can suggest risky strategies to aggressive players and safe strategies to defensive players. Furthermore, the feature-aware unit can provide advice considering the player's physical characteristics. For example, it can instruct the player on the optimal swing form and club selection based on physical characteristics such as height, weight, and muscle strength. In this way, the feature-aware unit can support score improvement by providing advice based on the player's individual characteristics. Some or all of the above processing in the feature-aware unit may be performed using AI, for example, or not. For example, the feature-aware unit can input data from the player's scorecard into an AI, which can then analyze the data and provide advice.

[0077] The course information acquisition unit can estimate the user's emotions and adjust the timing of course information acquisition based on the estimated emotions. For example, the course information acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the course information can be acquired with ample time before the start of play. If the user is nervous, the course information can be acquired immediately before play to provide the latest information. Furthermore, if the user is in a hurry, only the most important course information can be prioritized and acquired. In this way, the course information acquisition unit can acquire course information at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input user facial expression data into an AI, the AI ​​can analyze the data to estimate emotions, and adjust the timing of course information acquisition.

[0078] The course information acquisition unit can select the optimal acquisition method by referring to past play data when acquiring course information. For example, the course information acquisition unit can prioritize acquiring information on courses that have been played in the past. For example, it can acquire detailed information on particularly important holes from past play data. The course information acquisition unit can also prioritize acquiring information on holes that the user struggles with, based on past play data. For example, it can analyze past play data and prioritize acquiring information on holes where the user tends to score poorly. In this way, the course information acquisition unit can acquire optimal course information by referring to past play data. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without using AI. For example, the course information acquisition unit can input past play data into AI, and the AI ​​can analyze the data and select the optimal acquisition method.

[0079] The course information acquisition unit can filter course information based on the player's current skill level. For example, it can prioritize acquiring simple course information for beginners. For example, it can provide beginners with information such as basic course layout and obstacle locations. The course information acquisition unit can also acquire detailed course information for intermediate players. For example, it can provide intermediate players with detailed information such as green conditions and wind direction. Furthermore, the course information acquisition unit can acquire course information including advanced strategic information for advanced players. For example, it can provide advanced players with information such as strategy for specific holes and advice on club selection. In this way, the course information acquisition unit can acquire course information that is appropriate for the player's skill level. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input the player's skill level data into AI, which can then analyze and filter the data.

[0080] The course information acquisition unit can estimate the user's emotions and determine the priority of course information to acquire based on the estimated user emotions. For example, the course information acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the unit can prioritize acquiring overall course information. If the user is nervous, it can prioritize acquiring information on particularly difficult holes. Furthermore, if the user is in a hurry, it can prioritize acquiring information on the most important holes. In this way, the course information acquisition unit can prioritize acquiring the most suitable course information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the course information acquisition unit may be performed using AI, or not using AI. For example, the course information acquisition unit can input user facial expression data into an AI, which can analyze the data to estimate emotions and determine the priority of course information.

[0081] The course information acquisition unit can prioritize acquiring highly relevant course information by considering the player's geographical location when acquiring course information. For example, the course information acquisition unit can prioritize acquiring information on courses close to the current location. For example, it can acquire appropriate course information based on the weather at the current location. The course information acquisition unit can also acquire optimal course information based on the wind direction and wind speed at the current location. For example, it can acquire course information best suited for play by considering the wind direction and wind speed at the current location. Furthermore, the course information acquisition unit can also acquire optimal course information based on the temperature and humidity at the current location. For example, it can acquire course information best suited for play by considering the temperature and humidity at the current location. In this way, the course information acquisition unit can acquire optimal course information based on the player's geographical location. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without using AI. For example, the course information acquisition unit can input the player's geographical location data into AI, and the AI ​​can analyze the data to prioritize acquiring highly relevant course information.

