system

Abstract

The system according to this embodiment aims to advise golfers on the optimal club and hitting direction for each situation. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and an output unit. The collection unit collects data such as the average distance and characteristics of each club, golf course data, reviews, the performance of other users, and weather. The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's ability and characteristics, the characteristics of the golf course, the weather, reviews, the performance of others, etc. The output unit outputs the advice generated by the generation unit as text or images.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document l

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information for comprehensively providing a golf player with the optimal club and hitting direction for each situation has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to advise a golf player on the optimal club and hitting direction for each situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and an output unit. The collection unit collects data such as average distance and characteristics for each club, golf course data, reviews, other users' performance, and weather. The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's ability and characteristics, golf course characteristics, weather, reviews, and the performance of others. The output unit outputs the advice generated by the generation unit as text or images. [Effects of the Invention]

[0007] The system according to this embodiment can advise a golf player on the optimal club and direction of the shot for each situation. [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, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 advice system according to an embodiment of the present invention is a system that advises the user on the direction to aim and the club to use in each situation in golf. This golf advice system collects data such as the average distance and characteristics of each club, golf course data, user reviews on the web, the performance of other users using the service, and weather, and a generating AI analyzes this data to output the most appropriate advice for the situation. For example, the golf advice system uses data set by the user as initial values, or data accumulated in apps or watch-type devices. For example, it collects data such as the average distance and characteristics of each club set by the user, the terrain and hole layout of the golf course, reviews and performance of other users, and weather forecasts. Next, the golf advice system uses a generating AI to analyze the collected data. Based on the collected data, the generating AI comprehensively considers the user's ability and characteristics, the characteristics of the golf course, the weather, reviews, and the performance of others to generate the most appropriate advice for the situation. For example, it analyzes which club the user should use on a particular hole, in which direction to hit, and what kind of shot to hit. The generated advice is output as text or images. For example, it is displayed on the user's device (smartphone or watch-type device). This allows users to receive personalized strategies rather than the general strategies displayed on the navigation system in their golf carts. This system enables intermediate golfers to stabilize their scores on the course and hit more accurate shots. For example, for users who can hit consistent shots at the driving range but struggle to maintain consistent scores on the course, the AI-generated advice can provide personalized guidance, leading to improved scores. Furthermore, if the user holds their smartphone horizontally, the advice is displayed horizontally, ensuring safety while walking. As a result, the golf advice system can provide optimal advice by comprehensively considering the user's ability and habits, the characteristics of the golf course, the weather, reviews, and the performance of other players.

[0029] The golf advice system according to the embodiment comprises a collection unit, a generation unit, and an output unit. The collection unit collects data such as the average distance and characteristics of each club, golf course data, reviews, other users' performance, and weather. The collection unit collects data such as data set by the user as initial values, and data accumulated in apps or watch-type devices. The collection unit collects data such as the average distance and characteristics of each club set by the user, the terrain and hole layout of the golf course, reviews and performance of other users, and weather forecasts. The collection unit calculates the average distance based on data of clubs the user has used in the past in order to calculate the average distance for each club. The collection unit collects data such as the frequency of slices and hooks and the consistency of shots in order to analyze the user's characteristics. The collection unit collects data such as course layout, hole difficulty, and green condition in order to collect golf course data. The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's ability and characteristics, the characteristics of the golf course, the weather, reviews, and the performance of others. The generation unit analyzes data such as past score history and club usage statistics to analyze the user's abilities. The generation unit analyzes data such as the frequency of slices and hooks and shot consistency to analyze the user's habits. The generation unit analyzes data such as course layout, hole difficulty, and green condition to analyze the characteristics of a golf course. The generation unit analyzes data such as temperature, wind speed, and precipitation to analyze the weather. The generation unit analyzes data such as user rating scores and comment content to analyze reviews. The generation unit analyzes data such as other users' score history and club usage statistics to analyze the performance of others. The output unit outputs the advice generated by the generation unit as text or images. The output unit displays the generated advice as text or images on the user's device. The output unit displays the generated advice on a smartphone or watch device. The output unit displays the generated advice in text format. The output unit displays the generated advice in image format.The output unit, for example, displays the generated advice horizontally if the user is holding their smartphone horizontally. This allows the golf advice system according to the embodiment to provide optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, word-of-mouth, and the performance of others.

[0030] The data collection unit collects data such as average distance and characteristics for each club, golf course data, user reviews, other users' performance, and weather. Specifically, it collects data set by the user as initial values, as well as data accumulated through apps and watch devices. For example, it collects data such as the average distance and characteristics for each club set by the user, the terrain and hole layout of the golf course, user reviews and performance, and weather forecasts. To calculate the average distance for each club, it calculates the average distance based on data of clubs the user has used in the past. To analyze the user's characteristics, it collects data such as the frequency of slices and hooks and the consistency of shots. To collect golf course data, it collects data such as course layout, hole difficulty, and green condition. This data is automatically collected from the devices the user uses when playing golf. For example, it uses the GPS function built into smartphones and watch devices to record the user's location and movement route, and collects club usage and shot results in real time. In addition, data obtained from sensors and cameras at the golf course is also collected when the user plays at the golf course. This allows the data collection unit to understand the user's playing style and the characteristics of the golf course in detail. Furthermore, the data collection unit can collect weather forecasts and user reviews publicly available on the internet and update them in real time. This allows the data collection unit to always provide data based on the latest information and build a foundation for providing users with the best possible advice.

