system

Abstract

The system according to this embodiment aims to accurately determine the strength and direction of a golf shot on the green. [Solution] The system according to the embodiment comprises an analysis unit, a calculation unit, a generation unit, and an explanation unit. The analysis unit analyzes images that show the undulation information of the green, the position of the cup, and the position of the ball. The calculation unit calculates the strength and direction of the specific shot based on the information analyzed by the analysis unit. The generation unit generates a heat map based on the results calculated by the calculation unit. The explanation unit generates an explanatory text based on the heat map generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to accurately grasp the strength and direction of the hitting method on the golf green.

[0005] The system according to the embodiment aims to accurately grasp the strength and direction of the hitting method on the golf green.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a calculation unit, a generation unit, and an explanation unit. The analysis unit analyzes images that show the undulation information of the green, the position of the cup, and the position of the ball. The calculation unit calculates the strength and direction of the specific shot based on the information analyzed by the analysis unit. The generation unit generates a heat map based on the results calculated by the calculation unit. The explanation unit generates an explanatory text based on the heat map generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately determine the strength and direction of a shot on a golf green. [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 controls communication between a plurality of 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 putting support system according to an embodiment of the present invention is a system that, upon input of an image showing the undulation information of the green, the location of the cup, and the location of the ball, generates an image and explanatory text in heatmap format that shows how hard to putt and where to putt. This golf putting support system operates when the user inputs an image showing the undulation information of the green, the location of the cup, and the location of the ball. For example, the user inputs an image taken with a smartphone or camera into the system. This image is input into a generating AI and analyzed. The generating AI analyzes the input image and identifies the undulation information of the green, the location of the cup, and the location of the ball. For example, the generating AI uses image analysis technology to detect the slope of the green, the location of the cup, and the location of the ball. Next, based on this information, the generating AI calculates how hard to putt and where to putt. For example, the generating AI calculates the optimal putting technique considering the slope of the green and the distance to the cup. Based on the calculation result, the generating AI generates an image in heatmap format that indicates the direction. This image visually shows how hard to putt and where to putt. For example, the red areas indicate directions where you should hit hard, and the blue areas indicate directions where you should hit softly. Finally, the generating AI generates text along with the heatmap image explaining how hard to hit and where to hit. For example, it may include specific advice such as, "It's best to hit hard towards the right side of the cup." This system allows users to putt more effectively on the green. Thus, the golf putting assistance system enables users to putt more effectively on the green.

[0029] The golf putting support system according to this embodiment comprises an analysis unit, a calculation unit, a generation unit, and an explanation unit. The analysis unit analyzes an image that shows the undulation information of the green, the position of the cup, and the position of the ball. The analysis unit identifies the slope of the green, the position of the cup, and the position of the ball, for example, using image analysis technology. For example, the analysis unit can obtain the undulation information of the green by detecting feature points in the image and analyzing their positional relationships. The analysis unit can also use an algorithm to detect circular features in the image to identify the position of the cup. Furthermore, the analysis unit can use an algorithm to detect white circular features in the image to identify the position of the ball. The calculation unit calculates how hard to putt and where to putt based on the information analyzed by the analysis unit. The calculation unit calculates the optimal putting technique, for example, by considering the slope of the green and the distance to the cup. For example, the calculation unit can calculate the optimal putting technique by receiving the slope angle of the green and the distance to the cup as input and performing a physical simulation. The calculation unit can also correct the putting technique by considering environmental conditions such as wind speed and wind direction. The generation unit generates a heatmap based on the results calculated by the calculation unit. The generation unit can visually indicate putting techniques to the user by representing the calculation results with colors, for example. For example, the generation unit can generate a heatmap in which red areas indicate directions where the ball should be hit hard, and blue areas indicate directions where the ball should be hit softly. The generation unit can also adjust the color scheme of the heatmap according to the user's emotions. The explanation unit generates explanatory text based on the heatmap generated by the generation unit. For example, the explanation unit generates text that explains how hard to hit the ball and where to hit it, based on the information in the heatmap. For example, the explanation unit can generate specific advice such as, "It's best to hit the ball hard towards the right side of the cup." The explanation unit can also adjust the way the explanatory text is expressed according to the user's emotions. As a result, the golf putting support system according to this embodiment allows the user to putt more effectively on the green.

[0030] The analysis unit can identify the undulation information of the green, the location of the cup, and the location of the ball. The analysis unit can identify the slope of the green, the location of the cup, and the location of the ball, for example, by using image analysis technology. For example, the analysis unit can obtain information about the undulation information of the green by detecting feature points in the image and analyzing their positional relationships. The analysis unit can also use an algorithm to detect circular features in the image to identify the location of the cup. Furthermore, the analysis unit can use an algorithm to detect white circular features in the image to identify the location of the ball. This improves the accuracy of the calculation unit by allowing the analysis unit to accurately identify the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image data into a generative AI, and the generative AI can perform image analysis to identify information about the undulation information of the green, the location of the cup, and the location of the ball.

[0031] The calculation unit can calculate the optimal putting technique based on the green's slope and the distance to the cup. For example, the calculation unit can calculate the optimal putting technique by considering the green's slope and the distance to the cup. For example, the calculation unit can receive the green's slope angle and the distance to the cup as input and calculate the optimal putting technique by performing a physical simulation. The calculation unit can also correct the putting technique by considering environmental conditions such as wind speed and wind direction. As a result, the calculation unit can calculate the optimal putting technique, allowing the user to putt effectively. Some or all of the above processing in the calculation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the calculation unit can input data on the green's slope angle and the distance to the cup into a generative AI, which can then calculate the optimal putting technique by performing a physical simulation.