[0082] The course information acquisition unit can analyze the player's social media activity and acquire relevant course information when acquiring course information. For example, the course information acquisition unit can prioritize acquiring information on courses that the player has shared on social media. For example, it can acquire relevant course information based on information about courses that the player has shared in the past. The course information acquisition unit can also acquire information on courses played by the player's friends. For example, it can acquire relevant course information based on information about courses shared by the player's friends. Furthermore, the course information acquisition unit can acquire information on courses that are popular on social media. For example, it can acquire relevant course information based on information about courses that are trending on social media. In this way, the course information acquisition unit can acquire the most suitable course information based on the player's social media activity. Some or all of the above processing in the course information acquisition unit may be performed using AI, for example, or without AI. For example, the course information acquisition unit can input the player's social media data into AI, and the AI ​​can analyze the data to acquire relevant course information.

[0083] The condition information acquisition unit can estimate the user's emotions and adjust the timing of acquiring condition information based on the estimated emotions. For example, the condition information acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the condition information can be acquired well in advance of the start of play. If the user is nervous, the condition information can be acquired immediately before play to provide the latest information. Furthermore, if the user is in a hurry, only the most important condition information can be prioritized and acquired. In this way, the condition information acquisition unit can acquire condition information at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without AI. For example, the condition information acquisition unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and adjust the timing of acquiring condition information.

[0084] The condition information acquisition unit can select the optimal acquisition method by referring to past weather data when acquiring condition information. For example, the condition information acquisition unit can prioritize acquiring particularly important weather information from past weather data. For example, it can focus on acquiring information about weather conditions that the user finds difficult, based on past weather data. The condition information acquisition unit can also acquire the weather information with the greatest impact by referring to past weather data. For example, it can analyze past weather data and acquire the weather information best suited for gameplay. In this way, the condition information acquisition unit can acquire optimal condition information by referring to past weather data. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without using AI. For example, the condition information acquisition unit can input past weather data into AI, and the AI ​​can analyze the data and select the optimal acquisition method.

[0085] The condition information acquisition unit can filter the acquired condition information based on the player's past gameplay results. For example, the condition information acquisition unit can prioritize acquiring weather information that had a particularly significant impact from past gameplay results. For example, it can focus on acquiring information about weather conditions that the user finds difficult, based on past gameplay results. The condition information acquisition unit can also refer to past gameplay results to acquire the weather information with the greatest impact. For example, it can analyze past gameplay results to acquire the optimal weather information for gameplay. This allows the condition information acquisition unit to acquire optimal condition information based on the player's past gameplay results. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without AI. For example, the condition information acquisition unit can input past gameplay result data into AI, which can then analyze and filter the data.

[0086] The condition information acquisition unit can estimate the user's emotions and determine the priority of condition information to acquire based on the estimated user emotions. For example, the condition information acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the unit can prioritize acquiring overall condition information. If the user is stressed, it can prioritize acquiring weather information, which has a particularly significant impact. Furthermore, if the user is in a hurry, it can prioritize acquiring only the most important condition information. In this way, the condition information acquisition unit can prioritize acquiring the most appropriate condition information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without AI. For example, the condition information acquisition unit can input user facial expression data into an AI, which can analyze the data to estimate emotions and determine the priority of condition information.

[0087] The condition information acquisition unit can prioritize acquiring highly relevant condition information by considering the player's geographical location when acquiring condition information. For example, the condition information acquisition unit can prioritize acquiring weather information for the current location. For example, it can acquire optimal condition information based on the wind direction and wind speed at the current location. The condition information acquisition unit can also acquire optimal condition information based on the temperature and humidity at the current location. For example, it can acquire condition information that is optimal for gameplay by considering the temperature and humidity at the current location. In this way, the condition information acquisition unit can acquire optimal condition information based on the player's geographical location. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without using AI. For example, the condition information acquisition unit can input the player's geographical location data into AI, and the AI ​​can analyze the data and prioritize acquiring highly relevant condition information.