[0031] The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of other players. Specifically, to analyze the user's abilities, it analyzes data such as past score history and statistics on club usage. To analyze the user's habits, it analyzes data such as the frequency of slices and hooks and the consistency of shots. To analyze the characteristics of the golf course, it analyzes data such as course layout, hole difficulty, and green condition. To analyze the weather, it analyzes data such as temperature, wind speed, and precipitation. To analyze reviews, it analyzes data such as user rating scores and comment content. To analyze the performance of other players, it analyzes data such as the score history and club usage statistics of other users. By using AI technology in these analyses, it is possible to quickly and accurately grasp data patterns and trends. For example, based on the user's past score history, the AI ​​identifies the user's strong and weak shots and suggests areas for improvement. Also, when analyzing the characteristics of the golf course, it considers the course terrain and hole layout to suggest the optimal club selection and shot strategy. Furthermore, by analyzing weather data, it can suggest shot adjustments in response to changes in wind direction, wind speed, and temperature. By analyzing user reviews, it can provide advice for specific golf courses and holes based on other users' ratings and comments. As a result, the generation unit can generate optimal advice that comprehensively considers the user's playing style and the characteristics of the golf course, supporting the user in improving their score and enjoying the game.

[0032] The output unit outputs the advice generated by the generation unit as text and images. Specifically, it displays the generated advice as text and images on the user's device. For example, the advice displayed on smartphones and watch devices is designed for easy viewing during play. Text-based advice includes detailed instructions such as specific club selection, shot direction, and power. Image-based advice visually shows the course layout and hole terrain, allowing the user to understand it intuitively. Furthermore, the output unit can automatically adjust how the advice is displayed depending on the orientation and usage of the user's device. For example, if the user holds their smartphone horizontally, the advice will also be displayed horizontally to improve visibility. The output unit can also collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can record the results of the user's play following the advice and use that data to make the next advice more accurate. This allows the output unit to always provide the user with the latest and most optimal advice, improving the quality of play. In addition, the output unit supports multiple languages, accommodating users who speak different languages. This allows the output unit to provide consistent service to global users, thereby increasing user satisfaction.

[0033] The data collection unit can collect data set by the user as initial values, as well as data accumulated in apps and watch devices. For example, the data collection unit collects data set by the user as initial values. For example, the data collection unit collects data such as the score and the type of club used set by the user. For example, the data collection unit collects data accumulated in apps and watch devices. For example, the data collection unit collects data from apps and watch devices equipped with GPS and shot tracking functions. For example, the data collection unit analyzes data collected from apps and watch devices used by the user. By collecting data set by the user as initial values, as well as data accumulated in apps and watch devices, it is possible to provide more accurate advice. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from apps and watch devices into a generating AI and have the generating AI perform data analysis.

[0034] The generation unit can generate optimal advice by comprehensively considering factors such as the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of others. For example, to analyze the user's abilities, the generation unit analyzes data such as past score history and statistics on club usage. For example, to analyze the user's habits, the generation unit analyzes data such as the frequency of slices and hooks and the consistency of shots. For example, to analyze the characteristics of the golf course, the generation unit analyzes data such as course layout, hole difficulty, and green condition. For example, to analyze the weather, the generation unit analyzes data such as temperature, wind speed, and precipitation. For example, to analyze reviews, the generation unit analyzes data such as user rating scores and comment content. For example, to analyze the performance of others, the generation unit analyzes data such as the score history and statistics on club usage of other users. This allows the system to provide optimal advice by comprehensively considering factors such as the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of others. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI generate optimal advice.

[0035] The output unit can output the generated advice to the user's device as text or an image. The output unit can, for example, display the generated advice as text or an image on the user's device. The output unit can, for example, display the generated advice on a smartphone or a watch-type device. The output unit can, for example, display the generated advice in text format. The output unit can, for example, display the generated advice in image format. This allows the user to easily review the advice by outputting it to the user's device as text or an image. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the generated advice into a generating AI and have the generating AI perform the output as text or an image.

[0036] The generation unit can analyze which club to use, in which direction to hit, and what kind of shot to hit on a particular hole. For example, the generation unit can analyze which club to use on a particular hole. For example, the generation unit can analyze which direction to hit on a particular hole. For example, the generation unit can analyze what kind of shot to hit on a particular hole. By analyzing which club to use, in which direction to hit, and what kind of shot to hit on a particular hole, the generation unit can provide the user with optimal advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the club selection and shot direction for a particular hole into the generation AI and have the generation AI generate optimal advice.

[0037] The output unit can also display advice horizontally if the user is holding their smartphone horizontally. For example, if the user is holding their smartphone horizontally, the output unit will display the advice horizontally. This ensures walking safety by displaying the advice horizontally when the user is holding their smartphone horizontally. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input smartphone tilt data into a generating AI and have the generating AI execute the method for displaying the advice.