[0032] The generation unit can generate a heatmap based on the calculation results. The generation unit can visually indicate to the user how to play by representing the calculation results with colors, for example. For example, the generation unit can generate a heatmap in which red areas indicate directions where you should play hard and blue areas indicate directions where you should play softly. The generation unit can also adjust the color scheme of the heatmap according to the user's emotions. In this way, the user can visually understand how to play by generating a heatmap. 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 calculation result data into a generation AI, and the generation AI can generate a heatmap.

[0033] The explanation unit can generate specific advice based on the heatmap. For example, the explanation unit can generate sentences explaining how hard to hit the ball and where to hit it, based on the information in the heatmap. For example, the explanation unit can generate specific advice such as, "It's best to hit the ball hard towards the right side of the cup." The explanation unit can also adjust the expression of the explanation sentence according to the user's emotions. This allows the user to understand how hard to hit the ball and where to hit it by generating specific advice from the explanation unit. Some or all of the above processing in the explanation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the explanation unit can input heatmap data into a generating AI, and the generating AI can generate specific advice.

[0034] The analysis unit can improve the accuracy of its analysis by referring to past undulation information of the green during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to undulation information collected on the same green in the past. For example, the analysis unit can obtain undulation information from a past database and reflect it in the current analysis. The analysis unit can also consider changes in undulation under specific seasons or weather conditions from past data. Furthermore, the analysis unit can refer to past player data and reflect general trends in the analysis. In this way, the accuracy of the analysis is improved by referring to past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit may input past undulation information into a generating AI, and the generating AI may improve the accuracy of the analysis. Analysis can be performed.

[0035] The analysis unit can correct the analysis results based on weather information during the analysis. For example, the analysis unit can acquire current weather information and reflect it in the analysis results. For example, the analysis unit can acquire weather data in real time and predict the condition of the green. Furthermore, the analysis unit can predict the condition of the green in response to changes in weather and correct the analysis results. In addition, the analysis unit can refer to past weather data and compare the analysis results under similar weather conditions. This improves the accuracy of the analysis results by considering weather information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input weather data into a generating AI, and the generating AI can correct the analysis results by considering the weather information.

[0036] The analysis unit can customize the analysis results by taking into account the user's play style during the analysis. For example, if the user has an aggressive play style, the analysis unit can provide risky analysis results. For example, the analysis unit can identify the play style by referring to the user's past play data. Also, if the user has a defensive play style, the analysis unit can provide safe analysis results. Furthermore, the analysis unit can provide the optimal analysis results by referring to the user's past play data. In this way, by providing analysis results that match the user's play style, the user can choose a more appropriate way to play. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can customize the analysis results by inputting the user's play data into a generating AI, which then identifies the play style.

[0037] The analysis unit can compare its analysis results with those of other players by referencing their data during the analysis process. For example, the analysis unit can refer to other players' data and reflect general trends in its analysis results. For example, the analysis unit can refer to other players' score data and play history and compare its analysis results. Furthermore, the analysis unit can provide optimal analysis results based on other players' success stories. In addition, the analysis unit can provide analysis results that help avoid risks based on other players' failure stories. This improves the reliability of the analysis results by referencing other players' data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can improve reliability by inputting other players' data into a generating AI, which then compares the analysis results.

[0038] The calculation unit can improve calculation accuracy by referring to past play data during calculations. For example, the calculation unit can refer to the user's past play data to improve calculation accuracy. For example, the calculation unit can refer to past score history and shot data and reflect them in the current calculation. The calculation unit can also calculate the optimal shot under specific conditions from past data. Furthermore, the calculation unit can refer to the past data of other players and reflect general trends in the calculation. In this way, calculation accuracy is improved by referring to past data. Some or all of the above processing in the calculation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the calculation unit can input past play data into a generating AI, which can then perform analysis to improve calculation accuracy.

[0039] The calculation unit can correct the calculation results by considering wind speed and wind direction during the calculation. For example, the calculation unit can acquire the current wind speed and wind direction and reflect them in the calculation results. For example, the calculation unit can acquire weather data in real time and correct the calculation results by considering wind speed and wind direction. Furthermore, the calculation unit can correct the calculation results in response to changes in wind speed and wind direction. In addition, the calculation unit can refer to past wind speed and wind direction data and compare calculation results under similar conditions. This improves the accuracy of the calculation results by considering wind speed and wind direction. Some or all of the above processing in the calculation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the calculation unit can input wind speed and wind direction data into a generating AI, and the generating AI can correct the calculation results.

[0040] The calculation unit can customize the calculation results by taking into account the user's skill level during calculation. For example, if the user is a beginner, the calculation unit can provide basic calculation results. For example, the calculation unit can determine the skill level by referring to the user's past scores and play history. Furthermore, if the user is an intermediate player, the calculation unit can provide detailed calculation results. In addition, if the user is an advanced player, the calculation unit can provide complex calculation results. This allows the user to choose a more appropriate playing style by providing calculation results according to their skill level. Some or all of the above processing in the calculation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the calculation unit can customize the calculation results by inputting the user's skill level into a generating AI, which then determines the skill level.

[0041] The calculation unit can compare calculation results by referencing data from other players during the calculation process. For example, the calculation unit can refer to data from other players and reflect general trends in the calculation results. For example, the calculation unit can refer to other players' score data and play history and compare calculation results. Furthermore, the calculation unit can provide optimal calculation results based on the success stories of other players. In addition, the calculation unit can assess risks based on the failure stories of other players. It is possible to provide calculation results that avoid errors. This improves the reliability of the calculation results by referencing data from other players. Some or all of the above processing in the calculation unit may be performed using a generating AI, for example, or without a generating AI. For example, the calculation unit can improve reliability by inputting data from other players into a generating AI, which then compares the calculation results.