[0088] The condition information acquisition unit can analyze the player's social media activity and acquire relevant condition information when acquiring condition information. For example, the condition information acquisition unit can prioritize acquiring weather information shared by the player on social media. For example, it can acquire relevant condition information based on weather information previously shared by the player. The condition information acquisition unit can also acquire weather information shared by the player's friends. For example, it can acquire relevant condition information based on weather information shared by the player's friends. Furthermore, the condition information acquisition unit can acquire weather information that is popular on social media. For example, it can acquire relevant condition information based on weather information that is trending on social media. In this way, the condition information acquisition unit can acquire optimal condition information based on the player's social media activity. Some or all of the above processing in the condition information acquisition unit may be performed using AI, for example, or without AI. For example, the condition information acquisition unit can input the player's social media data into AI, and the AI ​​can analyze the data and acquire relevant condition information.

[0089] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, it can select detailed training data. If the user is tense, it can select training data that focuses on the essentials. Furthermore, if the user is in a hurry, it can select only the most important training data. In this way, the learning unit can select the optimal training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and select training data.

[0090] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective algorithm from past learning data. For example, it can adjust the algorithm parameters based on past learning data. The learning unit can also select the optimal learning algorithm by referring to past learning data. For example, it can analyze past learning data and select the learning algorithm best suited for gameplay. In this way, the learning unit can select the optimal learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI, and the AI ​​can analyze the data to optimize the learning algorithm.

[0091] The learning unit can weight the learning data based on the player's skill level during the learning process. For example, the learning unit can prioritize learning data for beginners. For example, it might prioritize basic swing data and score data for beginners. The learning unit can also prioritize learning data for intermediate players. For example, it might prioritize detailed swing data and score data for intermediate players. Furthermore, the learning unit can also prioritize learning data for advanced players. For example, it might prioritize advanced swing data and score data for advanced players. This allows the learning unit to weight the learning data according to the player's skill level. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the player's skill level data into an AI, which can then analyze the data and weight the learning data.

[0092] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the learning frequency can be increased. Conversely, if the user is tense, the learning frequency can be decreased. Furthermore, if the user is in a hurry, the learning frequency can be minimized. In this way, the learning unit can adjust the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user facial expression data into an AI, which can analyze the data to estimate emotions and adjust the learning frequency.

[0093] The learning unit can weight the training data based on the player's past gameplay results during training. For example, the learning unit may prioritize particularly important data from past gameplay results. For instance, it may weight the training data based on past gameplay results. The learning unit can also select the optimal training data by referring to past gameplay results. For example, it may analyze past gameplay results and select the optimal training data for gameplay. This allows the learning unit to weight the training data based on the player's past gameplay results. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past gameplay data into an AI, which can then analyze the data and weight the training data.

[0094] The learning unit can analyze the player's social media activity during training and incorporate relevant data into its learning process. For example, the learning unit can incorporate gameplay results shared by the player on social media into its learning process. For example, it can incorporate relevant data based on gameplay results previously shared by the player. The learning unit can also incorporate gameplay results shared by the player's friends into its learning process. For example, it can incorporate relevant data based on gameplay results shared by the player's friends. Furthermore, the learning unit can incorporate popular gameplay results on social media into its learning process. For example, it can incorporate relevant data based on gameplay results that are trending on social media. In this way, the learning unit can incorporate relevant data based on the player's social media activity into its learning process. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the player's social media data into an AI, which can then analyze the data and incorporate relevant data into its learning process.

[0095] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, the advice unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, it can provide detailed advice. If the user is tense, it can provide concise advice that gets straight to the point. Furthermore, if the user is in a hurry, it can provide only the most important advice. In this way, the advice unit can adjust the way it expresses advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, or not using AI. For example, the advice unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and adjust the way it expresses advice.

[0096] The advice unit can adjust the level of detail in its advice based on the player's skill level. For example, it can provide detailed advice for beginners, such as basic swing corrections and club selection advice. It can also provide specific advice for intermediate players, such as detailed swing corrections and strategic play advice. Furthermore, it can provide advanced advice for advanced players, such as advanced swing corrections and strategic play advice. This allows the advice unit to adjust the level of detail in its advice according to the player's skill level. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the player's skill level data into an AI, which can then analyze the data and adjust the level of detail in its advice.