[0038] The data collection unit can analyze the user's past play history and select the optimal data collection method. For example, the data collection unit can determine the type of data to collect based on data from clubs the user has used in the past. For example, the data collection unit can analyze the user's past scores and set priorities for the data to be collected. For example, the data collection unit can customize the data collection method by considering the user's past playing style. This allows the data collection unit to select the optimal data collection method by analyzing the user's past play history and provide more accurate advice. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past play history data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data while considering the user's current physical condition and fatigue level. For example, if the user is tired, the data collection unit can reduce the amount of data collected and focus on important data. For example, if the user is unwell, the data collection unit can temporarily stop data collection and wait for the user to recover. For example, if the user is healthy, the data collection unit can collect detailed data and gather information to provide highly accurate advice. By considering the user's physical condition and fatigue level when collecting data, the burden on the user is reduced and efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's physical condition and fatigue level data into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a specific golf course, the data collection unit will prioritize the collection of data related to that golf course. For example, if the user is at a specific hole, the data collection unit will prioritize the collection of data related to that hole. For example, if the user is in a specific region, the data collection unit will prioritize the collection of weather information for that region. By considering the user's geographical location information during data collection, highly relevant data can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze golf posts shared by a user on social media and collect relevant data. For example, the data collection unit can collect data from golf-related accounts that a user follows on social media. For example, the data collection unit can collect data from golf communities that a user participates in on social media. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The generation unit can adjust the level of detail of advice based on the user's play style when generating advice. For example, if the user has an aggressive play style, the generation unit will generate advice that encourages taking risks. For example, if the user has a defensive play style, the generation unit will generate advice that encourages safe play. For example, if the user has a balanced play style, the generation unit will generate advice that considers the balance between risk and safety. By adjusting the level of detail of advice based on the user's play style, the generation unit can provide the user with the most suitable advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's play style data into a generation AI and have the generation AI perform the adjustment of the level of detail of the advice.

[0043] The generation unit can apply different advice algorithms depending on the characteristics of the golf course when generating advice. For example, if the golf course is located in a hilly area, the generation unit will generate advice that takes slopes into account. For example, if the golf course is located along the coast, the generation unit will generate advice that takes wind effects into account. For example, if the golf course is located in a forested area, the generation unit will generate advice that avoids obstacles. In this way, by applying different advice algorithms depending on the characteristics of the golf course, the optimal advice for the user can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input golf course characteristic data into a generation AI and have the generation AI execute the application of the advice algorithm.

[0044] The generation unit can determine the priority of advice based on the user's play history when generating advice. For example, the generation unit may prioritize generating advice based on data of shots the user has successfully taken in the past. For example, the generation unit may generate advice including areas for improvement based on data of shots the user has failed at in the past. For example, the generation unit may analyze the user's past play history and prioritize generating the most effective advice. By determining the priority of advice based on the user's play history, the system can provide the user with the most suitable advice. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's play history data into a generation AI and have the generation AI determine the priority of advice.

[0045] The generation unit can adjust the order of advice based on the user's relevance when generating advice. For example, the generation unit adjusts the order of advice based on how often the user uses a particular club. For example, the generation unit adjusts the order of advice based on how often the user hits a particular shot. For example, the generation unit adjusts the order of advice based on the user's playing style. By adjusting the order of advice based on the user's relevance, the system can provide the user with the most appropriate advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the order of advice.

[0046] The output unit can select the optimal display method by referring to the user's past operation history when displaying advice. For example, the output unit can select the optimal display method based on display methods the user has used in the past. For example, the output unit can select a highly visible display method from the user's past operation history. For example, the output unit can analyze the user's past operation history and select the most effective display method. This allows the system to select the optimal display method by referring to the user's past operation history and provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0047] The output unit can customize the display method when displaying advice according to the characteristics of the user's device. For example, if the user is using a smartphone, the output unit provides a display method that matches the screen size. For example, if the user is using a tablet, the output unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the output unit provides a concise and highly visible display method. By customizing the display method according to the characteristics of the user's device, it is possible to provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device characteristic data into a generating AI and have the generating AI perform the customization of the display method.

[0048] The output unit can select the optimal display method when displaying advice, taking into account the user's geographical location. For example, if the user is at a specific golf course, the output unit will prioritize displaying information related to that golf course. For example, if the user is at a specific hole, the output unit will prioritize displaying information related to that hole. For example, if the user is in a specific region, the output unit will prioritize displaying weather information for that region. By selecting the optimal display method considering the user's geographical location, the output unit can provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0049] The output unit can analyze the user's social media activity and suggest display methods when displaying advice. For example, the output unit can analyze golf posts shared by the user on social media and display relevant information. For example, the output unit can display information from golf-related accounts that the user follows on social media. For example, the output unit can display information from golf communities that the user participates in on social media. By analyzing the user's social media activity, it is possible to provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for display methods.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit collects biometric data such as the user's heart rate and body temperature, allowing for real-time monitoring of the user's physical condition. For example, if the user's heart rate suddenly increases during a round of golf, the unit can generate advice encouraging them to take a break. It can also provide advice encouraging hydration if the user's body temperature is high. Furthermore, it can analyze the user's biometric data over the long term and propose training plans tailored to changes in their physical condition. This allows for safer and more effective golf play by providing advice that takes the user's health into consideration.

[0052] The data collection unit can collect and analyze videos of users' golf swings. For example, it can collect swing videos taken by users with their smartphones or cameras and analyze the swing form and timing. It can also compare a user's swing videos with those of other users and point out areas for improvement. Furthermore, it can compare a user's swing videos with those of professional golfers and suggest an ideal swing form. This allows users to objectively evaluate their own swing and receive specific advice for improvement.