[0042] The generation unit can improve generation accuracy by referring to past heatmap data during generation. For example, the generation unit can improve generation accuracy by referring to heatmap data collected in the past with the same green color. For example, the generation unit can retrieve heatmap data from a past database and reflect it in the current generation. The generation unit can also consider changes in the heatmap under specific seasons or weather conditions from past data. Furthermore, the generation unit can refer to past player data and reflect general trends in the generation. In this way, generation accuracy is improved by referring to past data. 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 past heatmap data into a generation AI, which can then perform analysis to improve generation accuracy.

[0043] The generation unit can correct the heatmap by taking weather information into consideration during generation. For example, the generation unit can acquire current weather information and reflect it in the heatmap. For example, the generation unit can acquire weather data in real time and correct the heatmap by taking weather information into consideration. The generation unit can also correct the heatmap in response to changes in weather. Furthermore, the generation unit can refer to past weather data and compare heatmaps under similar weather conditions. This improves the accuracy of the heatmap by considering weather information. 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 weather data into a generation AI, and the generation AI can correct the heatmap.

[0044] The generation unit can customize the heatmap during generation, taking into account the user's play style. For example, if the user has an aggressive play style, the generation unit can generate a risky heatmap. For example, the generation unit can identify the play style by referring to the user's past play data. Also, if the user has a defensive play style, the generation unit can generate a safe heatmap. Furthermore, the generation unit can generate an optimal heatmap by referring to the user's past play data. This allows the user to choose a more appropriate playing style by providing a heatmap tailored to their play style. 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 data into a generation AI, which can then identify the play style and customize the heatmap.

[0045] The generation unit can compare heatmaps by referencing data from other players during generation. For example, the generation unit can reference data from other players and reflect general trends in the heatmap. For example, the generation unit can reference other players' score data and play history and compare heatmaps. The generation unit can also generate an optimal heatmap based on the success stories of other players. Furthermore, the generation unit can generate a heatmap that avoids risks based on the failure stories of other players. This improves the reliability of the heatmap by referencing data from other players. 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 data from other players into a generation AI, and the generation AI can improve reliability by comparing heatmaps.

[0046] The description unit can improve the accuracy of description generation by referring to past description data. For example, the description unit can improve generation accuracy by referring to description data collected in the past for the same green. For example, the description unit can retrieve description data from past databases and reflect it in the current generation. The description unit can also consider how descriptions change under specific seasons or weather conditions from past data. Furthermore, the description unit can refer to past player data and reflect general trends in the description. In this way, the accuracy of description generation is improved by referring to past data. Some or all of the above processing in the description unit may be performed using a generation AI, for example, or without a generation AI. For example, the description unit can input past description data into a generation AI, which can then perform analysis to improve generation accuracy.

[0047] The description unit can correct the description text by taking weather information into consideration when generating the description text. For example, the description unit can acquire current weather information and reflect it in the description text. For example, the description unit can acquire weather data in real time and correct the description text by taking weather information into consideration. Furthermore, the description unit can correct the description text in response to changes in weather. In addition, the description unit can refer to past weather data and compare the description text under similar weather conditions. This improves the accuracy of the description text by taking weather information into consideration. Some or all of the above processing in the description unit may be performed using a generation AI, for example, or without a generation AI. For example, the description unit can input weather data into a generation AI, and the generation AI can correct the description text.

[0048] The description section can customize the description text when generating it, taking into account the user's skill level. For example, if the user is a beginner, the description section will generate a basic description text. For example, the description section can determine the user's skill level by referring to the user's past scores and play history. Furthermore, if the user is an intermediate player, the description section can generate a detailed description text. In addition, if the user is an advanced player, the description section can generate a specialized description text. This allows the user to receive more appropriate advice by providing descriptions tailored to their skill level. Some or all of the above processing in the description section may be performed using, for example, a generation AI, or without a generation AI. For example, the description section can input the user's skill level into a generation AI, and the generation AI can determine the skill level to customize the description text.

[0049] The description unit can compare descriptions by referencing data from other players when generating them. For example, the description unit can refer to data from other players and reflect general trends in the descriptions. For example, the description unit can refer to other players' score data and play history and compare descriptions. The description unit can also generate optimal descriptions based on the success stories of other players. Furthermore, the description unit can generate descriptions that avoid risks based on the failure stories of other players. In this way, the reliability of the descriptions is improved by referencing data from other players. Some or all of the above processing in the description unit may be performed using a generation AI, for example, or without a generation AI. For example, the description unit can input data from other players into a generation AI, and the generation AI can improve reliability by comparing descriptions.

[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 analysis unit can customize the analysis results by referring to the user's past play data. For example, the analysis unit can retrieve from the database what kind of shots the user has taken in the past that were successful or unsuccessful, and reflect this in the current analysis. Furthermore, the analysis unit can adjust the analysis results considering the user's play style (aggressive, defensive, etc.). In addition, the analysis unit can adjust the accuracy and detail of the analysis according to the user's skill level (beginner, intermediate, advanced). This allows for the provision of more personalized analysis results by utilizing the user's past play data.

[0052] The generation unit can customize the heatmap when generating it, taking into account the user's playing style. For example, if the user has an aggressive playing style, the generation unit can generate a heatmap that shows risky shots. Conversely, if the user has a defensive playing style, it can generate a heatmap that shows safe shots. Furthermore, it can refer to the user's past playing data to generate the optimal heatmap. By providing a heatmap tailored to the user's playing style, the unit enables the user to choose a more appropriate shot.

[0053] The analysis unit can compare its analysis results with those of other players by referencing their data during the analysis process. For example, it can refer to other players' score data and play history to compare its analysis results. It can also provide optimal analysis results based on other players' success stories. Furthermore, it can provide analysis results that help avoid risks based on other players' failure stories. In this way, referencing other players' data improves the reliability of the analysis results.