[0097] The advice unit can customize the content of advice based on the player's past gameplay results. For example, the advice unit can provide particularly important advice based on past gameplay results. For example, it can customize the content of advice based on past gameplay results. The advice unit can also refer to past gameplay results to provide optimal advice. For example, it can analyze past gameplay results and provide advice that is best suited to the current game. In this way, the advice unit can customize the content of advice based on the player's past gameplay results. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past gameplay data into AI, and the AI ​​can analyze the data to customize the content of the advice.

[0098] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, the advice unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, it can prioritize providing general advice. If the user is tense, it can prioritize providing particularly important advice. Furthermore, if the user is in a hurry, it can prioritize providing only the most important advice. In this way, the advice unit can determine the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and determine the priority of advice.

[0099] The advice unit can provide optimal advice by considering the player's geographical location. For example, the advice unit can provide optimal advice based on the current weather and wind direction. For instance, it can advise on the best club selection and swing points for playing, taking into account the current weather and wind direction. The advice unit can also advise on the best club selection based on the current terrain. For example, it can advise on the best club selection for playing, taking into account the current terrain. Furthermore, the advice unit can also advise on swing points based on the current temperature and humidity. For example, it can advise on the best swing points for playing, taking into account the current temperature and humidity. In this way, the advice unit can provide optimal advice based on the player's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the player's geographical location data into AI, which can then analyze the data and provide optimal advice.

[0100] The advice unit can analyze the player's social media activity and provide relevant advice when giving advice. For example, the advice unit can provide advice based on gameplay results shared by the player on social media. For example, it can provide relevant advice based on gameplay results shared by the player in the past. The advice unit can also provide advice based on gameplay results shared by the player's friends. For example, it can provide relevant advice based on gameplay results shared by the player's friends. Furthermore, the advice unit can provide advice based on popular gameplay results on social media. For example, it can provide relevant advice based on gameplay results that are trending on social media. In this way, the advice unit can provide relevant advice based on the player's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the player's social media data into AI, which can then analyze the data and provide relevant advice.

[0101] The feature consideration unit can estimate the user's emotions and adjust the feature consideration method based on the estimated user emotions. For example, the feature consideration unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, detailed features can be considered. If the user is tense, key features can be considered. Furthermore, if the user is in a hurry, only the most important features can be considered. In this way, the feature consideration unit can adjust the feature consideration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feature consideration unit may be performed using AI, or not using AI. For example, the feature consideration unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and adjust the feature consideration method.

[0102] The feature consideration unit can analyze the player's past gameplay results to select the optimal feature consideration method when considering features. For example, the feature consideration unit considers particularly important features from past gameplay results. For example, it weights features based on past gameplay results. The feature consideration unit can also select the optimal feature consideration method by referring to past gameplay results. For example, it analyzes past gameplay results and selects the feature consideration method best suited to the current game. This allows the feature consideration unit to select the optimal feature consideration method based on the player's past gameplay results. Some or all of the above processing in the feature consideration unit may be performed using AI, for example, or without AI. For example, the feature consideration unit can input past gameplay result data into AI, which can then analyze the data and select a feature consideration method.

[0103] The feature analysis unit can weight features based on the player's current skill level when considering features. For example, the feature analysis unit can prioritize features suitable for beginners. For example, it might prioritize basic swing features and score data for beginners. The feature analysis unit can also prioritize features suitable for intermediate players. For example, it might prioritize detailed swing features and score data for intermediate players. Furthermore, the feature analysis unit can also prioritize features suitable for advanced players. For example, it might prioritize advanced swing features and score data for advanced players. This allows the feature analysis unit to weight features according to the player's skill level. Some or all of the above processing in the feature analysis unit may be performed using AI, or not. For example, the feature analysis unit can input the player's skill level data into an AI, which can then analyze the data and weight the features.

[0104] The feature consideration unit can estimate the user's emotions and determine the priority of features based on the estimated user emotions. For example, the feature consideration unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the overall features are given priority. If the user is tense, particularly important features can be given priority. Furthermore, if the user is in a hurry, only the most important features can be given priority. In this way, the feature consideration unit can determine the priority of features according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feature consideration unit may be performed using AI, or not using AI. For example, the feature consideration unit can input user facial data into an AI, which can analyze the data to estimate emotions and determine the priority of features.