[0053] The data collection unit can monitor the user's golf club usage in real time and evaluate the wear condition of the clubs. For example, if a user frequently uses a particular club, the data collection unit can evaluate its wear condition and notify the user when it's time to replace it. It can also suggest club maintenance methods based on the wear condition of the clubs. Furthermore, the data collection unit can analyze the user's club usage data and suggest the optimal club set. This allows the user to always use the best clubs and improve the quality of their play.

[0054] The data collection unit can collect and analyze audio data from users during their golf game. For example, it can collect the voices a user makes during play and analyze the game situation and the user's emotional state. It can also compare a user's audio data with data from other users to identify areas for improvement. Furthermore, it can compare a user's audio data with data from professional golfers to suggest an ideal playing style. This allows users to objectively evaluate their own play and receive specific advice for improvement.

[0055] The data collection unit can collect and analyze ambient sound data from users during their golf game. For example, it can collect ambient sounds such as wind and birdsong on the golf course and analyze the gameplay. Furthermore, based on the ambient sound data, the unit can provide advice to improve concentration during play. It can also compare the ambient sound data with that of other users and point out areas for improvement in play. This allows users to objectively evaluate their own playing environment and receive specific advice for improvement.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data such as average distance and characteristics for each club, golf course data, reviews, other users' performance, and weather. For example, it collects data set by the user as initial values, as well as data accumulated through apps and watch devices. The data collection unit collects data such as average distance and characteristics for each club set by the user, golf course terrain and hole layout, reviews and performance of other users, and weather forecasts. Furthermore, in order to calculate the average distance for each club, it calculates the average distance based on data of clubs the user has used in the past, and in order to analyze the user's characteristics, it collects data such as the frequency of slices and hooks and the consistency of shots. In order to collect golf course data, it collects data such as course layout, hole difficulty, and green condition. Step 2: The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of others. For example, to analyze the user's abilities, it analyzes data such as past score history and statistics on clubs used. To analyze the user's habits, it analyzes data such as the frequency of slices and hooks and the consistency of shots. To analyze the characteristics of the golf course, it analyzes data such as course layout, hole difficulty, and green condition. To analyze the weather, it analyzes data such as temperature, wind speed, and precipitation. To analyze reviews, it analyzes data such as user rating scores and comment content. To analyze the performance of others, it analyzes data such as other users' score history and statistics on clubs used. Step 3: The output unit outputs the advice generated by the generation unit as text or images. For example, it displays the generated advice as text or images on the user's device, such as a smartphone or smartwatch. The generated advice is displayed in text format and as an image format. If the user is holding their smartphone horizontally, the advice is also displayed horizontally.

[0058] (Example of form 2) The golf advice system according to an embodiment of the present invention is a system that advises the user on the direction to aim and the club to use in each situation in golf. This golf advice system collects data such as the average distance and characteristics of each club, golf course data, user reviews on the web, the performance of other users using the service, and weather, and a generating AI analyzes this data to output the most appropriate advice for the situation. For example, the golf advice system uses data set by the user as initial values, or data accumulated in apps or watch-type devices. For example, it collects data such as the average distance and characteristics of each club set by the user, the terrain and hole layout of the golf course, reviews and performance of other users, and weather forecasts. Next, the golf advice system uses a generating AI to analyze the collected data. Based on the collected data, the generating AI comprehensively considers the user's ability and characteristics, the characteristics of the golf course, the weather, reviews, and the performance of others to generate the most appropriate advice for the situation. For example, it analyzes which club the user should use on a particular hole, in which direction to hit, and what kind of shot to hit. The generated advice is output as text or images. For example, it is displayed on the user's device (smartphone or watch-type device). This allows users to receive personalized strategies rather than the general strategies displayed on the navigation system in their golf carts. This system enables intermediate golfers to stabilize their scores on the course and hit more accurate shots. For example, for users who can hit consistent shots at the driving range but struggle to maintain consistent scores on the course, the AI-generated advice can provide personalized guidance, leading to improved scores. Furthermore, if the user holds their smartphone horizontally, the advice is displayed horizontally, ensuring safety while walking. As a result, the golf advice system can provide optimal advice by comprehensively considering the user's ability and habits, the characteristics of the golf course, the weather, reviews, and the performance of other players.

[0059] The golf advice system according to the embodiment comprises a collection unit, a generation unit, and an output unit. The collection unit collects data such as the average distance and characteristics of each club, golf course data, reviews, other users' performance, and weather. The collection unit collects data such as data set by the user as initial values, and data accumulated in apps or watch-type devices. The collection unit collects data such as the average distance and characteristics of each club set by the user, the terrain and hole layout of the golf course, reviews and performance of other users, and weather forecasts. The collection unit calculates the average distance based on data of clubs the user has used in the past in order to calculate the average distance for each club. The collection unit collects data such as the frequency of slices and hooks and the consistency of shots in order to analyze the user's characteristics. The collection unit collects data such as course layout, hole difficulty, and green condition in order to collect golf course data. The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's ability and characteristics, the characteristics of the golf course, the weather, reviews, and the performance of others. The generation unit analyzes data such as past score history and club usage statistics to analyze the user's abilities. The generation unit analyzes data such as the frequency of slices and hooks and shot consistency to analyze the user's habits. The generation unit analyzes data such as course layout, hole difficulty, and green condition to analyze the characteristics of a golf course. The generation unit analyzes data such as temperature, wind speed, and precipitation to analyze the weather. The generation unit analyzes data such as user rating scores and comment content to analyze reviews. The generation unit analyzes data such as other users' score history and club usage statistics to analyze the performance of others. The output unit outputs the advice generated by the generation unit as text or images. The output unit displays the generated advice as text or images on the user's device. The output unit displays the generated advice on a smartphone or watch device. The output unit displays the generated advice in text format. The output unit displays the generated advice in image format.The output unit, for example, displays the generated advice horizontally if the user is holding their smartphone horizontally. This allows the golf advice system according to the embodiment to provide optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, word-of-mouth, and the performance of others.