[0054] The calculation unit can correct the calculation results by taking weather information into account during the calculation process. For example, the calculation unit can acquire current weather information and reflect it in the calculation results. It can also correct the calculation results in response to changes in weather. Furthermore, it can refer to past weather data and compare calculation results under similar weather conditions. In this way, the accuracy of the calculation results is improved by taking weather information into account.

[0055] The description section can improve the accuracy of description generation by referring to past description data. For example, the description section can refer to description data collected for the same green in the past and reflect it in the current generation. It can also consider how descriptions will change under specific seasons or weather conditions based on past data. Furthermore, it can refer to past player data and reflect general trends in the descriptions. In this way, the accuracy of description generation is improved by referring to past data.

[0056] The analysis unit can customize the analysis results during the analysis process, taking into account the user's skill level. For example, if the user is a beginner, the analysis unit can provide basic analysis results. If the user is an intermediate player, it can provide detailed analysis results. Furthermore, if the user is an advanced player, it can provide complex analysis results. This allows the user to choose a more appropriate playing style by providing analysis results tailored to their skill level.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes images that reveal the undulation information of the green, the location of the cup, and the location of the ball. The analysis unit uses image analysis technology to identify the slope of the green, the location of the cup, and the location of the ball. For example, it detects feature points in the image and analyzes their positional relationships to obtain information about the undulation information of the green. In addition, it uses an algorithm to detect circular features in the image to identify the location of the cup, and an algorithm to detect white circular features in the image to identify the location of the ball. Step 2: The calculation unit calculates how hard to hit the ball and where to hit it, based on the information analyzed by the analysis unit. The calculation unit calculates the optimal shot, taking into account the slope of the green and the distance to the cup. For example, it can take the slope angle of the green and the distance to the cup as input and calculate the optimal shot by performing a physical simulation. It can also correct the shot by taking into account environmental conditions such as wind speed and wind direction. Step 3: The generation unit generates a heatmap based on the results calculated by the calculation unit. The generation unit visually indicates to the user how to hit the ball by representing the calculation results with colors. For example, it generates a heatmap in which red areas indicate directions where you should hit hard and blue areas indicate directions where you should hit softly. The color scheme of the heatmap can also be adjusted according to the user's emotions. Step 4: The explanation unit generates explanatory text based on the heatmap generated by the generation unit. Based on the information in the heatmap, the explanation unit generates text that explains how hard to hit the ball and where to hit it. For example, it can generate specific advice such as, "It's best to hit it hard towards the right side of the cup." It can also adjust the way the explanatory text is expressed according to the user's emotions.

[0059] (Example of form 2) The golf putting support system according to an embodiment of the present invention is a system that, upon input of an image showing the undulation information of the green, the location of the cup, and the location of the ball, generates an image and explanatory text in heatmap format that shows how hard to putt and where to putt. This golf putting support system operates when the user inputs an image showing the undulation information of the green, the location of the cup, and the location of the ball. For example, the user inputs an image taken with a smartphone or camera into the system. This image is input into a generating AI and analyzed. The generating AI analyzes the input image and identifies the undulation information of the green, the location of the cup, and the location of the ball. For example, the generating AI uses image analysis technology to detect the slope of the green, the location of the cup, and the location of the ball. Next, based on this information, the generating AI calculates how hard to putt and where to putt. For example, the generating AI calculates the optimal putting technique considering the slope of the green and the distance to the cup. Based on the calculation result, the generating AI generates an image in heatmap format that indicates the direction. This image visually shows how hard to putt and where to putt. For example, the red areas indicate directions where you should hit hard, and the blue areas indicate directions where you should hit softly. Finally, the generating AI generates text along with the heatmap image explaining how hard to hit and where to hit. For example, it may include specific advice such as, "It's best to hit hard towards the right side of the cup." This system allows users to putt more effectively on the green. Thus, the golf putting assistance system enables users to putt more effectively on the green.

[0060] The golf putting support system according to this embodiment comprises an analysis unit, a calculation unit, a generation unit, and an explanation unit. The analysis unit analyzes an image that shows the undulation information of the green, the position of the cup, and the position of the ball. The analysis unit identifies the slope of the green, the position of the cup, and the position of the ball, for example, using image analysis technology. For example, the analysis unit can obtain the undulation information of the green by detecting feature points in the image and analyzing their positional relationships. The analysis unit can also use an algorithm to detect circular features in the image to identify the position of the cup. Furthermore, the analysis unit can use an algorithm to detect white circular features in the image to identify the position of the ball. The calculation unit calculates how hard to putt and where to putt based on the information analyzed by the analysis unit. The calculation unit calculates the optimal putting technique, for example, by considering the slope of the green and the distance to the cup. For example, the calculation unit can calculate the optimal putting technique by receiving the slope angle of the green and the distance to the cup as input and performing a physical simulation. The calculation unit can also correct the putting technique by considering environmental conditions such as wind speed and wind direction. The generation unit generates a heatmap based on the results calculated by the calculation unit. The generation unit can visually indicate putting techniques to the user by representing the calculation results with colors, for example. For example, the generation unit can generate a heatmap in which red areas indicate directions where the ball should be hit hard, and blue areas indicate directions where the ball should be hit softly. The generation unit can also adjust the color scheme of the heatmap according to the user's emotions. The explanation unit generates explanatory text based on the heatmap generated by the generation unit. For example, the explanation unit generates text that explains how hard to hit the ball and where to hit it, based on the information in the heatmap. For example, the explanation unit can generate specific advice such as, "It's best to hit the ball hard towards the right side of the cup." The explanation unit can also adjust the way the explanatory text is expressed according to the user's emotions. As a result, the golf putting support system according to this embodiment allows the user to putt more effectively on the green.