[0105] The feature analysis unit can select the optimal feature analysis method by considering the player's geographical location information when analyzing features. For example, the feature analysis unit can select the optimal feature analysis method based on the weather and wind direction of the current location. For example, it can select the optimal feature analysis method for gameplay by considering the weather and wind direction of the current location. The feature analysis unit can also select the optimal feature analysis method based on the terrain of the current location. For example, it can select the optimal feature analysis method for gameplay by considering the terrain of the current location. Furthermore, the feature analysis unit can also select the optimal feature analysis method based on the temperature and humidity of the current location. For example, it can select the optimal feature analysis method for gameplay by considering the temperature and humidity of the current location. In this way, the feature analysis unit can select the optimal feature analysis method based on the player's geographical location information. Some or all of the above processing in the feature analysis unit may be performed using AI, for example, or without AI. For example, the feature analysis unit can input the player's geographical location data into AI, and the AI ​​can analyze the data to select the optimal feature analysis method.

[0106] The feature consideration unit can analyze the player's social media activity and consider relevant features when considering features. For example, the feature consideration unit can consider features shared by the player on social media. For example, it can consider relevant features based on features the player has shared in the past. The feature consideration unit can also consider features shared by the player's friends. For example, it can consider relevant features based on features shared by the player's friends. Furthermore, the feature consideration unit can also consider features that are popular on social media. For example, it can consider relevant features based on features that are trending on social media. In this way, the feature consideration unit can consider relevant features based on the player's social media activity. Some or all of the above processing in the feature consideration unit may be performed using AI, for example, or without AI. For example, the feature consideration unit can input the player's social media data into AI, which can analyze the data and consider relevant features.

[0107] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated emotions. For example, the data storage unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, detailed data can be stored. If the user is tense, concise data can be stored. Furthermore, if the user is in a hurry, only the most important data can be stored. In this way, the data storage unit can adjust the data storage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI or not using AI. For example, the data storage unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and adjust the data storage method.

[0108] The data storage unit can select the optimal storage method by referring to past data when storing data. For example, the data storage unit can prioritize storing particularly important data from past data. For example, it can weight data based on past data. The data storage unit can also select the optimal data storage method by referring to past data. For example, it can analyze past data and select the data storage method best suited for gameplay. In this way, the data storage unit can select the optimal data storage method by referring to past data. Some or all of the above-described processes in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input past data into AI, and the AI ​​can analyze the data and select the optimal storage method.

[0109] The data storage unit can estimate the user's emotions and adjust the data storage frequency based on the estimated emotions. For example, the data storage unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the data storage frequency can be increased. Conversely, if the user is tense, the data storage frequency can be decreased. Furthermore, if the user is in a hurry, the data storage frequency can be minimized. In this way, the data storage unit can adjust the data storage frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI, or not using AI. For example, the data storage unit can input user facial expression data into an AI, which can analyze the data to estimate emotions and adjust the data storage frequency.

[0110] The data storage unit can weight data based on the player's past gameplay results when storing data. For example, the data storage unit may prioritize particularly important data from past gameplay results. For example, it may weight data based on past gameplay results. The data storage unit can also store optimal data by referring to past gameplay results. For example, it may analyze past gameplay results and store data that is optimal for gameplay. This allows the data storage unit to weight data based on the player's past gameplay results. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input past gameplay data into an AI, which can then analyze the data and weight it.

[0111] The swing result recording unit can estimate the user's emotions and adjust the method of recording the swing results based on the estimated emotions. For example, the swing result recording unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, it can record detailed swing results. If the user is tense, it can record concise swing results. Furthermore, if the user is in a hurry, it can record only the most important swing results. In this way, the swing result recording unit can adjust the method of recording the swing results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the swing result recording unit may be performed using AI, for example, or without AI. For example, the swing result recording unit can input the user's facial expression data into an AI, which can analyze the data to estimate emotions and adjust the method of recording the swing results.