[0060] The data collection unit collects data such as average distance and characteristics for each club, golf course data, user reviews, other users' performance, and weather. Specifically, it collects data set by the user as initial values, as well as data accumulated through apps and watch devices. For example, it collects data such as the average distance and characteristics for each club set by the user, the terrain and hole layout of the golf course, user reviews and performance, and weather forecasts. To calculate the average distance for each club, it calculates the average distance based on data of clubs the user has used in the past. To analyze the user's characteristics, it collects data such as the frequency of slices and hooks and the consistency of shots. To collect golf course data, it collects data such as course layout, hole difficulty, and green condition. This data is automatically collected from the devices the user uses when playing golf. For example, it uses the GPS function built into smartphones and watch devices to record the user's location and movement route, and collects club usage and shot results in real time. In addition, data obtained from sensors and cameras at the golf course is also collected when the user plays at the golf course. This allows the data collection unit to understand the user's playing style and the characteristics of the golf course in detail. Furthermore, the data collection unit can collect weather forecasts and user reviews publicly available on the internet and update them in real time. This allows the data collection unit to always provide data based on the latest information and build a foundation for providing users with the best possible advice.

[0061] The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of other players. Specifically, to analyze the user's abilities, it analyzes data such as past score history and statistics on club usage. To analyze the user's habits, it analyzes data such as the frequency of slices and hooks and the consistency of shots. To analyze the characteristics of the golf course, it analyzes data such as course layout, hole difficulty, and green condition. To analyze the weather, it analyzes data such as temperature, wind speed, and precipitation. To analyze reviews, it analyzes data such as user rating scores and comment content. To analyze the performance of other players, it analyzes data such as the score history and club usage statistics of other users. By using AI technology in these analyses, it is possible to quickly and accurately grasp data patterns and trends. For example, based on the user's past score history, the AI ​​identifies the user's strong and weak shots and suggests areas for improvement. Also, when analyzing the characteristics of the golf course, it considers the course terrain and hole layout to suggest the optimal club selection and shot strategy. Furthermore, by analyzing weather data, it can suggest shot adjustments in response to changes in wind direction, wind speed, and temperature. By analyzing user reviews, it can provide advice for specific golf courses and holes based on other users' ratings and comments. As a result, the generation unit can generate optimal advice that comprehensively considers the user's playing style and the characteristics of the golf course, supporting the user in improving their score and enjoying the game.

[0062] The output unit outputs the advice generated by the generation unit as text and images. Specifically, it displays the generated advice as text and images on the user's device. For example, the advice displayed on smartphones and watch devices is designed for easy viewing during play. Text-based advice includes detailed instructions such as specific club selection, shot direction, and power. Image-based advice visually shows the course layout and hole terrain, allowing the user to understand it intuitively. Furthermore, the output unit can automatically adjust how the advice is displayed depending on the orientation and usage of the user's device. For example, if the user holds their smartphone horizontally, the advice will also be displayed horizontally to improve visibility. The output unit can also collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can record the results of the user's play following the advice and use that data to make the next advice more accurate. This allows the output unit to always provide the user with the latest and most optimal advice, improving the quality of play. In addition, the output unit supports multiple languages, accommodating users who speak different languages. This allows the output unit to provide consistent service to global users, thereby increasing user satisfaction.

[0063] The data collection unit can collect data set by the user as initial values, as well as data accumulated in apps and watch devices. For example, the data collection unit collects data set by the user as initial values. For example, the data collection unit collects data such as the score and the type of club used set by the user. For example, the data collection unit collects data accumulated in apps and watch devices. For example, the data collection unit collects data from apps and watch devices equipped with GPS and shot tracking functions. For example, the data collection unit analyzes data collected from apps and watch devices used by the user. By collecting data set by the user as initial values, as well as data accumulated in apps and watch devices, it is possible to provide more accurate advice. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from apps and watch devices into a generating AI and have the generating AI perform data analysis.

[0064] The generation unit can generate optimal advice by comprehensively considering factors such as the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of others. For example, to analyze the user's abilities, the generation unit analyzes data such as past score history and statistics on club usage. For example, to analyze the user's habits, the generation unit analyzes data such as the frequency of slices and hooks and the consistency of shots. For example, to analyze the characteristics of the golf course, the generation unit analyzes data such as course layout, hole difficulty, and green condition. For example, to analyze the weather, the generation unit analyzes data such as temperature, wind speed, and precipitation. For example, to analyze reviews, the generation unit analyzes data such as user rating scores and comment content. For example, to analyze the performance of others, the generation unit analyzes data such as the score history and statistics on club usage of other users. This allows the system to provide optimal advice by comprehensively considering factors such as the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of others. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI generate optimal advice.