[0061] The analysis unit can identify the undulation information of the green, the location of the cup, and the location of the ball. The analysis unit can identify the slope of the green, the location of the cup, and the location of the ball, for example, by using image analysis technology. For example, the analysis unit can obtain information about the undulation information of the green by detecting feature points in the image and analyzing their positional relationships. The analysis unit can also use an algorithm to detect circular features in the image to identify the location of the cup. Furthermore, the analysis unit can use an algorithm to detect white circular features in the image to identify the location of the ball. This improves the accuracy of the calculation unit by allowing the analysis unit to accurately identify the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input image data into a generative AI, and the generative AI can perform image analysis to identify information about the undulation information of the green, the location of the cup, and the location of the ball.

[0062] The calculation unit can calculate the optimal putting technique based on the green's slope and the distance to the cup. For example, the calculation unit can calculate the optimal putting technique by considering the green's slope and the distance to the cup. For example, the calculation unit can receive the green's slope angle and the distance to the cup as input and calculate the optimal putting technique by performing a physical simulation. The calculation unit can also correct the putting technique by considering environmental conditions such as wind speed and wind direction. As a result, the calculation unit can calculate the optimal putting technique, allowing the user to putt effectively. Some or all of the above processing in the calculation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the calculation unit can input data on the green's slope angle and the distance to the cup into a generative AI, which can then calculate the optimal putting technique by performing a physical simulation.

[0063] The generation unit can generate a heatmap based on the calculation results. The generation unit can visually indicate to the user how to play by representing the calculation results with colors, for example. For example, the generation unit can generate a heatmap in which red areas indicate directions where you should play hard and blue areas indicate directions where you should play softly. The generation unit can also adjust the color scheme of the heatmap according to the user's emotions. In this way, the user can visually understand how to play by generating a heatmap. 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 calculation result data into a generation AI, and the generation AI can generate a heatmap.

[0064] The explanation unit can generate specific advice based on the heatmap. For example, the explanation unit can generate sentences explaining how hard to hit the ball and where to hit it, based on the information in the heatmap. For example, the explanation unit can generate specific advice such as, "It's best to hit the ball hard towards the right side of the cup." The explanation unit can also adjust the expression of the explanation sentence according to the user's emotions. This allows the user to understand how hard to hit the ball and where to hit it by generating specific advice from the explanation unit. Some or all of the above processing in the explanation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the explanation unit can input heatmap data into a generating AI, and the generating AI can generate specific advice.

[0065] The analysis unit can estimate the user's emotions and adjust the analysis parameters based on the estimated emotions. For example, if the user is nervous, the analysis unit can improve the accuracy of the analysis to provide more detailed information. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. If the user is relaxed, the analysis unit can maintain normal accuracy and provide standard information. Furthermore, if the user is anxious, the analysis unit can improve accuracy and provide results quickly. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user facial expression data into a generative AI, and the generative AI can estimate emotions, thereby adjusting the analysis parameters.

[0066] The analysis unit can improve the accuracy of its analysis by referring to past undulation information of the green during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to undulation information collected on the same green in the past. For example, the analysis unit can obtain undulation information from a past database and reflect it in the current analysis. The analysis unit can also consider changes in undulation under specific seasons or weather conditions from past data. Furthermore, the analysis unit can refer to past player data and reflect general trends in the analysis. In this way, the accuracy of the analysis is improved by referring to past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit may input past undulation information into a generating AI, and the generating AI may improve the accuracy of the analysis. Analysis can be performed.

[0067] The analysis unit can correct the analysis results based on weather information during the analysis. For example, the analysis unit can acquire current weather information and reflect it in the analysis results. For example, the analysis unit can acquire weather data in real time and predict the condition of the green. Furthermore, the analysis unit can predict the condition of the green in response to changes in weather and correct the analysis results. In addition, the analysis unit can refer to past weather data and compare the analysis results under similar weather conditions. This improves the accuracy of the analysis results by considering weather information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input weather data into a generating AI, and the generating AI can correct the analysis results by considering the weather information.

[0068] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. In addition, if the user is anxious, the analysis unit can provide a display method that can be quickly understood. By adjusting the display method according to the user's emotions, the user can better understand the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI, and the generative AI can estimate the emotion, thereby adjusting the display method of the analysis results.

[0069] The analysis unit can customize the analysis results by taking into account the user's play style during the analysis. For example, if the user has an aggressive play style, the analysis unit can provide risky analysis results. For example, the analysis unit can identify the play style by referring to the user's past play data. Also, if the user has a defensive play style, the analysis unit can provide safe analysis results. Furthermore, the analysis unit can provide the optimal analysis results by referring to the user's past play data. In this way, by providing analysis results that match the user's play style, the user can choose a more appropriate way to play. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can customize the analysis results by inputting the user's play data into a generating AI, which then identifies the play style.

[0070] The analysis unit can compare its analysis results with those of other players by referencing their data during the analysis process. For example, the analysis unit can refer to other players' data and reflect general trends in its analysis results. For example, the analysis unit can refer to other players' score data and play history and compare its analysis results. Furthermore, the analysis unit can provide optimal analysis results based on other players' success stories. In addition, the analysis unit can provide analysis results that help avoid risks based on other players' failure stories. This improves the reliability of the analysis results by referencing other players' data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can improve reliability by inputting other players' data into a generating AI, which then compares the analysis results.

[0071] The calculation unit can estimate the user's emotions and adjust the calculation algorithm based on the estimated emotions. For example, if the user is nervous, the calculation unit adjusts the calculation algorithm with high accuracy. For example, the calculation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the calculation unit can use a standard calculation algorithm. Furthermore, if the user is anxious, the calculation unit can use an algorithm that provides calculation results quickly. In this way, by adjusting the calculation algorithm according to the user's emotions, more appropriate calculation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using a generative AI, or not using a generative AI. For example, the calculation unit can input user facial expression data into a generative AI, and the generative AI can estimate the emotion, thereby adjusting the calculation algorithm.