[0112] The swing result recording unit can select the optimal recording method by referring to past swing data when recording swing results. For example, the swing result recording unit can prioritize recording particularly important swing results from past swing data. For example, it can weight swing results based on past swing data. The swing result recording unit can also select the optimal swing result recording method by referring to past swing data. For example, it can analyze past swing data and select the optimal swing result recording method for play. In this way, the swing result recording unit can select the optimal swing result recording method by referring to past swing data. Some or all of the above processing in the swing result recording unit may be performed using AI, for example, or without using AI. For example, the swing result recording unit can input past swing data into AI, and the AI ​​can analyze the data and select the optimal recording method.

[0113] The swing result recording unit can estimate the user's emotions and adjust the frequency of recording swing results based on the estimated emotions. For example, the swing result recording unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the user is relaxed, the frequency of recording swing results can be increased. Conversely, if the user is tense, the frequency of recording swing results can be decreased. Furthermore, if the user is in a hurry, the frequency of recording swing results can be minimized. In this way, the swing result recording unit can adjust the frequency of recording swing results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the swing result recording unit may be performed using AI, for example, or without AI. For example, the swing result recording unit can input user facial expression data into an AI, which can analyze the data to estimate emotions and adjust the frequency of recording swing results.

[0114] The swing result recording unit can weight the swing data based on the player's past play results when recording swing results. For example, the swing result recording unit may prioritize particularly important swing results from past play results. For example, it may weight the swing data based on past play results. The swing result recording unit can also record the optimal swing result by referring to past play results. For example, it may analyze past play results and record the optimal swing result for play. This allows the swing result recording unit to weight the swing data based on the player's past play results. Some or all of the above processing in the swing result recording unit may be performed using AI, for example, or without AI. For example, the swing result recording unit may input past play result data into AI, which can then analyze the data and weight the swing data. === Hard Collateral 1-1 === Each of the multiple elements described above, including the course information acquisition unit, condition information acquisition unit, learning unit, advice unit, feature consideration unit, data storage unit, and swing result recording unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the course information acquisition unit acquires course information using the camera 42 and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The condition information acquisition unit acquires condition information using the sensors and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The learning unit learns past score information and swing results using the specific processing unit 290 of the data processing unit 12. The advice unit provides optimal advice using the specific processing unit 290 of the data processing unit 12. The feature consideration unit provides advice that takes into account the player's characteristics and habits using the specific processing unit 290 of the data processing unit 12. The data storage unit stores data in the storage 32 of the data processing unit 12. The swing result recording unit records the swing results using the sensors and camera 42 of the smart device 14, and the results are analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the course information acquisition unit, condition information acquisition unit, learning unit, advice unit, feature consideration unit, data storage unit, and swing result recording unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the course information acquisition unit acquires course information using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The condition information acquisition unit acquires condition information using the sensors and communication I / F 44 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The learning unit learns past score information and swing results using the specific processing unit 290 of the data processing unit 12. The advice unit provides optimal advice using the specific processing unit 290 of the data processing unit 12. The feature consideration unit provides advice that takes into account the player's characteristics and habits using the specific processing unit 290 of the data processing unit 12. The data storage unit stores data in the storage 32 of the data processing unit 12. The swing result recording unit records the swing results using the sensors and camera 42 of the smart glasses 214, and the results are analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the course information acquisition unit, condition information acquisition unit, learning unit, advice unit, feature consideration unit, data storage unit, and swing result recording unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the course information acquisition unit acquires course information using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The condition information acquisition unit acquires condition information using the sensors and communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The learning unit learns past score information and swing results using the specific processing unit 290 of the data processing unit 12. The advice unit provides optimal advice using the specific processing unit 290 of the data processing unit 12. The feature consideration unit provides advice that takes into account the player's characteristics and habits using the specific processing unit 290 of the data processing unit 12. The data storage unit stores data in the storage 32 of the data processing unit 12. The swing result recording unit records the swing results using the sensors and camera 42 of the headset terminal 314, and the results are analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the course information acquisition unit, condition information acquisition unit, learning unit, advice unit, feature consideration unit, data storage unit, and swing result recording unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the course information acquisition unit acquires course information using the camera 42 and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The condition information acquisition unit acquires condition information using the sensors and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The learning unit learns past score information and swing results using the specific processing unit 290 of the data processing unit 12. The advice unit provides optimal advice using the specific processing unit 290 of the data processing unit 12. The feature consideration unit provides advice that takes into account the player's characteristics and habits using the specific processing unit 290 of the data processing unit 12. The data storage unit stores data in the storage 32 of the data processing unit 12. The swing result recording unit records the swing results using the sensors and camera 42 of the robot 414, and the results are analyzed by the specific processing unit 290 of the data processing device 12.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The golf support system can also be equipped with a biometrics acquisition unit that obtains real-time biometric data. This unit acquires data such as the player's heart rate, body temperature, and sweating in real time, and based on this data, it can understand the player's physical condition and stress level. For example, if the heart rate suddenly increases, it can determine that the player is tense and provide advice on how to relax. If the body temperature is elevated, it can suggest taking a break, considering the risk of heatstroke. Furthermore, if the player is sweating heavily, it can advise on hydration. In this way, the golf support system can help manage the player's physical condition and provide a safer and more comfortable playing environment.