[0065] The output unit can output the generated advice to the user's device as text or an image. The output unit can, for example, display the generated advice as text or an image on the user's device. The output unit can, for example, display the generated advice on a smartphone or a watch-type device. The output unit can, for example, display the generated advice in text format. The output unit can, for example, display the generated advice in image format. This allows the user to easily review the advice by outputting it to the user's device as text or an image. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the generated advice into a generating AI and have the generating AI perform the output as text or an image.

[0066] The generation unit can analyze which club to use, in which direction to hit, and what kind of shot to hit on a particular hole. For example, the generation unit can analyze which club to use on a particular hole. For example, the generation unit can analyze which direction to hit on a particular hole. For example, the generation unit can analyze what kind of shot to hit on a particular hole. By analyzing which club to use, in which direction to hit, and what kind of shot to hit on a particular hole, the generation unit can provide the user with optimal advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the club selection and shot direction for a particular hole into the generation AI and have the generation AI generate optimal advice.

[0067] The output unit can also display advice horizontally if the user is holding their smartphone horizontally. For example, if the user is holding their smartphone horizontally, the output unit will display the advice horizontally. This ensures walking safety by displaying the advice horizontally when the user is holding their smartphone horizontally. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input smartphone tilt data into a generating AI and have the generating AI execute the method for displaying the advice.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect data frequently and gather detailed information. For example, if the user is stressed, the data collection unit will reduce the frequency of data collection to alleviate the user's burden. For example, if the user is focused, the data collection unit will prioritize the collection of important data to ensure efficient data collection. By adjusting the timing of data collection based on the user's emotions, the burden on the user is reduced and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0069] The data collection unit can analyze the user's past play history and select the optimal data collection method. For example, the data collection unit can determine the type of data to collect based on data from clubs the user has used in the past. For example, the data collection unit can analyze the user's past scores and set priorities for the data to be collected. For example, the data collection unit can customize the data collection method by considering the user's past playing style. This allows the data collection unit to select the optimal data collection method by analyzing the user's past play history and provide more accurate advice. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past play history data into a generating AI and have the generating AI select the optimal data collection method.

[0070] The data collection unit can filter data while considering the user's current physical condition and fatigue level. For example, if the user is tired, the data collection unit can reduce the amount of data collected and focus on important data. For example, if the user is unwell, the data collection unit can temporarily stop data collection and wait for the user to recover. For example, if the user is healthy, the data collection unit can collect detailed data and gather information to provide highly accurate advice. By considering the user's physical condition and fatigue level when collecting data, the burden on the user is reduced and efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's physical condition and fatigue level data into a generating AI and have the generating AI perform data filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is stressed, the data collection unit will prioritize collecting only important data. If the user is focused, the data collection unit will prioritize collecting data related to gameplay. This enables efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a specific golf course, the data collection unit will prioritize the collection of data related to that golf course. For example, if the user is at a specific hole, the data collection unit will prioritize the collection of data related to that hole. For example, if the user is in a specific region, the data collection unit will prioritize the collection of weather information for that region. By considering the user's geographical location information during data collection, highly relevant data can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0073] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze golf posts shared by a user on social media and collect relevant data. For example, the data collection unit can collect data from golf-related accounts that a user follows on social media. For example, the data collection unit can collect data from golf communities that a user participates in on social media. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0074] The generation unit can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit generates advice that includes detailed explanations. For example, if the user is stressed, the generation unit generates concise and to-the-point advice. For example, if the user is focused, the generation unit generates advice that includes specific instructions. By adjusting the way advice is expressed based on the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the way advice is expressed.

[0075] The generation unit can adjust the level of detail of advice based on the user's play style when generating advice. For example, if the user has an aggressive play style, the generation unit will generate advice that encourages taking risks. For example, if the user has a defensive play style, the generation unit will generate advice that encourages safe play. For example, if the user has a balanced play style, the generation unit will generate advice that considers the balance between risk and safety. By adjusting the level of detail of advice based on the user's play style, the generation unit can provide the user with the most suitable advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's play style data into a generation AI and have the generation AI perform the adjustment of the level of detail of the advice.

[0076] The generation unit can apply different advice algorithms depending on the characteristics of the golf course when generating advice. For example, if the golf course is located in a hilly area, the generation unit will generate advice that takes slopes into account. For example, if the golf course is located along the coast, the generation unit will generate advice that takes wind effects into account. For example, if the golf course is located in a forested area, the generation unit will generate advice that avoids obstacles. In this way, by applying different advice algorithms depending on the characteristics of the golf course, the optimal advice for the user can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input golf course characteristic data into a generation AI and have the generation AI execute the application of the advice algorithm.

[0077] The generation unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the generation unit generates detailed advice. For example, if the user is stressed, the generation unit generates concise advice. For example, if the user is focused, the generation unit generates advice that includes specific instructions. By adjusting the length of the advice based on the user's emotions, it is possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the advice.

[0078] The generation unit can determine the priority of advice based on the user's play history when generating advice. For example, the generation unit may prioritize generating advice based on data of shots the user has successfully taken in the past. For example, the generation unit may generate advice including areas for improvement based on data of shots the user has failed at in the past. For example, the generation unit may analyze the user's past play history and prioritize generating the most effective advice. By determining the priority of advice based on the user's play history, the system can provide the user with the most suitable advice. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's play history data into a generation AI and have the generation AI determine the priority of advice.