[0072] The calculation unit can improve calculation accuracy by referring to past play data during calculations. For example, the calculation unit can refer to the user's past play data to improve calculation accuracy. For example, the calculation unit can refer to past score history and shot data and reflect them in the current calculation. The calculation unit can also calculate the optimal shot under specific conditions from past data. Furthermore, the calculation unit can refer to the past data of other players and reflect general trends in the calculation. In this way, calculation accuracy is improved by referring to past data. Some or all of the above processing in the calculation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the calculation unit can input past play data into a generating AI, which can then perform analysis to improve calculation accuracy.

[0073] The calculation unit can correct the calculation results by considering wind speed and wind direction during the calculation. For example, the calculation unit can acquire the current wind speed and wind direction and reflect them in the calculation results. For example, the calculation unit can acquire weather data in real time and correct the calculation results by considering wind speed and wind direction. Furthermore, the calculation unit can correct the calculation results in response to changes in wind speed and wind direction. In addition, the calculation unit can refer to past wind speed and wind direction data and compare calculation results under similar conditions. This improves the accuracy of the calculation results by considering wind speed and wind direction. Some or all of the above processing in the calculation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the calculation unit can input wind speed and wind direction data into a generating AI, and the generating AI can correct the calculation results.

[0074] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated user emotions. For example, if the user is nervous, the calculation unit can provide a simple and highly visible display method. For example, the calculation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the calculation unit can provide a display method that includes detailed information. Furthermore, if the user is anxious, the calculation unit can provide a display method that can be quickly understood. By adjusting the display method according to the user's emotions, the user can better understand the calculation results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using a generative AI, or not using a generative AI. For example, the calculation unit can input the user's facial expression data into a generative AI, and the generative AI will estimate the emotion, thereby adjusting the display method of the calculation results.

[0075] The calculation unit can customize the calculation results by taking into account the user's skill level during calculation. For example, if the user is a beginner, the calculation unit can provide basic calculation results. For example, the calculation unit can determine the skill level by referring to the user's past scores and play history. Furthermore, if the user is an intermediate player, the calculation unit can provide detailed calculation results. In addition, if the user is an advanced player, the calculation unit can provide complex calculation results. This allows the user to choose a more appropriate playing style by providing calculation results according to their skill level. Some or all of the above processing in the calculation unit may be performed using, for example, a generating AI, or without a generating AI. For example, the calculation unit can customize the calculation results by inputting the user's skill level into a generating AI, which then determines the skill level.

[0076] The calculation unit can compare calculation results by referencing data from other players during the calculation process. For example, the calculation unit can refer to data from other players and reflect general trends in the calculation results. For example, the calculation unit can refer to other players' score data and play history and compare calculation results. Furthermore, the calculation unit can provide optimal calculation results based on the success stories of other players. In addition, the calculation unit can assess risks based on the failure stories of other players. It is possible to provide calculation results that avoid errors. This improves the reliability of the calculation results by referencing data from other players. Some or all of the above processing in the calculation unit may be performed using a generating AI, for example, or without a generating AI. For example, the calculation unit can improve reliability by inputting data from other players into a generating AI, which then compares the calculation results.

[0077] The generation unit can estimate the user's emotions and adjust the heatmap's color scheme based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a heatmap with calm colors. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the generation unit can generate a heatmap with bright colors. Furthermore, if the user is anxious, the generation unit can generate a heatmap with highly visible colors. By adjusting the heatmap's color scheme according to the user's emotions, the user can better understand the heatmap. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI, and the generation AI can estimate the emotion to adjust the heatmap's color scheme.

[0078] The generation unit can improve generation accuracy by referring to past heatmap data during generation. For example, the generation unit can improve generation accuracy by referring to heatmap data collected in the past with the same green color. For example, the generation unit can retrieve heatmap data from a past database and reflect it in the current generation. The generation unit can also consider changes in the heatmap under specific seasons or weather conditions from past data. Furthermore, the generation unit can refer to past player data and reflect general trends in the generation. In this way, generation accuracy is improved by referring to past data. 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 past heatmap data into a generation AI, which can then perform analysis to improve generation accuracy.

[0079] The generation unit can correct the heatmap by taking weather information into consideration during generation. For example, the generation unit can acquire current weather information and reflect it in the heatmap. For example, the generation unit can acquire weather data in real time and correct the heatmap by taking weather information into consideration. The generation unit can also correct the heatmap in response to changes in weather. Furthermore, the generation unit can refer to past weather data and compare heatmaps under similar weather conditions. This improves the accuracy of the heatmap by considering weather information. 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 weather data into a generation AI, and the generation AI can correct the heatmap.

[0080] The generation unit can estimate the user's emotions and adjust the display method of the heatmap based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is anxious, the generation unit can provide a display method that can be quickly understood. By adjusting the display method according to the user's emotions, the user can better understand the heatmap. 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, or not using a generation AI. For example, the generation unit can input user facial expression data into a generation AI, and the generation AI can estimate the emotion to adjust the display method of the heatmap.

[0081] The generation unit can customize the heatmap during generation, taking into account the user's play style. For example, if the user has an aggressive play style, the generation unit can generate a risky heatmap. For example, the generation unit can identify the play style by referring to the user's past play data. Also, if the user has a defensive play style, the generation unit can generate a safe heatmap. Furthermore, the generation unit can generate an optimal heatmap by referring to the user's past play data. This allows the user to choose a more appropriate playing style by providing a heatmap tailored to their play style. 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 data into a generation AI, which can then identify the play style and customize the heatmap.

[0082] The generation unit can compare heatmaps by referencing data from other players during generation. For example, the generation unit can reference data from other players and reflect general trends in the heatmap. For example, the generation unit can reference other players' score data and play history and compare heatmaps. The generation unit can also generate an optimal heatmap based on the success stories of other players. Furthermore, the generation unit can generate a heatmap that avoids risks based on the failure stories of other players. This improves the reliability of the heatmap by referencing data from other players. 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 data from other players into a generation AI, and the generation AI can improve reliability by comparing heatmaps.