[0117] The golf support system can also be equipped with a swing analysis unit that analyzes the player's swing form in 3D. The swing analysis unit uses multiple cameras and sensors to capture the player's swing in 3D and perform detailed analysis. For example, it can analyze the swing trajectory, angle, and speed, and specifically point out the player's swing habits and areas for improvement. The swing analysis unit can also compare current swing data with past swing data to track progress. Furthermore, it can compare current swing data with that of professional golfers to suggest an ideal swing form. This allows players to objectively evaluate their own swing and make effective improvements.

[0118] The golf support system can also be equipped with a psychological assessment unit that evaluates the player's psychological state. This unit analyzes the player's facial expressions, tone of voice, and speech patterns to assess their state. For example, if the player is anxious, it can provide advice to help them calm down. If the player is confident, it can provide positive feedback to help them maintain that confidence. Furthermore, if the player is feeling down, it can offer words of encouragement to boost their motivation. In this way, the golf support system can support the player's psychological state and help them achieve better performance.

[0119] The golf support system can also include a feedback collection unit to gather player feedback. This unit can provide an interface for players to input their feelings and suggestions for improvement after playing. For example, it can collect feedback on the difficulty players experienced on specific holes or the usefulness of advice. The feedback collection unit can also provide questionnaires to assess players' emotions and satisfaction levels. Furthermore, the feedback collection unit can improve the system based on the collected feedback. This allows the golf support system to provide services tailored to players' needs and deliver a better user experience.

[0120] The golf support system can also be equipped with a health monitoring unit that monitors the player's health status. This unit can monitor the player's vital signs and activity level in real time and assess their health. For example, it can acquire data such as heart rate, blood pressure, and oxygen saturation, and issue alerts if abnormalities are detected. The health monitoring unit can also measure the player's activity level and calories burned, and provide advice to maintain an appropriate level of exercise. Furthermore, it can suggest the timing of breaks and hydration based on the player's health condition. In this way, the golf support system can support the player's health management and enable safe and effective play.

[0121] The golf support system may also include a music provider that estimates the player's emotions and provides a playlist based on those emotions. The music provider can analyze the player's emotions and select and provide music that matches those emotions. For example, if the player is relaxed, it can provide calming music to maintain that relaxation. If the player is tense, it can provide relaxing music to alleviate that tension. Furthermore, if the player is focused, it can provide upbeat music to enhance their concentration. This allows the golf support system to provide music tailored to the player's emotions, thereby improving the quality of their play.

[0122] The golf support system can also include a comparative analysis unit that compares the player's swing data with that of other players. This unit can compare the player's swing data with that of other players and perform a relative evaluation. For example, it can evaluate data such as swing speed, angle, and distance compared to players of the same skill level. Furthermore, the comparative analysis unit can suggest an ideal swing form by comparing the player's data with that of professional golfers. In addition, the comparative analysis unit can track progress by comparing the player's current data with past data. This allows players to objectively evaluate their swing and make effective improvements.