[0079] The generation unit can adjust the order of advice based on the user's relevance when generating advice. For example, the generation unit adjusts the order of advice based on how often the user uses a particular club. For example, the generation unit adjusts the order of advice based on how often the user hits a particular shot. For example, the generation unit adjusts the order of advice based on the user's playing style. By adjusting the order of advice based on the user's relevance, the system can provide the user with the most appropriate advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the adjustment of the order of advice.

[0080] The output unit can estimate the user's emotions and adjust the way advice is displayed based on the estimated emotions. For example, if the user is relaxed, the output unit provides a display method that includes detailed information. For example, if the user is stressed, the output unit provides a concise and easily visible display method. For example, if the user is focused, the output unit provides a display method that includes specific instructions. By adjusting the way advice is displayed based on the user's emotions, it is possible to provide advice that is easy for the user to understand. 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 output unit may be performed using AI, for example, or without AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the way advice is displayed.

[0081] The output unit can select the optimal display method by referring to the user's past operation history when displaying advice. For example, the output unit can select the optimal display method based on display methods the user has used in the past. For example, the output unit can select a highly visible display method from the user's past operation history. For example, the output unit can analyze the user's past operation history and select the most effective display method. This allows the system to select the optimal display method by referring to the user's past operation history and provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0082] The output unit can customize the display method when displaying advice according to the characteristics of the user's device. For example, if the user is using a smartphone, the output unit provides a display method that matches the screen size. For example, if the user is using a tablet, the output unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the output unit provides a concise and highly visible display method. By customizing the display method according to the characteristics of the user's device, it is possible to provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device characteristic data into a generating AI and have the generating AI perform the customization of the display method.

[0083] The output unit can estimate the user's emotions and adjust the display order of advice based on the estimated emotions. For example, if the user is relaxed, the output unit will prioritize displaying detailed information. For example, if the user is stressed, the output unit will prioritize displaying important information. For example, if the user is focused, the output unit will prioritize displaying specific instructions. By adjusting the display order of advice based on the user's emotions, the system can provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the display order of advice.

[0084] The output unit can select the optimal display method when displaying advice, taking into account the user's geographical location. For example, if the user is at a specific golf course, the output unit will prioritize displaying information related to that golf course. For example, if the user is at a specific hole, the output unit will prioritize displaying information related to that hole. For example, if the user is in a specific region, the output unit will prioritize displaying weather information for that region. By selecting the optimal display method considering the user's geographical location, the output unit can provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0085] The output unit can analyze the user's social media activity and suggest display methods when displaying advice. For example, the output unit can analyze golf posts shared by the user on social media and display relevant information. For example, the output unit can display information from golf-related accounts that the user follows on social media. For example, the output unit can display information from golf communities that the user participates in on social media. By analyzing the user's social media activity, it is possible to provide advice that is easy for the user to understand. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for display methods.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit collects biometric data such as the user's heart rate and body temperature, allowing for real-time monitoring of the user's physical condition. For example, if the user's heart rate suddenly increases during a round of golf, the unit can generate advice encouraging them to take a break. It can also provide advice encouraging hydration if the user's body temperature is high. Furthermore, it can analyze the user's biometric data over the long term and propose training plans tailored to changes in their physical condition. This allows for safer and more effective golf play by providing advice that takes the user's health into consideration.

[0088] The generation unit can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is nervous, it can provide advice on breathing techniques or stretching to help them relax. If the user is confident, it can generate advice encouraging proactive play. Furthermore, if the user is feeling down, it can provide advice that includes a message of encouragement. By providing appropriate advice tailored to the user's emotional state, the quality of gameplay can be improved.

[0089] The data collection unit can collect and analyze videos of users' golf swings. For example, it can collect swing videos taken by users with their smartphones or cameras and analyze the swing form and timing. It can also compare a user's swing videos with those of other users and point out areas for improvement. Furthermore, it can compare a user's swing videos with those of professional golfers and suggest an ideal swing form. This allows users to objectively evaluate their own swing and receive specific advice for improvement.

[0090] The generation unit can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is focused, detailed advice can be provided during gameplay. If the user is relaxed, advice can be provided after gameplay. Furthermore, if the user is stressed, advice to help them relax can be provided before gameplay. This maximizes the effectiveness of gameplay by providing advice at the appropriate time according to the user's emotional state.

[0091] The data collection unit can monitor the user's golf club usage in real time and evaluate the wear condition of the clubs. For example, if a user frequently uses a particular club, the data collection unit can evaluate its wear condition and notify the user when it's time to replace it. It can also suggest club maintenance methods based on the wear condition of the clubs. Furthermore, the data collection unit can analyze the user's club usage data and suggest the optimal club set. This allows the user to always use the best clubs and improve the quality of their play.

[0092] The generation unit can estimate the user's emotions and personalize the advice based on those emotions. For example, if the user is relaxed, it can provide detailed technical advice. If the user is stressed, it can provide concise and to-the-point advice. Furthermore, if the user is focused, it can provide advice that includes specific instructions. This maximizes the effectiveness of gameplay by providing appropriate advice tailored to the user's emotional state.

[0093] The data collection unit can collect and analyze audio data from users during their golf game. For example, it can collect the voices a user makes during play and analyze the game situation and the user's emotional state. It can also compare a user's audio data with data from other users to identify areas for improvement. Furthermore, it can compare a user's audio data with data from professional golfers to suggest an ideal playing style. This allows users to objectively evaluate their own play and receive specific advice for improvement.