[0083] The explanatory unit can estimate the user's emotions and adjust the way the explanatory text is written based on the estimated emotions. For example, if the user is nervous, the explanatory unit can generate a concise and easy-to-understand explanatory text. For example, the explanatory unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the explanatory unit can generate a detailed explanatory text. Furthermore, if the user is anxious, the explanatory unit can generate an explanatory text that can be quickly understood. In this way, by adjusting the way the explanatory text is written according to the user's emotions, the user can understand the explanatory text more easily. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 explanatory unit may be performed using a generative AI, or not using a generative AI. For example, the explanatory unit can input the user's facial expression data into a generative AI, and the generative AI will estimate the emotion, thereby adjusting the way the explanatory text is written.

[0084] The description unit can improve the accuracy of description generation by referring to past description data. For example, the description unit can improve generation accuracy by referring to description data collected in the past for the same green. For example, the description unit can retrieve description data from past databases and reflect it in the current generation. The description unit can also consider how descriptions change under specific seasons or weather conditions from past data. Furthermore, the description unit can refer to past player data and reflect general trends in the description. In this way, the accuracy of description generation is improved by referring to past data. Some or all of the above processing in the description unit may be performed using a generation AI, for example, or without a generation AI. For example, the description unit can input past description data into a generation AI, which can then perform analysis to improve generation accuracy.

[0085] The description unit can correct the description text by taking weather information into consideration when generating the description text. For example, the description unit can acquire current weather information and reflect it in the description text. For example, the description unit can acquire weather data in real time and correct the description text by taking weather information into consideration. Furthermore, the description unit can correct the description text in response to changes in weather. In addition, the description unit can refer to past weather data and compare the description text under similar weather conditions. This improves the accuracy of the description text by taking weather information into consideration. Some or all of the above processing in the description unit may be performed using a generation AI, for example, or without a generation AI. For example, the description unit can input weather data into a generation AI, and the generation AI can correct the description text.

[0086] The description unit can estimate the user's emotions and adjust the length of the description based on the estimated emotions. For example, if the user is nervous, the description unit can generate a short, concise description. For example, the description unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Also, if the user is relaxed, the description unit can generate a detailed description. Furthermore, if the user is anxious, the description unit can generate a short, easily understandable description. By adjusting the length of the description according to the user's emotions, the description becomes easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the description unit may be performed using a generative AI, or not. For example, the description unit can input user facial expression data into a generative AI, and the generative AI can estimate emotions to adjust the length of the description.

[0087] The description section can customize the description text when generating it, taking into account the user's skill level. For example, if the user is a beginner, the description section will generate a basic description text. For example, the description section can determine the user's skill level by referring to the user's past scores and play history. Furthermore, if the user is an intermediate player, the description section can generate a detailed description text. In addition, if the user is an advanced player, the description section can generate a specialized description text. This allows the user to receive more appropriate advice by providing descriptions tailored to their skill level. Some or all of the above processing in the description section may be performed using, for example, a generation AI, or without a generation AI. For example, the description section can input the user's skill level into a generation AI, and the generation AI can determine the skill level to customize the description text.

[0088] The description unit can compare descriptions by referencing data from other players when generating them. For example, the description unit can refer to data from other players and reflect general trends in the descriptions. For example, the description unit can refer to other players' score data and play history and compare descriptions. The description unit can also generate optimal descriptions based on the success stories of other players. Furthermore, the description unit can generate descriptions that avoid risks based on the failure stories of other players. In this way, the reliability of the descriptions is improved by referencing data from other players. Some or all of the above processing in the description unit may be performed using a generation AI, for example, or without a generation AI. For example, the description unit can input data from other players into a generation AI, and the generation AI can improve reliability by comparing descriptions. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, calculation unit, generation unit, and explanation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the smart device 14 to detect green undulation information, cup position, and ball position, and these are analyzed by the control unit 46A. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and calculates the optimal shot based on the analyzed information. The generation unit generates a heat map based on the calculation results, for example, by the specific processing unit 290 of the data processing unit 12. The explanation unit generates an explanatory text based on the heat map, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, calculation unit, generation unit, and explanation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the smart glasses 214 to detect green undulation information, cup position, and ball position, and these are analyzed by the control unit 46A. The calculation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and calculates the optimal shot based on the analyzed information. The generation unit generates a heat map based on the calculation results, for example, by the identification processing unit 290 of the data processing unit 12. The explanation unit generates an explanatory text based on the heat map, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, calculation unit, generation unit, and explanation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the headset terminal 314 to detect green undulation information, cup position, and ball position, and the control unit 46A analyzes this information. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which calculates the optimal shot based on the analyzed information. The generation unit generates a heat map based on the calculation results, for example, by the specific processing unit 290 of the data processing unit 12. The explanation unit generates an explanatory text based on the heat map, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, calculation unit, generation unit, and explanation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the robot 414 to detect green undulation information, cup position, and ball position, and these are analyzed by the control unit 46A. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which calculates the optimal shot based on the analyzed information. The generation unit generates a heat map based on the calculation results, for example, by the specific processing unit 290 of the data processing unit 12. The explanation unit generates an explanatory text based on the heat map, for example, by the control unit 46A of the robot 414.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The analysis unit can customize the analysis results by referring to the user's past play data. For example, the analysis unit can retrieve from the database what kind of shots the user has taken in the past that were successful or unsuccessful, and reflect this in the current analysis. Furthermore, the analysis unit can adjust the analysis results considering the user's play style (aggressive, defensive, etc.). In addition, the analysis unit can adjust the accuracy and detail of the analysis according to the user's skill level (beginner, intermediate, advanced). This allows for the provision of more personalized analysis results by utilizing the user's past play data.