[0123] The golf support system can also include a timing adjustment unit that estimates the player's emotions and adjusts the timing of advice based on those emotions. The timing adjustment unit can analyze the player's emotions and provide advice at the optimal timing according to those emotions. For example, if the player is relaxed, advice can be provided with ample time during play. If the player is nervous, advice can be provided before play to alleviate tension. Furthermore, if the player is in a hurry, only the most important advice can be provided quickly. In this way, the golf support system can provide advice at the right time according to the player's emotions, thereby improving the quality of play.

[0124] The golf support system can also be equipped with a real-time analysis unit that analyzes the player's swing data in real time and provides immediate feedback. The real-time analysis unit can capture the player's swing in real time and perform immediate analysis. For example, it can analyze the swing trajectory, speed, angle, etc. in real time and provide immediate feedback to the player. In addition, the real-time analysis unit can update the data each time the player swings and provide advice based on the latest information. Furthermore, the real-time analysis unit can specifically point out areas for improvement in the player's swing and suggest effective practice methods. This allows the player to evaluate their swing in real time and make immediate improvements.

[0125] The golf support system may also include a difficulty adjustment unit that estimates the player's emotions and adjusts the difficulty of the game based on those emotions. The difficulty adjustment unit analyzes the player's emotions and adjusts the difficulty accordingly. For example, if the player is relaxed, the game will be played at a normal difficulty level. If the player is tense, the difficulty level will be lowered to alleviate their tension. Furthermore, if the player is focused, the difficulty level can be increased to provide a more challenging game. This allows the golf support system to provide a game at a difficulty level that matches the player's emotions, thereby improving the quality of play.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The course information acquisition unit acquires course information. This course information includes detailed information such as the distance of the 18 holes, pin positions, feet, golf course layout, location of obstacles, and green conditions. Step 2: The condition information acquisition unit acquires condition information. Condition information includes, for example, weather, wind speed, wind direction, humidity, and meteorological data. Step 3: The learning unit learns past score information and swing results based on the information acquired by the course information acquisition unit and the condition information acquisition unit. The learning unit analyzes data such as the player's scorecard data, play history, swing speed and angle, and ball distance to understand the player's characteristics and habits. Step 4: The advice team provides advice based on the information learned by the learning team. The advice team will instruct the player on which clubs to use, points to note during the swing, etc., taking into account the conditions on the day and the player's characteristics, and propose the optimal playing strategy for the player. Step 5: The characteristics analysis section provides advice that takes into account the player's characteristics and habits. The characteristics analysis section provides advice that takes into account the player's skill level, play style, physical characteristics, etc., and provides advice that is tailored to the player's individual needs.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A course information acquisition unit that acquires course information, A condition information acquisition unit that acquires condition information, A learning unit that learns past score information and swing results based on the information acquired by the course information acquisition unit and the condition information acquisition unit, An advice unit provides advice based on the information learned by the learning unit, It includes a feature consideration unit that provides advice taking into account the player's characteristics. A system characterized by the following features.

2. It includes a data storage unit for accumulating data. The system according to feature 1.

3. It is equipped with a swing result recording unit that records the results of the swing. The system according to feature 1.

4. The aforementioned course information acquisition unit, Get course information for 18 holes, including distance, pin position, and feet. The system according to feature 1.

5. The condition information acquisition unit, Obtain weather conditions and wind speed and direction information for each course. The system according to feature 1.

6. The aforementioned learning unit, Learns from past score information and individual swing results. The system according to feature 1.

7. The aforementioned advice section, Based on the conditions on the day and the player's characteristics, instruct them on which club to use and what precautions to take during the swing. The system according to feature 1.

8. The aforementioned feature consideration unit is Provides advice that takes into account the player's characteristics. The system according to feature 1.

9. The aforementioned course information acquisition unit, The system estimates the user's emotions and adjusts the timing of course information acquisition based on the estimated emotions. The system according to feature 1.

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