[0094] The generation unit can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is relaxed, it can provide advice that includes detailed explanations. If the user is stressed, it can provide concise and to-the-point advice. Furthermore, if the user is focused, it can provide advice that includes specific instructions. This maximizes the effectiveness of gameplay by providing appropriate advice tailored to the user's emotional state.

[0095] The data collection unit can collect and analyze ambient sound data from users during their golf game. For example, it can collect ambient sounds such as wind and birdsong on the golf course and analyze the gameplay. Furthermore, based on the ambient sound data, the unit can provide advice to improve concentration during play. It can also compare the ambient sound data with that of other users and point out areas for improvement in play. This allows users to objectively evaluate their own playing environment and receive specific advice for improvement.

[0096] The generation unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is relaxed, detailed advice can be prioritized. If the user is stressed, important advice can be prioritized. Furthermore, if the user is focused, advice including specific instructions can be prioritized. This maximizes the effectiveness of gameplay by providing appropriate advice tailored to the user's emotional state.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects data such as average distance and characteristics for each club, golf course data, reviews, other users' performance, and weather. For example, it collects data set by the user as initial values, as well as data accumulated through apps and watch devices. The data collection unit collects data such as average distance and characteristics for each club set by the user, golf course terrain and hole layout, reviews and performance of other users, and weather forecasts. Furthermore, in order to calculate the average distance for each club, it calculates the average distance based on data of clubs the user has used in the past, and in order to analyze the user's characteristics, it collects data such as the frequency of slices and hooks and the consistency of shots. In order to collect golf course data, it collects data such as course layout, hole difficulty, and green condition. Step 2: The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the performance of others. For example, to analyze the user's abilities, it analyzes data such as past score history and statistics on clubs used. To analyze the user's habits, it analyzes data such as the frequency of slices and hooks and the consistency of shots. To analyze the characteristics of the golf course, it analyzes data such as course layout, hole difficulty, and green condition. To analyze the weather, it analyzes data such as temperature, wind speed, and precipitation. To analyze reviews, it analyzes data such as user rating scores and comment content. To analyze the performance of others, it analyzes data such as other users' score history and statistics on clubs used. Step 3: The output unit outputs the advice generated by the generation unit as text or images. For example, it displays the generated advice as text or images on the user's device, such as a smartphone or smartwatch. The generated advice is displayed in text format and as an image format. If the user is holding their smartphone horizontally, the advice is also displayed horizontally.

[0099] 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] 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.

[0102] Each of the multiple elements described above, including the collection unit, generation unit, and output unit, is implemented in, for example, at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and generates optimal advice. The output unit displays the generated advice using, for example, the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, generation unit, and output unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and generates optimal advice. The output unit displays the generated advice using, for example, the display or speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, generation unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and generates optimal advice. The output unit displays the generated advice using, for example, the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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.

[0151] Each of the multiple elements described above, including the acquisition unit, generation unit, and output unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit collects data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates optimal advice. The output unit displays the generated advice using, for example, the display or speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The data collection department collects data such as average distance and characteristics of each club, golf course data, reviews, other users' performance, and weather. The generation unit analyzes the data collected by the collection unit and generates optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, word-of-mouth, the performance of others, etc. The system includes an output unit that outputs the advice generated by the generation unit as text or images. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data that users have set as initial values, as well as data accumulated in apps and watch-type devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The system comprehensively considers the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the track record of other users to generate optimal advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, The generated advice is output to the user's device as text or an image. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Analyze which club to use, in which direction to hit the ball, and what kind of shot to hit on a specific hole. The system described in Appendix 1, characterized by the features described herein. (Note 6) The output unit is, If the user is holding their smartphone horizontally, the advice will also be displayed horizontally. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past play history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed considering the user's current physical condition and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating advice, adjust the level of detail of the advice based on the user's play style. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating advice, different advice algorithms are applied depending on the characteristics of the golf course. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating advice, the system prioritizes the advice based on the user's play history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating advice, the order of advice is adjusted based on the relevance of the user. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, It estimates the user's emotions and adjusts how advice is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, When displaying advice, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When displaying advice, customize the display method according to the characteristics of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the user's emotions and adjusts the order in which advice is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When displaying advice, the system selects the optimal display method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When displaying advice, the system analyzes the user's social media activity and suggests how to display it. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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. The data collection department collects data such as average distance and characteristics of each club, golf course data, reviews, other users' performance, and weather. The data collected by the aforementioned collection unit is analyzed, and the generation unit generates optimal advice by comprehensively considering the user's abilities and habits, the characteristics of the golf course, the weather, word-of-mouth, the performance of others, etc. The system includes an output unit that outputs the advice generated by the generation unit as text or images. A system characterized by the following features.

2. The aforementioned collection unit is It collects data that users have set as initial values, as well as data accumulated in apps and watch-type devices. The system according to feature 1.

3. The generating unit is The system comprehensively considers the user's abilities and habits, the characteristics of the golf course, the weather, reviews, and the track record of other users to generate optimal advice. The system according to feature 1.

4. The output unit is, The generated advice is output to the user's device as text or an image. The system according to feature 1.

5. The generating unit is Analyze which club to use, in which direction to hit the ball, and what kind of shot to hit on a specific hole. The system according to feature 1.

6. The output unit is, If the user is holding their smartphone horizontally, the advice will also be displayed horizontally. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past play history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed considering the user's current physical condition and fatigue level. The system according to feature 1.

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

Images