[0091] The calculation unit can estimate the user's emotions and customize the calculation results based on those emotions. For example, if the user is nervous, the calculation unit can suggest a more conservative approach. If the user is relaxed, it can suggest a more aggressive approach. Furthermore, if the user is anxious, it can adjust the calculation algorithm to provide results quickly. This allows the user to choose a more appropriate approach by providing calculation results that match their emotions.

[0092] The generation unit can customize the heatmap when generating it, taking into account the user's playing style. For example, if the user has an aggressive playing style, the generation unit can generate a heatmap that shows risky shots. Conversely, if the user has a defensive playing style, it can generate a heatmap that shows safe shots. Furthermore, it can refer to the user's past playing data to generate the optimal heatmap. By providing a heatmap tailored to the user's playing style, the unit enables the user to choose a more appropriate shot.

[0093] The explanatory section can estimate the user's emotions and adjust the way the explanatory text is written based on those emotions. For example, if the user is nervous, the explanatory section can generate a concise and easy-to-understand explanation. If the user is relaxed, it can generate a detailed explanation. Furthermore, if the user is anxious, it can generate an explanation that can be quickly understood. In this way, by adjusting the way the explanatory text is written according to the user's emotions, the explanation becomes easier for the user to understand.

[0094] The analysis unit can compare its analysis results with those of other players by referencing their data during the analysis process. For example, it can refer to other players' score data and play history to compare its analysis results. It can also provide optimal analysis results based on other players' success stories. Furthermore, it can provide analysis results that help avoid risks based on other players' failure stories. In this way, referencing other players' data improves the reliability of the analysis results.

[0095] The calculation unit can correct the calculation results by taking weather information into account during the calculation process. For example, the calculation unit can acquire current weather information and reflect it in the calculation results. It can also correct the calculation results in response to changes in weather. Furthermore, it can refer to past weather data and compare calculation results under similar weather conditions. In this way, the accuracy of the calculation results is improved by taking weather information into account.

[0096] The generation unit can estimate the user's emotions and adjust the heatmap's color scheme based on those emotions. For example, if the user is tense, the generation unit can generate a heatmap with calm colors. If the user is relaxed, it can generate a heatmap with bright colors. Furthermore, if the user is anxious, it can generate a heatmap with highly visible colors. By adjusting the heatmap's color scheme according to the user's emotions, the user can better understand the heatmap.

[0097] The description section can improve the accuracy of description generation by referring to past description data. For example, the description section can refer to description data collected for the same green in the past and reflect it in the current generation. It can also consider how descriptions will change under specific seasons or weather conditions based on past data. Furthermore, it can refer to past player data and reflect general trends in the descriptions. In this way, the accuracy of description generation is improved by referring to past data.

[0098] The analysis unit can customize the analysis results during the analysis process, taking into account the user's skill level. For example, if the user is a beginner, the analysis unit can provide basic analysis results. If the user is an intermediate player, it can provide detailed analysis results. Furthermore, if the user is an advanced player, it can provide complex analysis results. This allows the user to choose a more appropriate playing style by providing analysis results tailored to their skill level.

[0099] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated emotions. For example, if the user is nervous, the calculation unit can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is anxious, it can provide a display method that can be quickly understood. In this way, by adjusting the display method according to the user's emotions, the user can better understand the calculation results.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The analysis unit analyzes images that reveal the undulation information of the green, the location of the cup, and the location of the ball. The analysis unit uses image analysis technology to identify the slope of the green, the location of the cup, and the location of the ball. For example, it detects feature points in the image and analyzes their positional relationships to obtain information about the undulation information of the green. In addition, it uses an algorithm to detect circular features in the image to identify the location of the cup, and an algorithm to detect white circular features in the image to identify the location of the ball. Step 2: The calculation unit calculates how hard to hit the ball and where to hit it, based on the information analyzed by the analysis unit. The calculation unit calculates the optimal shot, taking into account the slope of the green and the distance to the cup. For example, it can take the slope angle of the green and the distance to the cup as input and calculate the optimal shot by performing a physical simulation. It can also correct the shot by taking into account environmental conditions such as wind speed and wind direction. Step 3: The generation unit generates a heatmap based on the results calculated by the calculation unit. The generation unit visually indicates to the user how to hit the ball by representing the calculation results with colors. For example, it generates a heatmap in which red areas indicate directions where you should hit hard and blue areas indicate directions where you should hit softly. The color scheme of the heatmap can also be adjusted according to the user's emotions. Step 4: The explanation unit generates explanatory text based on the heatmap generated by the generation unit. Based on the information in the heatmap, the explanation unit generates text that explains how hard to hit the ball and where to hit it. For example, it can generate specific advice such as, "It's best to hit it hard towards the right side of the cup." It can also adjust the way the explanatory text is expressed according to the user's emotions.

[0102] 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] 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.

[0121] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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.

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

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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.

[0154] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of Symbols]

[0174] 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. An analysis unit analyzes images that show information about the undulation of the green, the position of the cup, and the position of the ball. Based on the information analyzed by the aforementioned analysis unit, a calculation unit calculates the specific strength and direction of the strike, A generation unit generates a heat map based on the results calculated by the calculation unit, The system includes an explanation unit that generates an explanation based on the heatmap generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Identify the undulation of the green, the location of the cup, and the position of the ball. The system according to feature 1.

3. The calculation unit, The method of putting is calculated based on the slope of the green and the distance to the cup. The system according to feature 1.

4. The generating unit is Generate a heatmap based on the calculation results. The system according to feature 1.

5. The above explanatory section is, Generate specific advice based on heatmaps The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis parameters based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, During analysis, historical green undulation information is referenced to improve analysis accuracy. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the analysis results are corrected based on weather information. The system according to feature 1.

Images